references.bib

@article{agrenUseMultipleLIDARderived2021,
  title = {Use of Multiple {{LIDAR-derived}} Digital Terrain Indices and Machine Learning for High-Resolution National-Scale Soil Moisture Mapping of the {{Swedish}} Forest Landscape},
  author = {{\AA}gren, Anneli M. and Larson, Johannes and Paul, Siddhartho Shekhar and Laudon, Hjalmar and Lidberg, William},
  year = {2021},
  month = dec,
  journal = {Geoderma},
  volume = {404},
  pages = {115280},
  issn = {00167061},
  doi = {10.1016/j.geoderma.2021.115280},
  urldate = {2023-02-15},
  langid = {english}
}

@article{almeidaOptimizingRemoteDetection2019,
  title = {Optimizing the {{Remote Detection}} of {{Tropical Rainforest Structure}} with {{Airborne Lidar}}: {{Leaf Area Profile Sensitivity}} to {{Pulse Density}} and {{Spatial Sampling}}},
  shorttitle = {Optimizing the {{Remote Detection}} of {{Tropical Rainforest Structure}} with {{Airborne Lidar}}},
  author = {de Almeida, Danilo Roberti Alves and Stark, Scott C. and Shao, Gang and Schietti, Juliana and Nelson, Bruce Walker and Silva, Carlos Alberto and Gorgens, Eric Bastos and Valbuena, Ruben and Papa, Daniel de Almeida and Brancalion, Pedro Henrique Santin},
  year = {2019},
  month = jan,
  journal = {Remote Sensing},
  volume = {11},
  number = {1},
  pages = {92},
  issn = {2072-4292},
  doi = {10.3390/rs11010092},
  urldate = {2023-03-09},
  abstract = {Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high ({$>$}15 pulses m-2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems.},
  langid = {english}
}

@article{alvitesTerrestrialLaserScanning2021,
  title = {Terrestrial {{Laser Scanning}} for {{Quantifying Timber Assortments}} from {{Standing Trees}} in a {{Mixed}} and {{Multi-Layered Mediterranean Forest}}},
  author = {Alvites, Cesar and Santopuoli, Giovanni and Hollaus, Markus and Pfeifer, Norbert and Maesano, Mauro and Moresi, Federico Valerio and Marchetti, Marco and Lasserre, Bruno},
  year = {2021},
  month = oct,
  journal = {Remote Sensing},
  volume = {13},
  number = {21},
  pages = {4265},
  issn = {2072-4292},
  doi = {10.3390/rs13214265},
  urldate = {2023-02-15},
  abstract = {Timber assortments are some of the most important goods provided by forests worldwide. To quantify the amount and type of timber assortment is strongly important for socio-economic purposes, but also for accurate assessment of the carbon stored in the forest ecosystems, regardless of their main function. Terrestrial laser scanning (TLS) became a promising tool for timber assortment assessment compared to the traditional surveys, allowing reconstructing the tree architecture directly and rapidly. This study aims to introduce an approach for timber assortment assessment using TLS data in a mixed and multi-layered Mediterranean forest. It consists of five steps: (1) pre-processing, (2) timber-leaf discrimination, (3) stem detection, (4) stem reconstruction, and (5) timber assortment assessment. We assume that stem form drives the stem reconstruction, and therefore, it influences the timber assortment assessment. Results reveal that the timber-leaf discrimination accuracy is 0.98 through the Random Forests algorithm. The overall detection rate for all trees is 84.4\%, and all trees with a diameter at breast height larger than 0.30 m are correctly identified. Results highlight that the main factors hindering stem reconstruction are the presence of defects outside the trunk, trees poorly covered by points, and the stem form. We expect that the proposed approach is a starting point for valorising the timber resources from unmanaged/managed forests, e.g., abandoned forests. Further studies to calibrate its performance under different forest stand conditions are furtherly required.},
  langid = {english}
}

@article{appiahmensahMappingSiteIndex2023,
  title = {Mapping Site Index in Coniferous Forests Using Bi-Temporal Airborne Laser Scanning Data and Field Data from the {{Swedish}} National Forest Inventory},
  author = {Appiah Mensah, Alex and Jonz{\'e}n, Jonas and Nystr{\"o}m, Kenneth and Wallerman, J{\"o}rgen and Nilsson, Mats},
  year = {2023},
  month = nov,
  journal = {Forest Ecology and Management},
  volume = {547},
  pages = {121395},
  issn = {03781127},
  doi = {10.1016/j.foreco.2023.121395},
  urldate = {2023-09-19},
  langid = {english}
}

@article{arumaePlanningCommercialThinnings2022,
  title = {Planning of {{Commercial Thinnings Using Machine Learning}} and {{Airborne Lidar Data}}},
  author = {Arum{\"a}e, Tauri and Lang, Mait and Sims, Allan and Laarmann, Diana},
  year = {2022},
  month = jan,
  journal = {Forests},
  volume = {13},
  number = {2},
  pages = {206},
  issn = {1999-4907},
  doi = {10.3390/f13020206},
  urldate = {2023-03-09},
  abstract = {The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5\% for southwestern Estonia and 85.7\% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites---92.1\% for southwest and 81.8\% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions.},
  langid = {english}
}

@article{assmannEcoDesDK15HighresolutionEcological2022,
  title = {{{EcoDes-DK15}}: High-Resolution Ecological Descriptors of Vegetation and Terrain Derived from {{Denmark}}'s National Airborne Laser Scanning Data Set},
  shorttitle = {{{EcoDes-DK15}}},
  author = {Assmann, Jakob J. and Moeslund, Jesper E. and Treier, Urs A. and Normand, Signe},
  year = {2022},
  month = feb,
  journal = {Earth System Science Data},
  volume = {14},
  number = {2},
  pages = {823--844},
  issn = {1866-3516},
  doi = {10.5194/essd-14-823-2022},
  urldate = {2023-02-15},
  abstract = {Abstract. Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as light detection and ranging (lidar). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote sensing knowledge. We processed Denmark's publicly available national airborne laser scanning (ALS) data set acquired in 2014/15, together with the accompanying elevation model, to compute 70 rasterised descriptors of interest for ecological studies. With a grain size of 10\,m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than 40\,000\,km2 covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small ({$\sim$}94\,GB, compressed 16.8\,GB), and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark's national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterised data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. The full data set is available on Zenodo: https://doi.org/10.5281/zenodo.4756556 (Assmann et al., 2021); a 5\,MB teaser subset is also available: https://doi.org/10.5281/zenodo.6035188 (Assmann et al., 2022a).},
  langid = {english}
}

@article{atkinsIntegratingForestStructural2023,
  title = {Integrating Forest Structural Diversity Measurement into Ecological Research},
  author = {Atkins, Jeff W. and Bhatt, Parth and Carrasco, Luis and Francis, Emily and Garabedian, James E. and Hakkenberg, Christopher R. and Hardiman, Brady S. and Jung, Jinha and Koirala, Anil and LaRue, Elizabeth A. and Oh, Sungchan and Shao, Gang and Shao, Guofan and Shugart, H. H. and Spiers, Anna and Stovall, Atticus E. L. and Surasinghe, Thilina D. and Tai, Xiaonan and Zhai, Lu and Zhang, Tao and Krause, Keith},
  year = {2023},
  month = sep,
  journal = {Ecosphere},
  volume = {14},
  number = {9},
  pages = {e4633},
  issn = {2150-8925, 2150-8925},
  doi = {10.1002/ecs2.4633},
  urldate = {2023-11-21},
  abstract = {Abstract             The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity---an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.},
  langid = {english}
}

@article{axelssonUseDualwavelengthAirborne2023,
  title = {The Use of Dual-Wavelength Airborne Laser Scanning for Estimating Tree Species Composition and Species-Specific Stem Volumes in a Boreal Forest},
  author = {Axelsson, Christoffer R. and Lindberg, Eva and Persson, Henrik J. and Holmgren, Johan},
  year = {2023},
  month = apr,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {118},
  pages = {103251},
  issn = {15698432},
  doi = {10.1016/j.jag.2023.103251},
  urldate = {2023-10-30},
  langid = {english}
}

@article{beeseUsingRepeatAirborne2022,
  title = {Using Repeat Airborne {{LiDAR}} to Map the Growth of Individual Oil Palms in {{Malaysian Borneo}} during the 2015--16 {{El Ni{\~n}o}}},
  author = {Beese, Lucy and Dalponte, Michele and Asner, Gregory P. and Coomes, David A. and Jucker, Tommaso},
  year = {2022},
  month = dec,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {115},
  pages = {103117},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.103117},
  urldate = {2023-02-15},
  langid = {english}
}

@article{belandPromotingUseLidar2019,
  title = {On Promoting the Use of Lidar Systems in Forest Ecosystem Research},
  author = {Beland, Martin and Parker, Geoffrey and Sparrow, Ben and Harding, David and Chasmer, Laura and Phinn, Stuart and Antonarakis, Alexander and Strahler, Alan},
  year = {2019},
  month = oct,
  journal = {Forest Ecology and Management},
  volume = {450},
  pages = {117484},
  issn = {03781127},
  doi = {10.1016/j.foreco.2019.117484},
  urldate = {2023-03-22},
  langid = {english}
}

@article{bienzBilderkennungssoftwareFurFeinerschliessungen2022,
  title = {Bilderkennungssoftware F{\"u}r {{Feinerschliessungen}} Im {{Wald}}},
  author = {Bienz, Raffael and Freuler, Andreas},
  year = {2022},
  month = jul,
  journal = {Schweizerische Zeitschrift fur Forstwesen},
  volume = {173},
  number = {4},
  pages = {196--197},
  issn = {2235-1469, 0036-7818},
  doi = {10.3188/szf.2022.0196},
  urldate = {2023-03-02},
  abstract = {Zum Schutz des Waldbodens und als Hilfe bei Planungsaufgaben wird im Kanton Aargau die Feinerschliessung digitalisiert. Mithilfe der bisher erfassten Feinerschliessung wurde ein Bilderkennungsmodell trainiert, um auf der restlichen Kantonsfl{\"a}che die Feinerschliessung automatisch zu kartieren. Das Modell hat rund 90 Prozent der sichtbaren Fahrspuren zuverl{\"a}ssig erkannt. Die Daten dienen f{\"u}r die Erstellung von Arbeitsauftr{\"a}gen oder Holzschlagskizzen und werden Maschinisten im Bord-GPS zur Verf{\"u}gung gestellt.},
  langid = {english}
}

@article{blackfordDigitalSoilMapping2021,
  title = {Digital Soil Mapping Workflow for Forest Resource Applications: A Case Study in the {{Hearst Forest}}, {{Ontario}}},
  shorttitle = {Digital Soil Mapping Workflow for Forest Resource Applications},
  author = {Blackford, Christopher and Heung, Brandon and Baldwin, Ken and Fleming, Robert L. and Hazlett, Paul W. and Morris, Dave M. and Uhlig, Peter W.C. and Webster, Kara L.},
  year = {2021},
  month = jan,
  journal = {Canadian Journal of Forest Research},
  volume = {51},
  number = {1},
  pages = {59--77},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2020-0066},
  urldate = {2023-03-09},
  abstract = {Accurate soil information is critically important for forest management planning and operations but is challenging to map. Digital soil mapping (DSM) improves upon the limitations of conventional soil mapping by explicitly linking a variety of environmental data layers to spatial soil point data sets to continuously predict soil variability across a landscape. Thus far, much DSM research has focussed on the development of ultrafine-resolution soil maps within agricultural systems; however, increasing availability of light detection and ranging (LiDAR) data presents new opportunities to apply DSM to support forest resource applications at multiple scales. This project describes a DSM workflow using LiDAR-derived elevation data and machine learning models (MLMs) to predict key forest soil attributes. A case study in the Hearst Forest in northeastern Ontario, Canada, is used to illustrate the workflow. We applied multiple MLMs to the Hearst Forest to predict soil moisture regime and textural class. Both qualitative and quantitative assessment pointed to the random forest MLM producing the best maps (63\% accuracy for moisture regime and 66\% accuracy for textural class). Where error occurred, soils were typically misclassified to neighbouring classes. This standardized, flexible workflow is a valuable tool for practitioners that want to undertake DSM as part of forest resource management and planning.},
  langid = {english}
}

@article{bredePeeringThicketEffects2022,
  title = {Peering through the Thicket: {{Effects}} of {{UAV LiDAR}} Scanner Settings and Flight Planning on Canopy Volume Discovery},
  shorttitle = {Peering through the Thicket},
  author = {Brede, Benjamin and Bartholomeus, Harm M. and Barbier, Nicolas and Pimont, Fran{\c c}ois and Vincent, Gr{\'e}goire and Herold, Martin},
  year = {2022},
  month = nov,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {114},
  pages = {103056},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.103056},
  urldate = {2023-02-15},
  langid = {english}
}

@article{brodicRefinementIndividualTree2022,
  title = {Refinement of {{Individual Tree Detection Results Obtained}} from {{Airborne Laser Scanning Data}} for a {{Mixed Natural Forest}}},
  author = {Brodi{\'c}, Nenad and Cvijetinovi{\'c}, {\v Z}eljko and Milenkovi{\'c}, Milutin and Kova{\v c}evi{\'c}, Jovan and Stan{\v c}i{\'c}, Nikola and Mitrovi{\'c}, Momir and Mihajlovi{\'c}, Dragan},
  year = {2022},
  month = oct,
  journal = {Remote Sensing},
  volume = {14},
  number = {21},
  pages = {5345},
  issn = {2072-4292},
  doi = {10.3390/rs14215345},
  urldate = {2023-02-15},
  abstract = {Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km {\texttimes} 4 km. The classification model's training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient ({$\kappa$}) obtained from the ten-fold cross validation for the training data were 90.4\% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0\% and a {$\kappa$} = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.},
  langid = {english}
}

@article{brownEvaluationSPL100Single2020,
  title = {Evaluation of {{SPL100 Single Photon Lidar Data}}},
  author = {Brown, Rebecca and Hartzell, Preston and Glennie, Craig},
  year = {2020},
  month = feb,
  journal = {Remote Sensing},
  volume = {12},
  number = {4},
  pages = {722},
  issn = {2072-4292},
  doi = {10.3390/rs12040722},
  urldate = {2023-02-15},
  abstract = {Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling.},
  langid = {english}
}

@article{brunnerSegmentationConiferTree2022,
  title = {Segmentation of Conifer Tree Crowns from Terrestrial Laser Scanning Point Clouds in Mixed Stands of {{Scots}} Pine and {{Norway}} Spruce},
  author = {Brunner, Andreas and Houtmeyers, Silke},
  year = {2022},
  month = oct,
  journal = {European Journal of Forest Research},
  volume = {141},
  number = {5},
  pages = {909--925},
  issn = {1612-4669, 1612-4677},
  doi = {10.1007/s10342-022-01481-5},
  urldate = {2023-02-15},
  abstract = {Abstract             Terrestrial laser scanning of conifer tree crowns is challenged by occlusion problems causing sparse point clouds for many trees. Automatic segmentation of conifer tree crowns from sparse point clouds is a task that has only recently been addressed and not solved in a way that all trees can be segmented automatically without assignment errors. We developed a new segmentation algorithm that is based on region growing from seeds in voxelized 3D laser point clouds. In our data, field measured tree positions and diameters were available as input data to estimate crown cores as seeds for the region growing. In other applications, these seeds can be derived from the laser point cloud. Segmentation success was judged visually in the 3D voxel clouds for 1294 tree crowns of Norway spruce and Scots pine on 24 plots in six mixed species stands. Only about half of the tree crowns had only minor or no segmentation errors allowing to fit concentric crown models. Segmentation errors were most often caused by unsegmented neighbors at the edge of the sample plots. Wrong assignments of crown parts were also more frequent in dense groups of trees and for understory trees. For some trees, point clouds were too sparse to describe the crown. Segmentation success rates were considerably higher for dominant trees in the plot center. Despite the incomplete automatic segmentation of tree crowns, metrics describing crown size and crown shape could be derived for a large number of sample trees. A description of the irregular shape of tree crowns was not possible for most trees due to the sparse point clouds in the upper crown of most trees.},
  langid = {english}
}

@article{caldersLaserScanningReveals2022,
  title = {Laser Scanning Reveals Potential Underestimation of Biomass Carbon in Temperate Forest},
  author = {Calders, Kim and Verbeeck, Hans and Burt, Andrew and Origo, Niall and Nightingale, Joanne and Malhi, Yadvinder and Wilkes, Phil and Raumonen, Pasi and Bunce, Robert G. H. and Disney, Mathias},
  year = {2022},
  month = oct,
  journal = {Ecological Solutions and Evidence},
  volume = {3},
  number = {4},
  issn = {2688-8319, 2688-8319},
  doi = {10.1002/2688-8319.12197},
  urldate = {2023-02-28},
  langid = {english}
}

@article{caldersStrucNetGlobalNetwork2023,
  title = {{{StrucNet}}: A Global Network for Automated Vegetation Structure Monitoring},
  shorttitle = {{{StrucNet}}},
  author = {Calders, Kim and Brede, Benjamin and Newnham, Glenn and Culvenor, Darius and Armston, John and Bartholomeus, Harm and Griebel, Anne and Hayward, Jodie and Junttila, Samuli and Lau, Alvaro and Levick, Shaun and Morrone, Rosalinda and Origo, Niall and Pfeifer, Marion and Verbesselt, Jan and Herold, Martin},
  editor = {Sankey, Temuulen and Murray, Nicholas},
  year = {2023},
  month = apr,
  journal = {Remote Sensing in Ecology and Conservation},
  pages = {rse2.333},
  issn = {2056-3485, 2056-3485},
  doi = {10.1002/rse2.333},
  urldate = {2023-06-02},
  langid = {english}
}

@article{camarrettaMonitoringForestStructure2020,
  title = {Monitoring Forest Structure to Guide Adaptive Management of Forest Restoration: A Review of Remote Sensing Approaches},
  shorttitle = {Monitoring Forest Structure to Guide Adaptive Management of Forest Restoration},
  author = {Camarretta, Nicol{\`o} and Harrison, Peter A. and Bailey, Tanya and Potts, Brad and Lucieer, Arko and Davidson, Neil and Hunt, Mark},
  year = {2020},
  month = jul,
  journal = {New Forests},
  volume = {51},
  number = {4},
  pages = {573--596},
  issn = {0169-4286, 1573-5095},
  doi = {10.1007/s11056-019-09754-5},
  urldate = {2023-06-02},
  langid = {english}
}

@article{caoBenchmarkingAirborneLaser2023,
  title = {Benchmarking Airborne Laser Scanning Tree Segmentation Algorithms in Broadleaf Forests Shows High Accuracy Only for Canopy Trees},
  author = {Cao, Yujie and Ball, James G.C. and Coomes, David A. and Steinmeier, Leon and Knapp, Nikolai and Wilkes, Phil and Disney, Mathias and Calders, Kim and Burt, Andrew and Lin, Yi and Jackson, Toby D.},
  year = {2023},
  month = sep,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {123},
  pages = {103490},
  issn = {15698432},
  doi = {10.1016/j.jag.2023.103490},
  urldate = {2023-10-20},
  langid = {english}
}

@techreport{caoTreeSegmentationAirborne2022,
  type = {Preprint},
  title = {Tree Segmentation in Airborne Laser Scanning Data Is Only Accurate for Canopy Trees},
  author = {Cao, Yujie and Ball, James G. C. and Coomes, David A. and Steinmeier, Leon and Knapp, Nikolai and Wilkes, Phil and Disney, Mathias and Calders, Kim and Burt, Andrew and Lin, Yi and Jackson, Tobias D.},
  year = {2022},
  month = dec,
  institution = {Ecology},
  doi = {10.1101/2022.11.29.518407},
  urldate = {2023-03-23},
  abstract = {Abstract           Individual tree segmentation from airborne laser scanning data is a longstanding and important challenge in forest remote sensing. There are a number of segmentation algorithms but robust intercomparison studies are rare due to the difficulty of obtaining reliable reference data. Here we provide a benchmark data set for temperate and tropical broadleaf forests generated from labelled terrestrial laser scanning data. We compare the performance of four widely used tree segmentation algorithms against this benchmark data set. All algorithms achieved reasonable accuracy for the canopy trees, but very low accuracy for the understory trees. The point cloud based algorithm AMS3D (Adaptive Mean Shift 3D) had the highest overall accuracy, closely followed by the 2D raster based region growing algorithm Dalponte2016+. This result was consistent across both forest types. This study emphasises the need to assess tree segmentation algorithms directly using benchmark data. We provide the first openly available benchmark data set for tropical forests and we hope future studies will extend this work to other regions.},
  langid = {english}
}

@misc{choudhryPrecisionForestryRevolution2018,
  title = {Precision Forestry: {{A}} Revolution in the {{woodsThe}} Precision Forestry Revolution {\textbar} {{McKinsey}}},
  author = {Choudhry, Harsh and O'Kelly, Glen},
  year = {2018},
  month = jun,
  journal = {https://www.mckinsey.com/industries/paper-forest-products-and-packaging/our-insights/precision-forestry-a-revolution-in-the-woods},
  urldate = {2023-03-03},
  abstract = {Advanced technologies could improve forest management significantly. What areas are most promising, and how can forestry companies start their digital transformation?},
  howpublished = {https://www.mckinsey.com/industries/paper-forest-products-and-packaging/our-insights/precision-forestry-a-revolution-in-the-woods}
}

@article{coopsFrameworkRealtimeForest2023,
  title = {Framework for near Real-Time Forest Inventory Using Multi Source Remote Sensing Data},
  author = {Coops, Nicholas C and Tompalski, Piotr and Goodbody, Tristan R H and Achim, Alexis and Mulverhill, Christopher},
  editor = {Fassnacht, Fabian},
  year = {2023},
  month = jan,
  journal = {Forestry: An International Journal of Forest Research},
  volume = {96},
  number = {1},
  pages = {1--19},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpac015},
  urldate = {2023-02-15},
  abstract = {Abstract             Forestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a `living inventory'. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non--stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data.},
  langid = {english}
}

@article{coopsModellingLidarderivedEstimates2021,
  title = {Modelling Lidar-Derived Estimates of Forest Attributes over Space and Time: {{A}} Review of Approaches and Future Trends},
  shorttitle = {Modelling Lidar-Derived Estimates of Forest Attributes over Space and Time},
  author = {Coops, Nicholas C. and Tompalski, Piotr and Goodbody, Tristan R.H. and Queinnec, Martin and Luther, Joan E. and Bolton, Douglas K. and White, Joanne C. and Wulder, Michael A. and {van Lier}, Oliver R. and Hermosilla, Txomin},
  year = {2021},
  month = jul,
  journal = {Remote Sensing of Environment},
  volume = {260},
  pages = {112477},
  issn = {00344257},
  doi = {10.1016/j.rse.2021.112477},
  urldate = {2023-03-22},
  langid = {english}
}

@article{cushmanSmallFieldPlots2023,
  title = {Small {{Field Plots Can Cause Substantial Uncertainty}} in {{Gridded Aboveground Biomass Products}} from {{Airborne Lidar Data}}},
  author = {Cushman, K. C. and Saatchi, Sassan and McRoberts, Ronald E. and {Anderson-Teixeira}, Kristina J. and Bourg, Norman A. and Chapman, Bruce and McMahon, Sean M. and Mulverhill, Christopher},
  year = {2023},
  month = jul,
  journal = {Remote Sensing},
  volume = {15},
  number = {14},
  pages = {3509},
  issn = {2072-4292},
  doi = {10.3390/rs15143509},
  urldate = {2023-10-20},
  abstract = {Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8--33.3 Mg ha-1 for site-specific models (one standard deviation), 11.1--28.2 Mg ha-1 for ecoregion-specific models, and 21.1--22.1 Mg ha-1 for the general model for pixels in the AGB range of 80--100 Mg ha-1. Only 3 of 11 site-specific models had a total uncertainty of {$<$}15 Mg ha-1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha-1 using 0.04 ha plots to 10.9 Mg ha-1 using 0.25 ha plots and 10.1 Mg ha-1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales ({$\geq$}0.25 ha).},
  langid = {english}
}

@article{dalagnolLargescaleVariationsDynamics2021,
  title = {Large-Scale Variations in the Dynamics of {{Amazon}} Forest Canopy Gaps from Airborne Lidar Data and Opportunities for Tree Mortality Estimates},
  author = {Dalagnol, Ricardo and Wagner, Fabien H. and Galv{\~a}o, L{\^e}nio S. and Streher, Annia S. and Phillips, Oliver L. and Gloor, Emanuel and Pugh, Thomas A. M. and Ometto, Jean P. H. B. and Arag{\~a}o, Luiz E. O. C.},
  year = {2021},
  month = jan,
  journal = {Scientific Reports},
  volume = {11},
  number = {1},
  pages = {1388},
  issn = {2045-2322},
  doi = {10.1038/s41598-020-80809-w},
  urldate = {2023-02-15},
  abstract = {Abstract             We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20--35\% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60\% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.},
  langid = {english}
}

@article{davisonEffectLeafonLeafoff2020,
  title = {The Effect of Leaf-on and Leaf-off Forest Canopy Conditions on {{LiDAR}} Derived Estimations of Forest Structural Diversity},
  author = {Davison, Sophie and Donoghue, Daniel N.M. and Galiatsatos, Nikolaos},
  year = {2020},
  month = oct,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {92},
  pages = {102160},
  issn = {15698432},
  doi = {10.1016/j.jag.2020.102160},
  urldate = {2023-03-14},
  langid = {english}
}

@article{derschCompleteTreeCrown2023,
  title = {Towards Complete Tree Crown Delineation by Instance Segmentation with {{Mask R}}--{{CNN}} and {{DETR}} Using {{UAV-based}} Multispectral Imagery and Lidar Data},
  author = {Dersch, S. and Sch{\"o}ttl, A. and Krzystek, P. and Heurich, M.},
  year = {2023},
  month = apr,
  journal = {ISPRS Open Journal of Photogrammetry and Remote Sensing},
  volume = {8},
  pages = {100037},
  issn = {26673932},
  doi = {10.1016/j.ophoto.2023.100037},
  urldate = {2023-05-23},
  langid = {english}
}

@article{dietmaierComparisonLiDARDigital2019,
  title = {Comparison of {{LiDAR}} and {{Digital Aerial Photogrammetry}} for {{Characterizing Canopy Openings}} in the {{Boreal Forest}} of {{Northern Alberta}}},
  author = {Dietmaier, Annette and McDermid, Gregory J. and Rahman, Mir Mustafizur and Linke, Julia and Ludwig, Ralf},
  year = {2019},
  month = aug,
  journal = {Remote Sensing},
  volume = {11},
  number = {16},
  pages = {1919},
  issn = {2072-4292},
  doi = {10.3390/rs11161919},
  urldate = {2023-03-20},
  abstract = {Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87\% overall accuracy), followed by the hybrid and DAP models (47\% and 46\%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9--53\%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP's sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology's capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.},
  langid = {english}
}

@article{diltsImprovedTopographicRuggedness2023,
  title = {Improved Topographic Ruggedness Indices More Accurately Model Fine-Scale Ecological Patterns},
  author = {Dilts, Thomas E. and Blum, Marcus E. and Shoemaker, Kevin T. and Weisberg, Peter J. and Stewart, Kelley M.},
  year = {2023},
  month = apr,
  journal = {Landscape Ecology},
  issn = {0921-2973, 1572-9761},
  doi = {10.1007/s10980-023-01646-6},
  urldate = {2023-04-20},
  langid = {english}
}

@misc{europeancommission.jointresearchcentre.NoncommercialLightDetection2021,
  title = {Non-Commercial {{Light Detection}} and {{Ranging}} ({{LiDAR}}) Data in {{Europe}}.},
  author = {{European Commission. Joint Research Centre.}},
  year = {2021},
  urldate = {2023-02-27},
  langid = {english}
}

@article{eysnBenchmarkLidarBasedSingle2015,
  title = {A {{Benchmark}} of {{Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data}} from the {{Alpine Space}}},
  author = {Eysn, Lothar and Hollaus, Markus and Lindberg, Eva and Berger, Fr{\'e}d{\'e}ric and Monnet, Jean-Matthieu and Dalponte, Michele and Kobal, Milan and Pellegrini, Marco and Lingua, Emanuele and Mongus, Domen and Pfeifer, Norbert},
  year = {2015},
  month = may,
  journal = {Forests},
  volume = {6},
  number = {12},
  pages = {1721--1747},
  issn = {1999-4907},
  doi = {10.3390/f6051721},
  urldate = {2023-03-14},
  langid = {english}
}

@article{ferrazLidarDetectionIndividual2016,
  title = {Lidar Detection of Individual Tree Size in Tropical Forests},
  author = {Ferraz, Ant{\'o}nio and Saatchi, Sassan and Mallet, Cl{\'e}ment and Meyer, Victoria},
  year = {2016},
  month = sep,
  journal = {Remote Sensing of Environment},
  volume = {183},
  pages = {318--333},
  issn = {00344257},
  doi = {10.1016/j.rse.2016.05.028},
  urldate = {2023-03-23},
  langid = {english}
}

@article{fuhrDetectingOvermatureForests2022,
  title = {Detecting Overmature Forests with Airborne Laser Scanning ( {{{\textsc{ALS}}}} )},
  shorttitle = {Detecting Overmature Forests with Airborne Laser Scanning (},
  author = {Fuhr, Marc and Lalech{\`e}re, Etienne and Monnet, Jean-Matthieu and Berg{\`e}s, Laurent},
  editor = {Disney, Mat and Hernandez-Clemente, Roc{\'i}o},
  year = {2022},
  month = oct,
  journal = {Remote Sensing in Ecology and Conservation},
  volume = {8},
  number = {5},
  pages = {731--743},
  issn = {2056-3485, 2056-3485},
  doi = {10.1002/rse2.274},
  urldate = {2023-02-15},
  langid = {english}
}

@article{ganzMeasuringTreeHeight2019,
  title = {Measuring {{Tree Height}} with {{Remote Sensing}}---{{A Comparison}} of {{Photogrammetric}} and {{LiDAR Data}} with {{Different Field Measurements}}},
  author = {Ganz, Selina and K{\"a}ber, Yannek and Adler, Petra},
  year = {2019},
  month = aug,
  journal = {Forests},
  volume = {10},
  number = {8},
  pages = {694},
  issn = {1999-4907},
  doi = {10.3390/f10080694},
  urldate = {2023-03-09},
  abstract = {We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of -0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.},
  langid = {english}
}

@article{garabedianQuantitativeAnalysisWoodpecker2014,
  title = {Quantitative Analysis of Woodpecker Habitat Using High-Resolution Airborne {{LiDAR}} Estimates of Forest Structure and Composition},
  author = {Garabedian, James E. and McGaughey, Robert J. and Reutebuch, Stephen E. and Parresol, Bernard R. and Kilgo, John C. and Moorman, Christopher E. and Peterson, M. Nils},
  year = {2014},
  month = apr,
  journal = {Remote Sensing of Environment},
  volume = {145},
  pages = {68--80},
  issn = {00344257},
  doi = {10.1016/j.rse.2014.01.022},
  urldate = {2023-04-11},
  langid = {english}
}

@article{gavilan-acunaEstimatingPotentialTree2022,
  title = {Estimating Potential Tree Height in {{{\emph{Pinus}}}}{\emph{ Radiata}} Plantations Using Airborne Laser Scanning Data},
  author = {{Gavil{\'a}n-Acu{\~n}a}, Gonzalo and Coops, Nicholas C. and Tompalski, Piotr and {Mena-Quijada}, Pablo},
  year = {2022},
  month = oct,
  journal = {Canadian Journal of Forest Research},
  volume = {52},
  number = {10},
  pages = {1353--1366},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2022-0121},
  urldate = {2023-03-01},
  abstract = {Representing the spatial distribution of trees and competition interactions in growth models improves growth prediction and provides insights into spatially explicit forecasts for precise silvicultural interventions. However, this information is rarely taken into account over large areas because obtaining the spatial distribution of individual trees and estimating their competition is both expensive and time consuming. Airborne laser scanning enables rapid estimation of tree height and other attributes over large areas. In this study, we implemented an individual tree detection approach to first extract tree attributes of Pinus radiata D.~Don plantations, and second to use this spatially explicit information on tree location and competition to forecast potential tree height, defined as a maximum projected tree height at rotation age. To do so, using a chronosequence of tree heights, we developed a tree height growth model using a Chapman--Richards function, utilizing the effect of inter-tree competition and stand-level top height (TH) on the tree height growth. The results showed that using chronosequence of heights, competition, and TH resulted in accurate predictions of potential tree height (root mean square error~=~2.9~m; mean absolute percentage error~=~0.154\%). We concluded that individual tree height growth is significantly influenced by competition, with increased competition values associated with reductions in potential height growth by 22.2\% at 30 years.},
  langid = {english}
}

@article{glasmannMappingSubcanopyLight2023,
  title = {Mapping Subcanopy Light Regimes in Temperate Mountain Forests from {{Airborne Laser Scanning}}, {{Sentinel-1}} and {{Sentinel-2}}},
  author = {Glasmann, Felix and Senf, Cornelius and Seidl, Rupert and Annigh{\"o}fer, Peter},
  year = {2023},
  month = dec,
  journal = {Science of Remote Sensing},
  volume = {8},
  pages = {100107},
  issn = {26660172},
  doi = {10.1016/j.srs.2023.100107},
  urldate = {2023-11-21},
  langid = {english}
}

@article{goodbodyAirborneLaserScanning2021,
  title = {Airborne Laser Scanning for Quantifying Criteria and Indicators of Sustainable Forest Management in {{Canada}}},
  author = {Goodbody, Tristan R.H. and Coops, Nicholas C. and Luther, Joan E. and Tompalski, Piotr and Mulverhill, Christopher and Frizzle, Catherine and Fournier, Richard and Furze, Shane and Herniman, Sam},
  year = {2021},
  month = jul,
  journal = {Canadian Journal of Forest Research},
  volume = {51},
  number = {7},
  pages = {972--985},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2020-0424},
  urldate = {2023-03-06},
  abstract = {Airborne laser scanning (ALS) has emerged as a technology capable of generating descriptors of vegetation structure and best available terrain information. Research and operational implementations of ALS data have highlighted their value for characterizing forest structure and generating spatially explicit and objective spatial coverages and mapping products for forest management. Continued emphasis to enhance forest stewardship is promoting novel methods to integrate ALS to detail non-timber ecosystem values like habitat, soil, and water. Standardized criteria and indicator frameworks such as the Canadian Council of Forest Ministers provide a reliable starting point for where ALS has opportunities to characterize ecosystems objectively regardless of location. In this review of primarily Canadian work, we highlight how ALS is becoming an increasingly viable technology for deriving meaningful indicators to meet sustainable forest management criteria. We review and highlight the value of ALS for quantifying indicators of biological diversity, ecosystem condition and productivity, soil and water, and the role of forests in global ecological cycles. We conclude by highlighting the need for increased education, tech transfer, flexible software, and reporting frameworks alongside five key considerations for using ALS to derive meaningful indicators of sustainable forest management.},
  langid = {english}
}

@article{goodbodyAirborneLaserScanning2023,
  title = {Airborne Laser Scanning to Optimize the Sampling Efficiency of a Forest Management Inventory in {{South-Eastern Germany}}},
  author = {Goodbody, Tristan R.H. and Coops, Nicholas C. and Senf, Cornelius and Seidl, Rupert},
  year = {2023},
  month = dec,
  journal = {Ecological Indicators},
  volume = {157},
  pages = {111281},
  issn = {1470160X},
  doi = {10.1016/j.ecolind.2023.111281},
  urldate = {2023-11-21},
  langid = {english}
}

@article{goodbodyDigitalAerialPhotogrammetry2019,
  title = {Digital {{Aerial Photogrammetry}} for {{Updating Area-Based Forest Inventories}}: {{A Review}} of {{Opportunities}}, {{Challenges}}, and {{Future Directions}}},
  shorttitle = {Digital {{Aerial Photogrammetry}} for {{Updating Area-Based Forest Inventories}}},
  author = {Goodbody, Tristan R. H. and Coops, Nicholas C. and White, Joanne C.},
  year = {2019},
  month = jun,
  journal = {Current Forestry Reports},
  volume = {5},
  number = {2},
  pages = {55--75},
  issn = {2198-6436},
  doi = {10.1007/s40725-019-00087-2},
  urldate = {2023-02-15},
  langid = {english}
}

@article{goodbodySgsRStructurallyGuided2023,
  title = {{{{\emph{sgsR}}}} : A Structurally Guided Sampling Toolbox for {{LiDAR-based}} Forest Inventories},
  shorttitle = {{{{\emph{sgsR}}}}},
  author = {Goodbody, Tristan R H and Coops, Nicholas C and Queinnec, Martin and White, Joanne C and Tompalski, Piotr and Hudak, Andrew T and Auty, David and Valbuena, Ruben and LeBoeuf, Antoine and Sinclair, Ian and McCartney, Grant and Prieur, Jean-Francois and Woods, Murray E},
  editor = {Fassnacht, Fabian},
  year = {2023},
  month = feb,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpac055},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpac055},
  urldate = {2023-02-15},
  abstract = {Abstract             Establishing field inventories can be labor intensive, logistically challenging and expensive. Optimizing a sample to derive accurate forest attribute predictions is a key management-level inventory objective. Traditional sampling designs involving pre-defined, interpreted strata could result in poor selection of within-strata sampling intensities, leading to inaccurate estimates of forest structural variables. The use of airborne laser scanning (ALS) data as an applied forest inventory tool continues to improve understanding of the composition and spatial distribution of vegetation structure across forested landscapes. The increased availability of wall-to-wall ALS data is promoting the concept of structurally guided sampling (SGS), where ALS metrics are used as an auxiliary data source driving stratification and sampling within management-level forest inventories. In this manuscript, we present an open-source R package named sgsR that provides a robust toolbox for implementing various SGS approaches. The goal of this package is to provide a toolkit to facilitate better optimized allocation of sample units and sample size, as well as to assess and augment existing plot networks by accounting for current forest structural conditions. Here, we first provide justification for SGS approaches and the creation of the sgsR toolbox. We then briefly describe key functions and workflows the package offers and provide two reproducible examples. Avenues to implement SGS protocols according to auxiliary data needs are presented.},
  langid = {english}
}

@article{gorgensAutomatedOperationalLogging2020,
  title = {Automated Operational Logging Plan Considering Multi-Criteria Optimization},
  author = {G{\"o}rgens, Eric Bastos and Mund, Jan-Peter and Cremer, Tobias and {de Conto}, Tiago and Krause, Stuart and Valbuena, Ruben and Rodriguez, Luiz Carlos Estraviz},
  year = {2020},
  month = mar,
  journal = {Computers and Electronics in Agriculture},
  volume = {170},
  pages = {105253},
  issn = {01681699},
  doi = {10.1016/j.compag.2020.105253},
  urldate = {2023-03-03},
  langid = {english}
}

@article{grilUsingAirborneLiDAR2023,
  title = {Using Airborne {{LiDAR}} to Map Forest Microclimate Temperature Buffering or Amplification},
  author = {Gril, Eva and Laslier, Marianne and {Gallet-Moron}, Emilie and Durrieu, Sylvie and Spicher, Fabien and Le Roux, Vincent and Brasseur, Boris and Haesen, Stef and Van Meerbeek, Koenraad and Decocq, Guillaume and Marrec, Ronan and Lenoir, Jonathan},
  year = {2023},
  month = dec,
  journal = {Remote Sensing of Environment},
  volume = {298},
  pages = {113820},
  issn = {00344257},
  doi = {10.1016/j.rse.2023.113820},
  urldate = {2023-10-20},
  langid = {english}
}

@incollection{guoLiDARRemoteSensing2022,
  title = {{{LiDAR Remote Sensing}} of {{Forest Ecosystems}}: {{Applications}} and {{Prospects}}},
  shorttitle = {{{LiDAR Remote Sensing}} of {{Forest Ecosystems}}},
  booktitle = {New {{Thinking}} in {{GIScience}}},
  author = {Guo, Qinghua and Liang, Xinlian and Li, Wenkai and Jin, Shichao and Guan, Hongcan and Cheng, Kai and Su, Yanjun and Tao, Shengli},
  editor = {Li, Bin and Shi, Xun and Zhu, A-Xing and Wang, Cuizhen and Lin, Hui},
  year = {2022},
  pages = {221--231},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  doi = {10.1007/978-981-19-3816-0_24},
  urldate = {2023-04-14},
  isbn = {978-981-19381-5-3 978-981-19381-6-0},
  langid = {english}
}

@article{haesenForestTempSubCanopy2021,
  title = {{{ForestTemp}} -- {{Sub}}-canopy Microclimate Temperatures of {{European}} Forests},
  author = {Haesen, Stef and Lembrechts, Jonas J. and De Frenne, Pieter and Lenoir, Jonathan and Aalto, Juha and Ashcroft, Michael B. and Kopeck{\'y}, Martin and Luoto, Miska and Maclean, Ilya and Nijs, Ivan and Niittynen, Pekka and Hoogen, Johan and Arriga, Nicola and Br{\r u}na, Josef and Buchmann, Nina and {\v C}iliak, Marek and Collalti, Alessio and De Lombaerde, Emiel and Descombes, Patrice and Gharun, Mana and Goded, Ignacio and Govaert, Sanne and Greiser, Caroline and Grelle, Achim and Gruening, Carsten and Hederov{\'a}, Lucia and Hylander, Kristoffer and Kreyling, J{\"u}rgen and Kruijt, Bart and Macek, Martin and M{\'a}li{\v s}, Franti{\v s}ek and Man, Mat{\v e}j and Manca, Giovanni and Matula, Radim and Meeussen, Camille and Merinero, Sonia and Minerbi, Stefano and Montagnani, Leonardo and Muffler, Lena and Ogaya, Rom{\`a} and Penuelas, Josep and Plichta, Roman and Portillo-Estrada, Miguel and Schmeddes, Jonas and Shekhar, Ankit and Spicher, Fabien and Ujh{\'a}zyov{\'a}, Mariana and Vangansbeke, Pieter and Weigel, Robert and Wild, Jan and Zellweger, Florian and Van Meerbeek, Koenraad},
  year = {2021},
  month = dec,
  journal = {Global Change Biology},
  volume = {27},
  number = {23},
  pages = {6307--6319},
  issn = {1354-1013, 1365-2486},
  doi = {10.1111/gcb.15892},
  urldate = {2023-04-20},
  langid = {english}
}

@article{hauglinLargeScaleMapping2021,
  title = {Large Scale Mapping of Forest Attributes Using Heterogeneous Sets of Airborne Laser Scanning and {{National Forest Inventory}} Data},
  author = {Hauglin, Marius and Rahlf, Johannes and Schumacher, Johannes and Astrup, Rasmus and Breidenbach, Johannes},
  year = {2021},
  month = dec,
  journal = {Forest Ecosystems},
  volume = {8},
  number = {1},
  pages = {65},
  issn = {2197-5620},
  doi = {10.1186/s40663-021-00338-4},
  urldate = {2023-02-24},
  abstract = {Abstract                            Background               The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10\,years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level.                                         Materials and methods                                The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in                 species-specific models                 that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in                 stratum-specific models                 that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to                 general models                 that were fit without stratifying the data.                                                        Results               The species-specific models had relative root-mean-squared errors (RMSEs) of 35\%, 34\%, 31\%, and 12\% for volume, aboveground biomass, basal area, and Lorey's height, respectively. These RMSEs were 2--7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0--4\,pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8\,pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0--3.9\,pp., depending on the main tree species. RMSEs for timber volume ranged between 19\%--59\% among individual ALS projects.                                         Conclusions               The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.},
  langid = {english}
}

@article{hawryloHowAdequatelyDetermine2024,
  title = {How to Adequately Determine the Top Height of Forest Stands Based on Airborne Laser Scanning Point Clouds?},
  author = {Hawry{\l}o, Pawe{\l} and Socha, Jaros{\l}aw and W{\k e}{\.z}yk, Piotr and Ocha{\l}, Wojciech and Krawczyk, Wojciech and Miszczyszyn, Jakub and {Tymi{\'n}ska-Czaba{\'n}ska}, Luiza},
  year = {2024},
  month = jan,
  journal = {Forest Ecology and Management},
  volume = {551},
  pages = {121528},
  issn = {03781127},
  doi = {10.1016/j.foreco.2023.121528},
  urldate = {2024-01-04},
  langid = {english}
}

@article{heinaroAirborneLaserScanning2021,
  title = {Airborne Laser Scanning Reveals Large Tree Trunks on Forest Floor},
  author = {Heinaro, Einari and Tanhuanp{\"a}{\"a}, Topi and Yrttimaa, Tuomas and Holopainen, Markus and Vastaranta, Mikko},
  year = {2021},
  month = jul,
  journal = {Forest Ecology and Management},
  volume = {491},
  pages = {119225},
  issn = {03781127},
  doi = {10.1016/j.foreco.2021.119225},
  urldate = {2023-02-15},
  langid = {english}
}

@article{heinaroEvaluatingFactorsImpacting2023,
  title = {Evaluating {{Factors Impacting Fallen Tree Detection}} from {{Airborne Laser Scanning Point Clouds}}},
  author = {Heinaro, Einari and Tanhuanp{\"a}{\"a}, Topi and Vastaranta, Mikko and Yrttimaa, Tuomas and Kukko, Antero and Hakala, Teemu and Mattsson, Teppo and Holopainen, Markus},
  year = {2023},
  month = jan,
  journal = {Remote Sensing},
  volume = {15},
  number = {2},
  pages = {382},
  issn = {2072-4292},
  doi = {10.3390/rs15020382},
  urldate = {2023-02-15},
  abstract = {Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.},
  langid = {english}
}

@article{hoffmannTrafficabilityPredictionUsing2022,
  title = {Trafficability {{Prediction Using Depth-to-Water Maps}}: The {{Status}} of {{Application}} in {{Northern}} and {{Central European Forestry}}},
  shorttitle = {Trafficability {{Prediction Using Depth-to-Water Maps}}},
  author = {Hoffmann, Stephan and Sch{\"o}nauer, Marian and Heppelmann, Joachim and Asikainen, Antti and Cacot, Emmanuel and Eberhard, Benno and Hasenauer, Hubert and Ivanovs, Janis and Jaeger, Dirk and Lazdins, Andis and Mohtashami, Sima and Moskalik, Tadeusz and Nordfjell, Tomas and Stere{\'n}czak, Krzysztof and Talbot, Bruce and Uusitalo, Jori and Vuillermoz, Morgan and Astrup, Rasmus},
  year = {2022},
  month = mar,
  journal = {Current Forestry Reports},
  volume = {8},
  number = {1},
  pages = {55--71},
  issn = {2198-6436},
  doi = {10.1007/s40725-021-00153-8},
  urldate = {2023-02-15},
  abstract = {Abstract                            Purpose of Review               Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions.                                         Recent Findings               A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information.                                         Summary               The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.},
  langid = {english}
}

@article{holmgrenTreeCrownSegmentation2022,
  title = {Tree Crown Segmentation in Three Dimensions Using Density Models Derived from Airborne Laser Scanning},
  author = {Holmgren, Johan and Lindberg, Eva and Olofsson, Kenneth and Persson, Henrik J.},
  year = {2022},
  month = jan,
  journal = {International Journal of Remote Sensing},
  volume = {43},
  number = {1},
  pages = {299--329},
  issn = {0143-1161, 1366-5901},
  doi = {10.1080/01431161.2021.2018149},
  urldate = {2023-02-24},
  langid = {english}
}

@article{holopainenOutlookNextGeneration2014,
  title = {Outlook for the {{Next Generation}}'s {{Precision Forestry}} in {{Finland}}},
  author = {Holopainen, Markus and Vastaranta, Mikko and Hyypp{\"a}, Juha},
  year = {2014},
  month = jul,
  journal = {Forests},
  volume = {5},
  number = {7},
  pages = {1682--1694},
  issn = {1999-4907},
  doi = {10.3390/f5071682},
  urldate = {2023-03-03},
  langid = {english}
}

@article{holzwarthEarthObservationBased2020,
  title = {Earth {{Observation Based Monitoring}} of {{Forests}} in {{Germany}}: {{A Review}}},
  shorttitle = {Earth {{Observation Based Monitoring}} of {{Forests}} in {{Germany}}},
  author = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia},
  year = {2020},
  month = oct,
  journal = {Remote Sensing},
  volume = {12},
  number = {21},
  pages = {3570},
  issn = {2072-4292},
  doi = {10.3390/rs12213570},
  urldate = {2023-03-07},
  abstract = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.},
  langid = {english}
}

@article{hopfstockAufWegDigitalen2021,
  title = {{Auf dem Weg zu einem Digitalen Zwilling von Deutschland}},
  author = {Hopfstock, Anja},
  year = {2021},
  journal = {zfv -- Zeitschrift f{\"u}r Geod{\"a}sie, Geoinformation und Landmanagement},
  number = {6/2021},
  pages = {385--390},
  issn = {1618-8950},
  doi = {10.12902/zfv-0379-2021},
  urldate = {2023-02-27},
  abstract = {A high resolution, digital twin of Germany will enable Federal and Local Authorities to address current issues, such as adapting to climate change, increased land use or sociodemographic changes in an efficient and also holistic way. The first step is the development of a countrywide areal LiDAR-scan based 3D model. The LiDAR survey will have a resolution of at least 40 points per square meter. These data will be linked with many other geospatial datasets in a cloud-based data management platform. BKG is currently creating a first prototype in cooperation with the city of Hamburg for demonstrating the feasibility of the concept.},
  langid = {ngerman}
}

@article{huoEstimatingConservationValue2023,
  title = {Estimating the Conservation Value of Boreal Forests Using Airborne Laser Scanning},
  author = {Huo, Langning and Strengbom, Joachim and Lundmark, Tomas and Westerfelt, Per and Lindberg, Eva},
  year = {2023},
  month = mar,
  journal = {Ecological Indicators},
  volume = {147},
  pages = {109946},
  issn = {1470160X},
  doi = {10.1016/j.ecolind.2023.109946},
  urldate = {2023-03-22},
  langid = {english}
}

@article{huoLowVegetationIdentification2022,
  title = {Towards Low Vegetation Identification: {{A}} New Method for Tree Crown Segmentation from {{LiDAR}} Data Based on a Symmetrical Structure Detection Algorithm ({{SSD}})},
  shorttitle = {Towards Low Vegetation Identification},
  author = {Huo, Langning and Lindberg, Eva and Holmgren, Johan},
  year = {2022},
  month = mar,
  journal = {Remote Sensing of Environment},
  volume = {270},
  pages = {112857},
  issn = {00344257},
  doi = {10.1016/j.rse.2021.112857},
  urldate = {2023-04-11},
  langid = {english}
}

@article{hyyppaAccurateDerivationStem2020,
  title = {Accurate Derivation of Stem Curve and Volume Using Backpack Mobile Laser Scanning},
  author = {Hyypp{\"a}, Eric and Kukko, Antero and Kaijaluoto, Risto and White, Joanne C. and Wulder, Michael A. and Py{\"o}r{\"a}l{\"a}, Jiri and Liang, Xinlian and Yu, Xiaowei and Wang, Yunsheng and Kaartinen, Harri and Virtanen, Juho-Pekka and Hyypp{\"a}, Juha},
  year = {2020},
  month = mar,
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {161},
  pages = {246--262},
  issn = {09242716},
  doi = {10.1016/j.isprsjprs.2020.01.018},
  urldate = {2023-04-28},
  langid = {english}
}

@article{iglhautStructureMotionPhotogrammetry2019,
  title = {Structure from {{Motion Photogrammetry}} in {{Forestry}}: A {{Review}}},
  shorttitle = {Structure from {{Motion Photogrammetry}} in {{Forestry}}},
  author = {Iglhaut, Jakob and Cabo, Carlos and Puliti, Stefano and Piermattei, Livia and O'Connor, James and Rosette, Jacqueline},
  year = {2019},
  month = sep,
  journal = {Current Forestry Reports},
  volume = {5},
  number = {3},
  pages = {155--168},
  issn = {2198-6436},
  doi = {10.1007/s40725-019-00094-3},
  urldate = {2023-03-03},
  langid = {english}
}

@article{iglsederPotentialCombiningSatellite2023,
  title = {The Potential of Combining Satellite and Airborne Remote Sensing Data for Habitat Classification and Monitoring in Forest Landscapes},
  author = {Iglseder, Anna and Immitzer, Markus and Dost{\'a}lov{\'a}, Alena and Kasper, Andreas and Pfeifer, Norbert and Bauerhansl, Christoph and Sch{\"o}ttl, Stefan and Hollaus, Markus},
  year = {2023},
  month = mar,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {117},
  pages = {103131},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.103131},
  urldate = {2023-03-22},
  langid = {english}
}

@article{jarronDetectionQuantificationCoarse2021,
  title = {Detection and {{Quantification}} of {{Coarse Woody Debris}} in {{Natural Forest Stands Using Airborne LiDAR}}},
  author = {Jarron, Lukas R and Coops, Nicholas C and MacKenzie, William H and Dykstra, Pamela},
  year = {2021},
  month = sep,
  journal = {Forest Science},
  volume = {67},
  number = {5},
  pages = {550--563},
  issn = {0015-749X, 1938-3738},
  doi = {10.1093/forsci/fxab023},
  urldate = {2023-02-15},
  abstract = {Abstract             Coarse woody debris (CWD) is a meaningful contributor to forest carbon cycles, wildlife habitat, and biodiversity and can influence wildfire behavior. Using airborne laser scanning (ALS), we map CWD across a range of natural forest stand types in north-central British Columbia, Canada, providing forest managers with spatially detailed information on the presence and volume of ground-level woody biomass. We describe a novel methodology that isolates CWD returns from large diameter logs (\&gt;30cm) using a refined grounding algorithm, a mixture of height and pulse-based filters and linear pattern recognition, to transform ALS returns into measurable, vectorized shapes. We then assess the accuracy of CWD detection at the individual log level and predict CWD volume at the plot level. We detected 64\% of CWD logs and 79\% of CWD volume within our plots. Increased elevation of CWD significantly aided detection (P\>=\>0.04), whereas advanced stages of decay hindered detection (P\>=\>0.04). ALS-predicted CWD volume totals were compared against field-measured CWD and displayed a strong correlation (R\>=\>0.81), allowing us to expand the methodology to map CWD over a larger region. The expanded CWD volume map compared ALS volume predictions between stands and suggests greater volume in stands with older and more heterogeneous stand structure.},
  langid = {english}
}

@article{jarronDetectionSubcanopyForest2020,
  title = {Detection of Sub-Canopy Forest Structure Using Airborne {{LiDAR}}},
  author = {Jarron, Lukas R. and Coops, Nicholas C. and MacKenzie, William H. and Tompalski, Piotr and Dykstra, Pamela},
  year = {2020},
  month = jul,
  journal = {Remote Sensing of Environment},
  volume = {244},
  pages = {111770},
  issn = {00344257},
  doi = {10.1016/j.rse.2020.111770},
  urldate = {2023-02-15},
  langid = {english}
}

@article{johnsonFineresolutionLandscapescaleBiomass2022,
  title = {Fine-Resolution Landscape-Scale Biomass Mapping Using a Spatiotemporal Patchwork of {{LiDAR}} Coverages},
  author = {Johnson, Lucas K. and Mahoney, Michael J. and Bevilacqua, Eddie and Stehman, Stephen V. and Domke, Grant M. and Beier, Colin M.},
  year = {2022},
  month = nov,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {114},
  pages = {103059},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.103059},
  urldate = {2023-02-15},
  langid = {english}
}

@article{joyceDetectionCoarseWoody2019,
  title = {Detection of Coarse Woody Debris Using Airborne Light Detection and Ranging ({{LiDAR}})},
  author = {Joyce, Michael J. and Erb, John D. and Sampson, Barry A. and Moen, Ron A.},
  year = {2019},
  month = feb,
  journal = {Forest Ecology and Management},
  volume = {433},
  pages = {678--689},
  issn = {03781127},
  doi = {10.1016/j.foreco.2018.11.049},
  urldate = {2023-02-15},
  langid = {english}
}

@article{juckerAllometricEquationsIntegrating2017,
  title = {Allometric Equations for Integrating Remote Sensing Imagery into Forest Monitoring Programmes},
  author = {Jucker, Tommaso and Caspersen, John and Chave, J{\'e}r{\^o}me and Antin, C{\'e}cile and Barbier, Nicolas and Bongers, Frans and Dalponte, Michele and {van Ewijk}, Karin Y. and Forrester, David I. and Haeni, Matthias and Higgins, Steven I. and Holdaway, Robert J. and Iida, Yoshiko and Lorimer, Craig and Marshall, Peter L. and Momo, St{\'e}phane and Moncrieff, Glenn R. and Ploton, Pierre and Poorter, Lourens and Rahman, Kassim Abd and Schlund, Michael and Sonk{\'e}, Bonaventure and Sterck, Frank J. and Trugman, Anna T. and Usoltsev, Vladimir A. and Vanderwel, Mark C. and Waldner, Peter and Wedeux, Beatrice M. M. and Wirth, Christian and W{\"o}ll, Hannsj{\"o}rg and Woods, Murray and Xiang, Wenhua and Zimmermann, Niklaus E. and Coomes, David A.},
  year = {2017},
  month = jan,
  journal = {Global Change Biology},
  volume = {23},
  number = {1},
  pages = {177--190},
  issn = {13541013},
  doi = {10.1111/gcb.13388},
  urldate = {2023-04-18},
  langid = {english}
}

@article{jutras-perreaultDetectingPresenceNatural2023,
  title = {Detecting the Presence of Natural Forests Using Airborne Laser Scanning Data},
  author = {{Jutras-Perreault}, Marie-Claude and Gobakken, Terje and N{\ae}sset, Erik and {\O}rka, Hans Ole},
  year = {2023},
  month = oct,
  journal = {Forest Ecosystems},
  pages = {100146},
  issn = {21975620},
  doi = {10.1016/j.fecs.2023.100146},
  urldate = {2023-10-30},
  langid = {english}
}

@article{kaminskaSpeciesrelatedSingleDead2018,
  title = {Species-Related Single Dead Tree Detection Using Multi-Temporal {{ALS}} Data and {{CIR}} Imagery},
  author = {Kami{\'n}ska, Agnieszka and Lisiewicz, Maciej and Stere{\'n}czak, Krzysztof and Kraszewski, Bart{\l}omiej and Sadkowski, Rafa{\l}},
  year = {2018},
  month = dec,
  journal = {Remote Sensing of Environment},
  volume = {219},
  pages = {31--43},
  issn = {00344257},
  doi = {10.1016/j.rse.2018.10.005},
  urldate = {2023-02-15},
  langid = {english}
}

@article{kamoskeLeafAreaDensity2019,
  title = {Leaf Area Density from Airborne {{LiDAR}}: {{Comparing}} Sensors and Resolutions in a Temperate Broadleaf Forest Ecosystem},
  shorttitle = {Leaf Area Density from Airborne {{LiDAR}}},
  author = {Kamoske, Aaron G. and Dahlin, Kyla M. and Stark, Scott C. and Serbin, Shawn P.},
  year = {2019},
  month = feb,
  journal = {Forest Ecology and Management},
  volume = {433},
  pages = {364--375},
  issn = {03781127},
  doi = {10.1016/j.foreco.2018.11.017},
  urldate = {2023-03-09},
  langid = {english}
}

@article{kaneLidarSEyeView2022,
  title = {A {{Lidar}}'s-{{Eye View}} of {{How Forests Are Faring}}},
  author = {Kane, Van and Van Wagtendonk, Liz and Brenner, Andrew},
  year = {2022},
  month = apr,
  journal = {Eos},
  volume = {103},
  issn = {2324-9250},
  doi = {10.1029/2022EO220218},
  urldate = {2023-11-21},
  abstract = {Success in Yosemite is driving the wider use of lidar surveys to support forest health and wildfire resilience, study wildlife habitats, and monitor water resources.}
}

@article{kisslingCountrywideDataEcosystem2023,
  title = {Country-Wide Data of Ecosystem Structure from the Third {{Dutch}} Airborne Laser Scanning Survey},
  author = {Kissling, W. Daniel and Shi, Yifang and Koma, Zs{\'o}fia and Meijer, Christiaan and Ku, Ou and Nattino, Francesco and Seijmonsbergen, Arie C. and Grootes, Meiert W.},
  year = {2023},
  month = feb,
  journal = {Data in Brief},
  volume = {46},
  pages = {108798},
  issn = {23523409},
  doi = {10.1016/j.dib.2022.108798},
  urldate = {2024-04-08},
  langid = {english}
}

@article{kopeckyTopographicWetnessIndex2021,
  title = {Topographic {{Wetness Index}} Calculation Guidelines Based on Measured Soil Moisture and Plant Species Composition},
  author = {Kopeck{\'y}, Martin and Macek, Martin and Wild, Jan},
  year = {2021},
  month = feb,
  journal = {Science of The Total Environment},
  volume = {757},
  pages = {143785},
  issn = {00489697},
  doi = {10.1016/j.scitotenv.2020.143785},
  urldate = {2023-02-15},
  langid = {english}
}

@article{korpelaInfluencePhenologyWaveform2023,
  title = {Influence of Phenology on Waveform Features in Deciduous and Coniferous Trees in Airborne {{LiDAR}}},
  author = {Korpela, Ilkka and Polvivaara, Antti and Hovi, Aarne and Junttila, Samuli and Holopainen, Markus},
  year = {2023},
  month = aug,
  journal = {Remote Sensing of Environment},
  volume = {293},
  pages = {113618},
  issn = {00344257},
  doi = {10.1016/j.rse.2023.113618},
  urldate = {2023-05-17},
  langid = {english}
}

@article{kostensaloRecreatingStructurallyRealistic2023,
  title = {Recreating Structurally Realistic Tree Maps with Airborne Laser Scanning and Ground Measurements},
  author = {Kostensalo, J. and Meht{\"a}talo, L. and Tuominen, S. and Packalen, P. and Myllym{\"a}ki, M.},
  year = {2023},
  month = dec,
  journal = {Remote Sensing of Environment},
  volume = {298},
  pages = {113782},
  issn = {00344257},
  doi = {10.1016/j.rse.2023.113782},
  urldate = {2023-10-23},
  langid = {english}
}

@article{krisanskiForestStructuralComplexity2021,
  title = {Forest {{Structural Complexity Tool}}---{{An Open Source}}, {{Fully-Automated Tool}} for {{Measuring Forest Point Clouds}}},
  author = {Krisanski, Sean and Taskhiri, Mohammad Sadegh and Gonzalez Aracil, Susana and Herries, David and Muneri, Allie and Gurung, Mohan Babu and Montgomery, James and Turner, Paul},
  year = {2021},
  month = nov,
  journal = {Remote Sensing},
  volume = {13},
  number = {22},
  pages = {4677},
  issn = {2072-4292},
  doi = {10.3390/rs13224677},
  urldate = {2023-02-17},
  abstract = {Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.},
  langid = {english}
}

@article{krzystekLargeScaleMappingTree2020,
  title = {Large-{{Scale Mapping}} of {{Tree Species}} and {{Dead Trees}} in {{{\v S}umava National Park}} and {{Bavarian Forest National Park Using Lidar}} and {{Multispectral Imagery}}},
  author = {Krzystek, Peter and Serebryanyk, Alla and Schn{\"o}rr, Claudius and {\v C}ervenka, Jaroslav and Heurich, Marco},
  year = {2020},
  month = feb,
  journal = {Remote Sensing},
  volume = {12},
  number = {4},
  pages = {661},
  issn = {2072-4292},
  doi = {10.3390/rs12040661},
  urldate = {2023-02-15},
  abstract = {Knowledge of forest structures---and of dead wood in particular---is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species---subdivided into conifers and broadleaf trees---and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in {\v S}umava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for {\v S}umava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood---located in areas of pest infestation---is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13\%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90\%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.},
  langid = {english}
}

@article{kukenbrinkBenchmarkingLaserScanning2022,
  title = {Benchmarking Laser Scanning and Terrestrial Photogrammetry to Extract Forest Inventory Parameters in a Complex Temperate Forest},
  author = {K{\"u}kenbrink, Daniel and Marty, Mauro and B{\"o}sch, Ruedi and Ginzler, Christian},
  year = {2022},
  month = sep,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {113},
  pages = {102999},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.102999},
  urldate = {2023-03-21},
  langid = {english}
}

@article{kukenbrinkQuantificationHiddenCanopy2017,
  title = {Quantification of Hidden Canopy Volume of Airborne Laser Scanning Data Using a Voxel Traversal Algorithm},
  author = {K{\"u}kenbrink, Daniel and Schneider, Fabian D. and Leiterer, Reik and Schaepman, Michael E. and Morsdorf, Felix},
  year = {2017},
  month = jun,
  journal = {Remote Sensing of Environment},
  volume = {194},
  pages = {424--436},
  issn = {00344257},
  doi = {10.1016/j.rse.2016.10.023},
  urldate = {2023-05-17},
  langid = {english}
}

@article{lampingComparisonLowCostCommercial2021,
  title = {Comparison of {{Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry}} to {{Airborne Laser Scanning}} across {{Multiple Forest Types}} in {{California}}, {{USA}}},
  author = {Lamping, James E. and Zald, Harold S. J. and Madurapperuma, Buddhika D. and Graham, Jim},
  year = {2021},
  month = oct,
  journal = {Remote Sensing},
  volume = {13},
  number = {21},
  pages = {4292},
  issn = {2072-4292},
  doi = {10.3390/rs13214292},
  urldate = {2023-10-20},
  abstract = {Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74--0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17--0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53--0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.},
  langid = {english}
}

@article{lamprechtMachineLearningMethod2017,
  title = {A {{Machine Learning Method}} for {{Co-Registration}} and {{Individual Tree Matching}} of {{Forest Inventory}} and {{Airborne Laser Scanning Data}}},
  author = {Lamprecht, Sebastian and Hill, Andreas and Stoffels, Johannes and Udelhoven, Thomas},
  year = {2017},
  month = may,
  journal = {Remote Sensing},
  volume = {9},
  number = {5},
  pages = {505},
  issn = {2072-4292},
  doi = {10.3390/rs9050505},
  urldate = {2023-02-15},
  langid = {english}
}

@article{lenoirUnveilUnseenUsing2022,
  title = {Unveil the Unseen: {{Using LiDAR}} to Capture Time-lag Dynamics in the Herbaceous Layer of {{European}} Temperate Forests},
  shorttitle = {Unveil the Unseen},
  author = {Lenoir, Jonathan and Gril, Eva and Durrieu, Sylvie and Horen, H{\'e}l{\`e}ne and Laslier, Marianne and Lembrechts, Jonas J. and Zellweger, Florian and Alleaume, Samuel and Brasseur, Boris and Buridant, J{\'e}r{\^o}me and Dayal, Karun and De Frenne, Pieter and Gallet-Moron, Emilie and Marrec, Ronan and Meeussen, Camille and Rocchini, Duccio and Van Meerbeek, Koenraad and Decocq, Guillaume},
  year = {2022},
  month = feb,
  journal = {Journal of Ecology},
  volume = {110},
  number = {2},
  pages = {282--300},
  issn = {0022-0477, 1365-2745},
  doi = {10.1111/1365-2745.13837},
  urldate = {2023-04-12},
  langid = {english}
}

@article{levickScalingWoodVolume2016,
  title = {Scaling Wood Volume Estimates from Inventory Plots to Landscapes with Airborne {{LiDAR}} in Temperate Deciduous Forest},
  author = {Levick, Shaun R. and Hessenm{\"o}ller, Dominik and Schulze, E-Detlef},
  year = {2016},
  month = dec,
  journal = {Carbon Balance and Management},
  volume = {11},
  number = {1},
  pages = {7},
  issn = {1750-0680},
  doi = {10.1186/s13021-016-0048-7},
  urldate = {2023-03-09},
  langid = {english}
}

@article{liangCloseRangeRemoteSensing2022,
  title = {Close-{{Range Remote Sensing}} of {{Forests}}: {{The}} State of the Art, Challenges, and Opportunities for Systems and Data Acquisitions},
  shorttitle = {Close-{{Range Remote Sensing}} of {{Forests}}},
  author = {Liang, Xinlian and Kukko, Antero and Balenovic, Ivan and Saarinen, Ninni and Junttila, Samuli and Kankare, Ville and Holopainen, Markus and Mokros, Martin and Surovy, Peter and Kaartinen, Harri and Jurjevic, Luka and Honkavaara, Eija and Nasi, Roope and Liu, Jingbin and Hollaus, Markus and Tian, Jiaojiao and Yu, Xiaowei and Pan, Jie and Cai, Shangshu and Virtanen, Juho-Pekka and Wang, Yunsheng and Hyyppa, Juha},
  year = {2022},
  month = sep,
  journal = {IEEE Geoscience and Remote Sensing Magazine},
  volume = {10},
  number = {3},
  pages = {32--71},
  issn = {2168-6831, 2473-2397, 2373-7468},
  doi = {10.1109/MGRS.2022.3168135},
  urldate = {2023-02-17}
}

@article{lidbergMappingDrainageDitches2023,
  title = {Mapping {{Drainage Ditches}} in {{Forested Landscapes Using Deep Learning}} and {{Aerial Laser Scanning}}},
  author = {Lidberg, William and Paul, Siddhartho Shekhar and Westphal, Florian and Richter, Kai Florian and Lavesson, Niklas and Melniks, Raitis and Ivanovs, Janis and Ciesielski, Mariusz and Leinonen, Antti and {\AA}gren, Anneli M.},
  year = {2023},
  month = mar,
  journal = {Journal of Irrigation and Drainage Engineering},
  volume = {149},
  number = {3},
  pages = {04022051},
  issn = {0733-9437, 1943-4774},
  doi = {10.1061/JIDEDH.IRENG-9796},
  urldate = {2023-02-15},
  langid = {english}
}

@article{linesShapeTreesReimagining2022,
  title = {The Shape of Trees: {{Reimagining}} Forest Ecology in Three Dimensions with Remote Sensing},
  shorttitle = {The Shape of Trees},
  author = {Lines, Emily Rebecca and Fischer, Fabian J{\"o}rg and Owen, Harry Jon Foord and Jucker, Tommaso},
  year = {2022},
  month = aug,
  journal = {Journal of Ecology},
  volume = {110},
  number = {8},
  pages = {1730--1745},
  issn = {0022-0477, 1365-2745},
  doi = {10.1111/1365-2745.13944},
  urldate = {2023-02-16},
  langid = {english}
}

@article{mahoneyFilteringGroundNoise2022,
  title = {Filtering Ground Noise from {{LiDAR}} Returns Produces Inferior Models of Forest Aboveground Biomass in Heterogenous Landscapes},
  author = {Mahoney, Michael J and Johnson, Lucas K and Bevilacqua, Eddie and Beier, Colin M},
  year = {2022},
  month = dec,
  journal = {GIScience \& Remote Sensing},
  volume = {59},
  number = {1},
  pages = {1266--1280},
  issn = {1548-1603, 1943-7226},
  doi = {10.1080/15481603.2022.2103069},
  urldate = {2023-07-20},
  langid = {english}
}

@article{malhiNewPerspectivesEcology2018,
  title = {New Perspectives on the Ecology of Tree Structure and Tree Communities through Terrestrial Laser Scanning},
  author = {Malhi, Yadvinder and Jackson, Tobias and Patrick Bentley, Lisa and Lau, Alvaro and Shenkin, Alexander and Herold, Martin and Calders, Kim and Bartholomeus, Harm and Disney, Mathias I.},
  year = {2018},
  month = apr,
  journal = {Interface Focus},
  volume = {8},
  number = {2},
  pages = {20170052},
  issn = {2042-8898, 2042-8901},
  doi = {10.1098/rsfs.2017.0052},
  urldate = {2023-03-06},
  abstract = {Terrestrial laser scanning (TLS) opens up the possibility of describing the three-dimensional structures of trees in natural environments with unprecedented detail and accuracy. It is already being extensively applied to describe               how               ecosystem biomass and structure vary between sites, but can also facilitate major advances in developing and testing mechanistic theories of tree form and forest structure, thereby enabling us to understand               why               trees and forests have the biomass and three-dimensional structure they do. Here we focus on the ecological challenges and benefits of understanding tree form, and highlight some advances related to capturing and describing tree shape that are becoming possible with the advent of TLS. We present examples of ongoing work that applies, or could potentially apply, new TLS measurements to better understand the constraints on optimization of tree form. Theories of resource distribution networks, such as metabolic scaling theory, can be tested and further refined. TLS can also provide new approaches to the scaling of woody surface area and crown area, and thereby better quantify the metabolism of trees. Finally, we demonstrate how we can develop a more mechanistic understanding of the effects of avoidance of wind risk on tree form and maximum size. Over the next few years, TLS promises to deliver both major empirical and conceptual advances in the quantitative understanding of trees and tree-dominated ecosystems, leading to advances in understanding the ecology of why trees and ecosystems look and grow the way they do.},
  langid = {english}
}

@article{maltamoEstimationPeriodicAnnual2022,
  title = {Estimation of Periodic Annual Increment of Tree Ring Widths by Airborne Laser Scanning},
  author = {Maltamo, Matti and Vartiainen, Petteri and Packalen, Petteri and Korhonen, Lauri},
  year = {2022},
  month = apr,
  journal = {Canadian Journal of Forest Research},
  volume = {52},
  number = {4},
  pages = {644--651},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2021-0267},
  urldate = {2023-02-15},
  abstract = {Most forest growth studies using airborne laser scanning (ALS) consider how the changes in forest attributes are observed in repeated ALS data acquisitions, but the prediction of future forest growth from ALS data is still a rarely discussed topic. This study examined the prediction of the periodic annual increment (PAI) of the width of tree rings over a period of 10~years. The requirement for this approach is that ALS data are acquired at the beginning of the growth period. This is followed by field measurements of growth by drilling after a given growth period. The PAI was modelled in terms of ALS metrics by using the principle of the area-based approach. The metrics related to intensity were particularly significant as predictors, whereas the effective leaf area index was not. The root-mean-square error (RMSE) of the predictions was slightly over 21\%. Additional field information (soil type, management operations) improved the RMSE by 2.7 percentage units.},
  langid = {english}
}

@article{mandlburgerCOMPARISONSINGLEPHOTON2019,
  title = {A {{COMPARISON OF SINGLE PHOTON AND FULL WAVEFORM LIDAR}}},
  author = {Mandlburger, G. and Lehner, H. and Pfeifer, N.},
  year = {2019},
  month = may,
  journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume = {IV-2/W5},
  pages = {397--404},
  issn = {2194-9050},
  doi = {10.5194/isprs-annals-IV-2-W5-397-2019},
  urldate = {2023-03-13},
  abstract = {Abstract. Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2--3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10\,cm.},
  langid = {english}
}

@article{mesas-carrascosaCombiningLiDARIntensity2012,
  title = {Combining {{LiDAR}} Intensity with Aerial Camera Data to Discriminate Agricultural Land Uses},
  author = {{Mesas-Carrascosa}, Francisco Javier and {Castillejo-Gonz{\'a}lez}, Isabel Luisa and De La Orden, Manuel S{\'a}nchez and Porras, Alfonso Garc{\'i}a-Ferrer},
  year = {2012},
  month = jun,
  journal = {Computers and Electronics in Agriculture},
  volume = {84},
  pages = {36--46},
  issn = {01681699},
  doi = {10.1016/j.compag.2012.02.020},
  urldate = {2023-07-11},
  langid = {english}
}

@article{moanDetectingExcludingDisturbed2023,
  title = {Detecting and Excluding Disturbed Forest Areas Improves Site Index Determination Using Bitemporal Airborne Laser Scanner Data},
  author = {Moan, Maria {\AA} and Noordermeer, Lennart and White, Joanne C and Coops, Nicholas C and Bollands{\aa}s, Ole M},
  editor = {Fassnacht, Fabian},
  year = {2023},
  month = may,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpad025},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad025},
  urldate = {2023-05-17},
  abstract = {Abstract             Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46--0.59 and 0.62--0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P\,\&lt;\,0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.},
  langid = {english}
}

@article{mohieddinneAssessmentSoilCompaction2023,
  title = {Assessment of Soil Compaction and Rutting in Managed Forests through an Airborne {{{\textsc{LiDAR}}}} Technique},
  shorttitle = {Assessment of Soil Compaction and Rutting in Managed Forests through an Airborne},
  author = {Mohieddinne, Hamza and Brasseur, Boris and Gallet-Moron, Emilie and Lenoir, Jonathan and Spicher, Fabien and Kobaissi, Ahmad and Horen, H{\'e}l{\`e}ne},
  year = {2023},
  month = mar,
  journal = {Land Degradation \& Development},
  volume = {34},
  number = {5},
  pages = {1558--1569},
  issn = {1085-3278, 1099-145X},
  doi = {10.1002/ldr.4553},
  urldate = {2023-03-20},
  langid = {english}
}

@article{morleyUpdatingForestRoad2023,
  title = {Updating Forest Road Networks Using Single Photon {{LiDAR}} in Northern {{Forest}} Environments},
  author = {Morley, Ilythia D and Coops, Nicholas C and Roussel, Jean-Romain and Achim, Alexis and Dech, Jeff and Meecham, Dawson and McCartney, Grant and Reid, Douglas E B and McPherson, Scott and Quist, Lauren and McDonell, Chris},
  editor = {Fassnacht, Fabian},
  year = {2023},
  month = apr,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpad021},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad021},
  urldate = {2023-05-17},
  abstract = {Abstract             Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario's Boreal and Great Lakes---St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of `Full access' roads and 29 per cent of `Partial access' roads being undriveable by standard vehicles and 45 per cent of `Status unknown' roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4~m, and the average road width error is 2~m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.},
  langid = {english}
}

@incollection{morsdorfLaegerenSiteAugmented2020,
  title = {The {{Laegeren Site}}: {{An Augmented Forest Laboratory}}: {{Combining}} 3-{{D Reconstruction}} and {{Radiative Transfer Models}} for {{Trait-Based Assessment}} of {{Functional Diversity}}},
  shorttitle = {The {{Laegeren Site}}},
  booktitle = {Remote {{Sensing}} of {{Plant Biodiversity}}},
  author = {Morsdorf, Felix and Schneider, Fabian D. and Gullien, Carla and K{\"u}kenbrink, Daniel and Leiterer, Reik and Schaepman, Michael E.},
  editor = {{Cavender-Bares}, Jeannine and Gamon, John A. and Townsend, Philip A.},
  year = {2020},
  pages = {83--104},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-030-33157-3_4},
  urldate = {2023-05-17},
  abstract = {Abstract             Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity.},
  isbn = {978-3-030-33156-6 978-3-030-33157-3},
  langid = {english}
}

@article{moudryVegetationStructureDerived2023,
  title = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability: {{The}} Way Forward},
  shorttitle = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability},
  author = {Moudr{\'y}, V{\'i}t{\v e}zslav and Cord, Anna F. and G{\'a}bor, Luk{\'a}{\v s} and Laurin, Gaia Vaglio and Bart{\'a}k, Vojt{\v e}ch and Gdulov{\'a}, Kate{\v r}ina and Malavasi, Marco and Rocchini, Duccio and Stere{\'n}czak, Krzysztof and Pro{\v s}ek, Ji{\v r}{\'i} and Kl{\'a}p{\v s}t{\v e}, Petr and Wild, Jan},
  year = {2023},
  month = jan,
  journal = {Diversity and Distributions},
  volume = {29},
  number = {1},
  pages = {39--50},
  issn = {1366-9516, 1472-4642},
  doi = {10.1111/ddi.13644},
  urldate = {2023-02-15},
  langid = {english}
}

@article{murrayEstimatingTreeSpecies2024,
  title = {Estimating Tree Species Composition from Airborne Laser Scanning Data Using Point-Based Deep Learning Models},
  author = {Murray, Brent A. and Coops, Nicholas C. and Winiwarter, Lukas and White, Joanne C. and Dick, Adam and Barbeito, Ignacio and Ragab, Ahmed},
  year = {2024},
  month = jan,
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {207},
  pages = {282--297},
  issn = {09242716},
  doi = {10.1016/j.isprsjprs.2023.12.008},
  urldate = {2024-01-04},
  langid = {english}
}

@article{neudamSimulationSilviculturalTreatments2023,
  title = {Simulation of Silvicultural Treatments Based on Real {{3D}} Forest Data from Mobile Laser Scanning Point Clouds},
  author = {Neudam, Liane C. and Fuchs, Jasper M. and Mjema, Ezekiel and Johannmeier, Alina and Ammer, Christian and Annigh{\"o}fer, Peter and Paul, Carola and Seidel, Dominik},
  year = {2023},
  month = mar,
  journal = {Trees, Forests and People},
  volume = {11},
  pages = {100372},
  issn = {26667193},
  doi = {10.1016/j.tfp.2023.100372},
  urldate = {2023-05-09},
  langid = {english}
}

@article{nilssonNationwideForestAttribute2017,
  title = {A Nationwide Forest Attribute Map of {{Sweden}} Predicted Using Airborne Laser Scanning Data and Field Data from the {{National Forest Inventory}}},
  author = {Nilsson, Mats and Nordkvist, Karin and Jonz{\'e}n, Jonas and Lindgren, Nils and Axensten, Peder and Wallerman, J{\"o}rgen and Egberth, Mikael and Larsson, Svante and Nilsson, Liselott and Eriksson, Johan and Olsson, H{\aa}kan},
  year = {2017},
  month = jun,
  journal = {Remote Sensing of Environment},
  volume = {194},
  pages = {447--454},
  issn = {00344257},
  doi = {10.1016/j.rse.2016.10.022},
  urldate = {2023-02-24},
  langid = {english}
}

@article{noordermeerComparingAccuraciesForest2019,
  title = {Comparing the Accuracies of Forest Attributes Predicted from Airborne Laser Scanning and Digital Aerial Photogrammetry in Operational Forest Inventories},
  author = {Noordermeer, Lennart and Bollands{\aa}s, Ole Martin and {\O}rka, Hans Ole and N{\ae}sset, Erik and Gobakken, Terje},
  year = {2019},
  month = jun,
  journal = {Remote Sensing of Environment},
  volume = {226},
  pages = {26--37},
  issn = {00344257},
  doi = {10.1016/j.rse.2019.03.027},
  urldate = {2023-02-15},
  langid = {english}
}

@article{noordermeerImputingStemFrequency2023,
  title = {Imputing Stem Frequency Distributions Using Harvester and Airborne Laser Scanner Data: A Comparison of Inventory Approaches},
  shorttitle = {Imputing Stem Frequency Distributions Using Harvester and Airborne Laser Scanner Data},
  author = {Noordermeer, Lennart and {\O}rka, Hans Ole and Gobakken, Terje},
  year = {2023},
  journal = {Silva Fennica},
  volume = {57},
  number = {3},
  issn = {00375330, 22424075},
  doi = {10.14214/sf.23023},
  urldate = {2023-12-06},
  abstract = {Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold's error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.}
}

@article{noordermeerPredictingMappingSite2020,
  title = {Predicting and Mapping Site Index in Operational Forest Inventories Using Bitemporal Airborne Laser Scanner Data},
  author = {Noordermeer, Lennart and Gobakken, Terje and N{\ae}sset, Erik and Bollands{\aa}s, Ole Martin},
  year = {2020},
  month = feb,
  journal = {Forest Ecology and Management},
  volume = {457},
  pages = {117768},
  issn = {03781127},
  doi = {10.1016/j.foreco.2019.117768},
  urldate = {2023-02-24},
  langid = {english}
}

@article{nord-larsenEstimationForestResources2012,
  title = {Estimation of Forest Resources from a Country Wide Laser Scanning Survey and National Forest Inventory Data},
  author = {{Nord-Larsen}, Thomas and Schumacher, Johannes},
  year = {2012},
  month = apr,
  journal = {Remote Sensing of Environment},
  volume = {119},
  pages = {148--157},
  issn = {00344257},
  doi = {10.1016/j.rse.2011.12.022},
  urldate = {2023-02-15},
  langid = {english}
}

@article{nowakHiddenGapsCanopy2022,
  title = {Hidden Gaps under the Canopy: {{LiDAR-based}} Detection and Quantification of Porosity in Tree Belts},
  shorttitle = {Hidden Gaps under the Canopy},
  author = {Nowak, Maciej M. and P{\k e}dziwiatr, Katarzyna and Bogawski, Pawe{\l}},
  year = {2022},
  month = sep,
  journal = {Ecological Indicators},
  volume = {142},
  pages = {109243},
  issn = {1470160X},
  doi = {10.1016/j.ecolind.2022.109243},
  urldate = {2023-02-15},
  langid = {english}
}

@misc{ogfdroneteamLiDARALSIm2022,
  title = {{LiDAR (ALS) im Wald Teil 1: R{\"u}ckegassen sichtbar machen {\textbar} Drohnenbefliegungen in Forst- und Landwirtschaft}},
  shorttitle = {{LiDAR (ALS) im Wald Teil 1}},
  author = {{OGF drone team}},
  year = {2022},
  month = jul,
  urldate = {2023-03-02},
  langid = {ngerman}
}

@article{olpendaEstimationSubcanopySolar2019,
  title = {Estimation of Sub-Canopy Solar Radiation from {{LiDAR}} Discrete Returns in Mixed Temporal Forest of {{Bia{\l}owie{\.z}a}}, {{Poland}}},
  author = {Olpenda, Alex S. and Stere{\'n}czak, Krzysztof and B{\k e}dkowski, Krzysztof},
  year = {2019},
  month = jul,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {79},
  pages = {116--132},
  issn = {15698432},
  doi = {10.1016/j.jag.2019.03.005},
  urldate = {2023-03-02},
  langid = {english}
}

@article{olpendaModelingSolarRadiation2018,
  title = {Modeling {{Solar Radiation}} in the {{Forest Using Remote Sensing Data}}: {{A Review}} of {{Approaches}} and {{Opportunities}}},
  shorttitle = {Modeling {{Solar Radiation}} in the {{Forest Using Remote Sensing Data}}},
  author = {Olpenda, Alex and Stere{\'n}czak, Krzysztof and B{\k e}dkowski, Krzysztof},
  year = {2018},
  month = may,
  journal = {Remote Sensing},
  volume = {10},
  number = {5},
  pages = {694},
  issn = {2072-4292},
  doi = {10.3390/rs10050694},
  urldate = {2023-03-02},
  langid = {english}
}

@article{packalenCircularSquarePlots2023,
  title = {Circular or Square Plots in {{ALS-based}} Forest Inventories---Does It Matter?},
  author = {Packalen, Petteri and Strunk, Jacob and Maltamo, Matti and Myllym{\"a}ki, Mari},
  year = {2023},
  month = jan,
  journal = {Forestry: An International Journal of Forest Research},
  volume = {96},
  number = {1},
  pages = {49--61},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpac032},
  urldate = {2023-04-11},
  abstract = {Abstract             In airborne laser scanning (ALS)-based forest inventories, there is commonly a discrepancy between the plot shape used for model fitting (typically circular) and the shape of population elements (typically square) where predictions are needed. Circular plots are easy to establish, locate and have the smallest number of edge trees on average. Therefore, a circle is the most common plot shape in both traditional and remote sensing-based forest inventories. In contrast, the shape of population elements used for remote sensing-based predictions is nearly always a square because it enables division of the target population into a grid of non-overlapping plots. In this study, we investigate shape effects for ALS-based forest inventories using circular and square plot shapes. This has not been examined earlier. Aboveground biomass was used as the response variable. The sampling design was created in a way that the probability of selection for any location inside a stem-mapped 30~m\,{\texttimes}\,30~m plot was the same for the circular (radius 7.95~m) and square (side length 14.09~m) plot. This configuration enabled us to compare circular and square plots with the same areas and identical sampling probabilities for every tree in the population. Our primary finding is that for equal area square and circular plots, there is no evidence of systematic prediction error when a model fitted to one shape is used to predict for the other shape. Our secondary finding is that root mean square error (RMSE) value is slightly underestimated (1.2 per cent) when a model fitted to circular plots is used to predict for square plots. A small underestimation of RMSE due to plot shape effect has hardly practical significance in stand-level forest management inventories, but the plot shape effect may be problematic in large area forest surveys.},
  langid = {english}
}

@article{pennerAutomatedCharacterizationForest2023,
  title = {Automated Characterization of Forest Canopy Vertical Layering for Predicting Forest Inventory Attributes by Layer Using Airborne {{LiDAR}} Data},
  author = {Penner, Margaret and White, Joanne C and Woods, Murray E},
  editor = {Latifi, Hooman},
  year = {2023},
  month = jun,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpad033},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad033},
  urldate = {2023-07-13},
  abstract = {Abstract             Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m3 ha-1), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 {\texttimes} 25~m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74\% (n~=\,266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46--52\% versus 57\%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17--19\% versus 18\% for BA and 12--15\% versus 12\% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer---both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions.},
  langid = {english}
}

@article{perssonTwophaseForestInventory2022,
  title = {Two-Phase Forest Inventory Using Very-High-Resolution Laser Scanning},
  author = {Persson, Henrik J. and Olofsson, Kenneth and Holmgren, Johan},
  year = {2022},
  month = mar,
  journal = {Remote Sensing of Environment},
  volume = {271},
  pages = {112909},
  issn = {00344257},
  doi = {10.1016/j.rse.2022.112909},
  urldate = {2023-02-24},
  langid = {english}
}

@article{plotheDigitalisierungImCluster2022,
  title = {{Der Digitalisierung im Cluster Forst und Holz auf die Spr{\"u}nge helfen}},
  author = {Plothe, Martina},
  year = {2022},
  month = oct,
  journal = {AFZ DerWald},
  volume = {2022},
  number = {19},
  urldate = {2023-03-27},
  abstract = {Borkenk{\"a}fer ,,riechende`` Drohnen, eine lernf{\"a}hige App zur Holzeinschlagsplanung oder digitale Zwillinge bei der Hol...},
  langid = {ngerman}
}

@article{prieurComparisonThreeAirborne2021,
  title = {A {{Comparison}} of {{Three Airborne Laser Scanner Types}} for {{Species Identification}} of {{Individual Trees}}},
  author = {Prieur, Jean-Fran{\c c}ois and {St-Onge}, Beno{\^i}t and Fournier, Richard A. and Woods, Murray E. and Rana, Parvez and Kneeshaw, Daniel},
  year = {2021},
  month = dec,
  journal = {Sensors},
  volume = {22},
  number = {1},
  pages = {35},
  issn = {1424-8220},
  doi = {10.3390/s22010035},
  urldate = {2023-02-15},
  abstract = {Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83--90\%; 12 species, 46--54\%; 4 species, 68--79\%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91\%; 12 species, 58\%; 4 species, 84\%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.},
  langid = {english}
}

@article{purfuerstForstlicheVerfahrenstechnikWo2022,
  title = {{Forstliche Verfahrenstechnik: Wo stehen wir, wo geht die Reise hin?}},
  shorttitle = {{Forstliche Verfahrenstechnik}},
  author = {Purf{\"u}rst, Thomas},
  year = {2022},
  month = oct,
  journal = {AFZ DerWald},
  volume = {2022},
  number = {19},
  urldate = {2023-03-27},
  abstract = {Dieser Artikel soll den aktuellen Stand der Forschung in der Forstlichen Verfahrenstechnik aufzeigen und einen Ausblick ...},
  langid = {ngerman}
}

@article{queinnecDevelopingForestInventory2022,
  ids = {queinnecDevelopingForestInventory2022a},
  title = {Developing a Forest Inventory Approach Using Airborne Single Photon Lidar Data: From Ground Plot Selection to Forest Attribute Prediction},
  shorttitle = {Developing a Forest Inventory Approach Using Airborne Single Photon Lidar Data},
  author = {Queinnec, Martin and Coops, Nicholas C and White, Joanne C and McCartney, Grant and Sinclair, Ian},
  year = {2022},
  month = may,
  journal = {Forestry: An International Journal of Forest Research},
  volume = {95},
  number = {3},
  pages = {347--362},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpab051},
  urldate = {2023-02-15},
  abstract = {Abstract             An increasing number of jurisdictions are integrating airborne laser scanning (ALS) into forest inventory programs to produce spatially explicit and accurate inventories of forest resources. However, wall-to-wall ALS coverage relative to the total area of managed forest remains limited in large forest nations such as Canada, wherein logistics, cost and acquisition capacity can be limiting factors. Technologies such as single photon light detection and ranging (SPL) have emerged commercially, which have the capacity to provide efficient ALS acquisitions over large areas and with a greater point density than conventional linear-mode ALS. However, the large-scale operational application of SPL in a forest inventory still needs to be effectively demonstrated. In this study, we used wall-to-wall SPL data (collected with a Leica SPL100) across a 630\,000 ha boreal forest in Ontario, Canada to develop a forest inventory. Specifically, we used a structurally guided sampling approach enabled via a principal component analysis of the SPL100 data to establish a network of 250 ground plots. Random forest models were then used to produce area-based estimates of forest attributes of interest. Results demonstrated that the sampling approach enabled the optimization and enhancement of the existing plot network by extending the range of sampled structural types and reducing the number of plots in oversampled forest types. Moreover, Lorey's height, basal area, quadratic mean diameter at breast height, stem density, gross and merchantable volume and above-ground biomass were estimated with a relative root mean square error of 8.5, 19.76, 13.97, 30.82, 21.53, 23.79 and 22.87 per cent, respectively, and relative bias \&lt;1 per cent. Model accuracies achieved using the SPL100 were comparable with those obtained using linear-mode ALS in a previous forest inventory. This study demonstrates the utility of the SPL100 for the complete development of a forest inventory over large forest areas, from ground plot establishment through to the production of forest attribute estimates.},
  langid = {english}
}

@article{queinnecMappingDominantBoreal2023,
  title = {Mapping {{Dominant Boreal Tree Species Groups}} by {{Combining Area-Based}} and {{Individual Tree Crown LiDAR Metrics}} with {{Sentinel-2 Data}}},
  author = {Queinnec, Martin and Coops, Nicholas C. and White, Joanne C. and Griess, Verena C. and Schwartz, Naomi B. and McCartney, Grant},
  year = {2023},
  month = jan,
  journal = {Canadian Journal of Remote Sensing},
  volume = {49},
  number = {1},
  pages = {2130742},
  issn = {0703-8992, 1712-7971},
  doi = {10.1080/07038992.2022.2130742},
  urldate = {2023-07-13},
  langid = {english}
}

@article{riofrioHarmonizingMultitemporalAirborne2022,
  title = {Harmonizing Multi-Temporal Airborne Laser Scanning Point Clouds to Derive Periodic Annual Height Increments in Temperate Mixedwood Forests},
  author = {Riofr{\'i}o, Jos{\'e} and White, Joanne C. and Tompalski, Piotr and Coops, Nicholas C. and Wulder, Michael A.},
  year = {2022},
  month = oct,
  journal = {Canadian Journal of Forest Research},
  volume = {52},
  number = {10},
  pages = {1334--1352},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2022-0055},
  urldate = {2023-02-15},
  abstract = {When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compromised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005--2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures ( R               2               ~{$\geq~$}0.88), we found a weak relationship between the field- and ALS-derived PAI ( R               2               ~=~0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.},
  langid = {english}
}

@article{riofrioModellingHeightGrowth2023,
  title = {Modelling Height Growth of Temperate Mixedwood Forests Using an Age-Independent Approach and Multi-Temporal Airborne Laser Scanning Data},
  author = {Riofr{\'i}o, Jos{\'e} and White, Joanne C. and Tompalski, Piotr and Coops, Nicholas C. and Wulder, Michael A.},
  year = {2023},
  month = sep,
  journal = {Forest Ecology and Management},
  volume = {543},
  pages = {121137},
  issn = {03781127},
  doi = {10.1016/j.foreco.2023.121137},
  urldate = {2023-06-02},
  langid = {english}
}

@article{rochaCrownLevelStructureFuel2023,
  title = {Crown-{{Level Structure}} and {{Fuel Load Characterization}} from {{Airborne}} and {{Terrestrial Laser Scanning}} in a {{Longleaf Pine}} ({{Pinus}} Palustris {{Mill}}.) {{Forest Ecosystem}}},
  author = {Rocha, Kleydson Diego and Silva, Carlos Alberto and Cosenza, Diogo N. and Mohan, Midhun and Klauberg, Carine and Schlickmann, Monique Bohora and Xia, Jinyi and Leite, Rodrigo V. and de Almeida, Danilo Roberti Alves and Atkins, Jeff W. and Cardil, Adrian and Rowell, Eric and Parsons, Russ and {S{\'a}nchez-L{\'o}pez}, Nuria and Prichard, Susan J. and Hudak, Andrew T.},
  year = {2023},
  month = feb,
  journal = {Remote Sensing},
  volume = {15},
  number = {4},
  pages = {1002},
  issn = {2072-4292},
  doi = {10.3390/rs15041002},
  urldate = {2023-02-15},
  abstract = {Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from -4 to 3\%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE\% between 4.56 and 50.99\%), and low biases (between 0.03\% and -2.86\%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83\%, TLS = 7.22\%, and ALS + TLS = 4.56\%). All models and datasets had similar accuracies in terms of bias ({$<$}2.0\%), except for CB in ALS (-2.53\%) and ALS + TLS (-2.86\%), and SB in ALS + TLS data (-2.22\%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.},
  langid = {english}
}

@article{rodriguez-vivancosAnalysisStructureMotion2022,
  title = {Analysis of Structure from Motion and Airborne Laser Scanning Features for the Evaluation of Forest Structure},
  author = {{Rodr{\'i}guez-Vivancos}, Alejandro and Manzanera, Jos{\'e} Antonio and {Mart{\'i}n-Fern{\'a}ndez}, Susana and {Garc{\'i}a-Cimarras}, Alba and {Garc{\'i}a-Abril}, Antonio},
  year = {2022},
  month = jun,
  journal = {European Journal of Forest Research},
  volume = {141},
  number = {3},
  pages = {447--465},
  issn = {1612-4669, 1612-4677},
  doi = {10.1007/s10342-022-01447-7},
  urldate = {2023-02-15},
  abstract = {Abstract                            Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5--6.5 returns m               -2               ) and the sampling intensity (0.3--3.4\%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r\,=\,18~m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable.},
  langid = {english}
}

@article{rousselCorrectionUpdateEnhancement2022,
  title = {Correction, Update, and Enhancement of Vectorial Forestry Road Maps Using {{ALS}} Data, a Pathfinder, and Seven Metrics},
  author = {Roussel, Jean-Romain and Bourdon, Jean-Fran{\c c}ois and Morley, Ilythia D. and Coops, Nicholas C. and Achim, Alexis},
  year = {2022},
  month = nov,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {114},
  pages = {103020},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.103020},
  urldate = {2023-02-15},
  langid = {english}
}

@article{rousselLidRPackageAnalysis2020,
  title = {{{lidR}}: {{An R}} Package for Analysis of {{Airborne Laser Scanning}} ({{ALS}}) Data},
  shorttitle = {{{lidR}}},
  author = {Roussel, Jean-Romain and Auty, David and Coops, Nicholas C. and Tompalski, Piotr and Goodbody, Tristan R.H. and Meador, Andrew S{\'a}nchez and Bourdon, Jean-Fran{\c c}ois and {de Boissieu}, Florian and Achim, Alexis},
  year = {2020},
  month = dec,
  journal = {Remote Sensing of Environment},
  volume = {251},
  pages = {112061},
  issn = {00344257},
  doi = {10.1016/j.rse.2020.112061},
  urldate = {2023-02-16},
  langid = {english}
}

@article{rousselVectorialTopologicallyValid2023,
  title = {Vectorial and Topologically Valid Segmentation of Forestry Road Networks from {{ALS}} Data},
  author = {Roussel, Jean-Romain and Bourdon, Jean-Fran{\c c}ois and Morley, Ilythia D. and Coops, Nicholas C. and Achim, Alexis},
  year = {2023},
  month = apr,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {118},
  pages = {103267},
  issn = {15698432},
  doi = {10.1016/j.jag.2023.103267},
  urldate = {2023-04-20},
  langid = {english}
}

@article{saarelaMappingAbovegroundBiomass2020,
  title = {Mapping Aboveground Biomass and Its Prediction Uncertainty Using {{LiDAR}} and Field Data, Accounting for Tree-Level Allometric and {{LiDAR}} Model Errors},
  author = {Saarela, Svetlana and W{\"a}stlund, Andr{\'e} and Holmstr{\"o}m, Emma and Mensah, Alex Appiah and Holm, S{\"o}ren and Nilsson, Mats and Fridman, Jonas and St{\aa}hl, G{\"o}ran},
  year = {2020},
  month = dec,
  journal = {Forest Ecosystems},
  volume = {7},
  number = {1},
  pages = {43},
  issn = {2197-5620},
  doi = {10.1186/s40663-020-00245-0},
  urldate = {2023-04-11},
  abstract = {Abstract                            Background               The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.                                         Results                                Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km                 2                 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m {\texttimes}18 m map units was found to range between 9 and 447 Mg {$\cdot$}                 ha                 -1                 . The corresponding root mean square errors ranged between 10 and 162 Mg {$\cdot$}                 ha                 -1                 . For the entire study region, the mean aboveground biomass was 55 Mg {$\cdot$}                 ha                 -1                 and the corresponding relative root mean square error 8\%. At this level 75\% of the mean square error was due to the uncertainty associated with tree-level models.                                                        Conclusions               Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.},
  langid = {english}
}

@article{schaferAssessingPotentialSynthetic2023,
  title = {Assessing the Potential of Synthetic and {\emph{Ex Situ}} Airborne Laser Scanning and Ground Plot Data to Train Forest Biomass Models},
  author = {Sch{\"a}fer, Jannika and Winiwarter, Lukas and Weiser, Hannah and Novotn{\'y}, Jan and H{\"o}fle, Bernhard and Schmidtlein, Sebastian and Henniger, Hans and Krok, Grzegorz and Stere{\'n}czak, Krzysztof and Fassnacht, Fabian Ewald},
  editor = {Lam, Tzeng Yih},
  year = {2023},
  month = dec,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpad061},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad061},
  urldate = {2023-12-06},
  abstract = {Abstract             Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r\${\textasciicircum}\{2\}\$), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52--122 \%, r\${\textasciicircum}\{2\}\$ decrease of 4--18 \%). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21--118 \%, r\${\textasciicircum}\{2\}\$ decrease of 5--14 \% compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r\${\textasciicircum}\{2\}\$, as long as no more than 12--346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r\${\textasciicircum}\{2\}\$ were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available.},
  langid = {english}
}

@article{schaferGeneratingSyntheticLaser2023,
  title = {Generating Synthetic Laser Scanning Data of Forests by Combining Forest Inventory Information, a Tree Point Cloud Database and an Open-Source Laser Scanning Simulator},
  author = {Sch{\"a}fer, Jannika and Weiser, Hannah and Winiwarter, Lukas and H{\"o}fle, Bernhard and Schmidtlein, Sebastian and Fassnacht, Fabian Ewald},
  year = {2023},
  month = apr,
  journal = {Forestry: An International Journal of Forest Research},
  pages = {cpad006},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpad006},
  urldate = {2023-05-24},
  abstract = {Abstract             Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was -- for most of the study sites -- better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.},
  langid = {english}
}

@article{schneiderMappingFunctionalDiversity2017,
  title = {Mapping Functional Diversity from Remotely Sensed Morphological and Physiological Forest Traits},
  author = {Schneider, Fabian D. and Morsdorf, Felix and Schmid, Bernhard and Petchey, Owen L. and Hueni, Andreas and Schimel, David S. and Schaepman, Michael E.},
  year = {2017},
  month = nov,
  journal = {Nature Communications},
  volume = {8},
  number = {1},
  pages = {1441},
  issn = {2041-1723},
  doi = {10.1038/s41467-017-01530-3},
  urldate = {2023-05-17},
  abstract = {Abstract             Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity--ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology.},
  langid = {english}
}

@article{seelyModellingTreeBiomass2023,
  title = {Modelling Tree Biomass Using Direct and Additive Methods with Point Cloud Deep Learning in a Temperate Mixed Forest},
  author = {Seely, Harry and Coops, Nicholas C. and White, Joanne C. and Montw{\'e}, David and Winiwarter, Lukas and Ragab, Ahmed},
  year = {2023},
  month = dec,
  journal = {Science of Remote Sensing},
  volume = {8},
  pages = {100110},
  issn = {26660172},
  doi = {10.1016/j.srs.2023.100110},
  urldate = {2023-12-06},
  langid = {english}
}

@article{shokirovHabitatHighsLows2023,
  title = {Habitat Highs and Lows: {{Using}} Terrestrial and {{UAV LiDAR}} for Modelling Avian Species Richness and Abundance in a Restored Woodland},
  shorttitle = {Habitat Highs and Lows},
  author = {Shokirov, Shukhrat and Jucker, Tommaso and Levick, Shaun R. and Manning, Adrian D. and Bonnet, Timothee and Yebra, Marta and Youngentob, Kara N.},
  year = {2023},
  month = feb,
  journal = {Remote Sensing of Environment},
  volume = {285},
  pages = {113326},
  issn = {00344257},
  doi = {10.1016/j.rse.2022.113326},
  urldate = {2023-03-20},
  langid = {english}
}

@article{silvaForestGapRPackageForest2019,
  title = {{{ForestGapR}}: {{An}} r {{Package}} for Forest Gap Analysis from Canopy Height Models},
  shorttitle = {F},
  author = {Silva, Carlos A. and Valbuena, Ruben and Pinag{\'e}, Ekena R. and Mohan, Midhun and Almeida, Danilo R. A. and North Broadbent, Eben and Jaafar, Wan Shafrina Wan Mohd and Papa, Daniel and Cardil, Adrian and Klauberg, Carine},
  editor = {Graham, Laura},
  year = {2019},
  month = aug,
  journal = {Methods in Ecology and Evolution},
  volume = {10},
  number = {8},
  pages = {1347--1356},
  issn = {2041-210X, 2041-210X},
  doi = {10.1111/2041-210X.13211},
  urldate = {2023-03-01},
  langid = {english}
}

@article{slavikSpatialAnalysisDense2023,
  title = {Spatial {{Analysis}} of {{Dense LiDAR Point Clouds}} for {{Tree Species Group Classification Using Individual Tree Metrics}}},
  author = {Slav{\'i}k, Martin and Ku{\v z}elka, Karel and Modlinger, Roman and Surov{\'y}, Peter},
  year = {2023},
  month = aug,
  journal = {Forests},
  volume = {14},
  number = {8},
  pages = {1581},
  issn = {1999-4907},
  doi = {10.3390/f14081581},
  urldate = {2023-08-04},
  abstract = {This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark--Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8\% using a GLM approach and 95.1\% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6\% and RF an accuracy of 93.8\%.},
  langid = {english}
}

@article{soininenPredictingGrowthIndividual2022,
  title = {Predicting {{Growth}} of {{Individual Trees Directly}} and {{Indirectly Using}} 20-{{Year Bitemporal Airborne Laser Scanning Point Cloud Data}}},
  author = {Soininen, Valtteri and Kukko, Antero and Yu, Xiaowei and Kaartinen, Harri and Luoma, Ville and Saikkonen, Otto and Holopainen, Markus and Matikainen, Leena and Lehtom{\"a}ki, Matti and Hyypp{\"a}, Juha},
  year = {2022},
  month = nov,
  journal = {Forests},
  volume = {13},
  number = {12},
  pages = {2040},
  issn = {1999-4907},
  doi = {10.3390/f13122040},
  urldate = {2023-02-15},
  abstract = {Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination (R2) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were -0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-\% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling.},
  langid = {english}
}

@article{solbergMappingLAINorway2009,
  title = {Mapping {{LAI}} in a {{Norway}} Spruce Forest Using Airborne Laser Scanning},
  author = {Solberg, Svein and Brunner, Andreas and Hanssen, Kjersti Holt and Lange, Holger and N{\ae}sset, Erik and Rautiainen, Miina and Stenberg, Pauline},
  year = {2009},
  month = nov,
  journal = {Remote Sensing of Environment},
  volume = {113},
  number = {11},
  pages = {2317--2327},
  issn = {00344257},
  doi = {10.1016/j.rse.2009.06.010},
  urldate = {2023-03-09},
  langid = {english}
}

@article{sparksAccuracyLiDARBasedIndividual2021,
  title = {Accuracy of a {{LiDAR-Based Individual Tree Detection}} and {{Attribute Measurement Algorithm Developed}} to {{Inform Forest Products Supply Chain}} and {{Resource Management}}},
  author = {Sparks, Aaron M. and Smith, Alistair M.S.},
  year = {2021},
  month = dec,
  journal = {Forests},
  volume = {13},
  number = {1},
  pages = {3},
  issn = {1999-4907},
  doi = {10.3390/f13010003},
  urldate = {2023-10-20},
  abstract = {Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView{\textregistered} algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. {\textpm}387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer's/user's accuracies {$>$} {\textasciitilde}60\%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.},
  langid = {english}
}

@article{sparksCrossComparisonIndividualTree2022,
  title = {Cross-{{Comparison}} of {{Individual Tree Detection Methods Using Low}} and {{High Pulse Density Airborne Laser Scanning Data}}},
  author = {Sparks, Aaron M. and Corrao, Mark V. and Smith, Alistair M. S.},
  year = {2022},
  month = jul,
  journal = {Remote Sensing},
  volume = {14},
  number = {14},
  pages = {3480},
  issn = {2072-4292},
  doi = {10.3390/rs14143480},
  urldate = {2023-02-15},
  abstract = {Numerous individual tree detection (ITD) methods have been developed for use with airborne laser scanning (ALS) data to provide tree-scale forest inventories across large spatial extents. Despite the growing number of methods, relatively few have been comparatively assessed using a single benchmark forest inventory validation dataset, limiting their operational application. In this study, we assessed seven ITD methods, representing three common approaches (point-cloud-based, raster-based, hybrid), across coniferous forest stands with diverse structure and composition to understand how ITD and height measurement accuracy vary with method, input parameters and data, and stand density. There was little variability in accuracy between the ITD methods where the average F-score and standard deviation ({\textpm}SD) were 0.47 {\textpm} 0.03 using a lower pulse density ALS dataset with an average of 8 pulses per square meter (ppm2) and 0.50 {\textpm} 0.02 using a higher pulse density ALS dataset with an average of 22 ppm2. Using higher ALS pulse density data produced higher ITD accuracies (F-score increase of 10--13\%) in some of the methods versus more modest gains in other methods (F-score increase of 1--3\%). Omission errors were strongly related with stand density and largely consisted of suppressed trees underneath the dominant canopy. Simple canopy height model (CHM)-based methods that utilized fixed-size local maximum filters had the lowest omission errors for trees across all canopy positions. ITD accuracy had large intra-method variation depending on input parameters; however, the highest accuracies were obtained when parameters such as search window size and spacing thresholds were equal to or less than the average crown diameter of trees in the study area. All ITD methods produced height measurements for the detected trees that had low RMSE ({$<$}1.1 m) and bias ({$<$}0.5 m). Overall, the results from this study may help guide end-users with ITD method application and highlight future ITD method improvements.},
  langid = {english}
}

@article{sparksIntegratingActiveFire2023,
  title = {Integrating Active Fire Behavior Observations and Multitemporal Airborne Laser Scanning Data to Quantify Fire Impacts on Tree Growth: {{A}} Pilot Study in Mature {{Pinus}} Ponderosa Stands},
  shorttitle = {Integrating Active Fire Behavior Observations and Multitemporal Airborne Laser Scanning Data to Quantify Fire Impacts on Tree Growth},
  author = {Sparks, Aaron M. and Smith, Alistair M.S. and Hudak, Andrew T. and Corrao, Mark V. and Kremens, Robert L. and Keefe, Robert F.},
  year = {2023},
  month = oct,
  journal = {Forest Ecology and Management},
  volume = {545},
  pages = {121246},
  issn = {03781127},
  doi = {10.1016/j.foreco.2023.121246},
  urldate = {2023-07-11},
  langid = {english}
}

@article{sterenczakMappingIndividualTrees2020,
  title = {Mapping Individual Trees with Airborne Laser Scanning Data in an {{European}} Lowland Forest Using a Self-Calibration Algorithm},
  author = {Stere{\'n}czak, Krzysztof and Kraszewski, Bart{\l}omiej and Mielcarek, Mi{\l}osz and Piasecka, {\.Z}aneta and Lisiewicz, Maciej and Heurich, Marco},
  year = {2020},
  month = dec,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {93},
  pages = {102191},
  issn = {15698432},
  doi = {10.1016/j.jag.2020.102191},
  urldate = {2023-02-15},
  langid = {english}
}

@article{stovallDevelopingNondestructiveSpecies2023,
  title = {Developing Nondestructive Species-specific Tree Allometry with Terrestrial Laser Scanning},
  author = {Stovall, Atticus E. L. and Vorster, Anthony and Anderson, Ryan and Evangelista, Paul},
  year = {2023},
  month = jan,
  journal = {Methods in Ecology and Evolution},
  volume = {14},
  number = {1},
  pages = {280--290},
  issn = {2041-210X, 2041-210X},
  doi = {10.1111/2041-210X.14027},
  urldate = {2023-03-21},
  langid = {english}
}

@article{strakerInstanceSegmentationIndividual2023,
  title = {Instance Segmentation of Individual Tree Crowns with {{YOLOv5}}: {{A}} Comparison of Approaches Using the {{ForInstance}} Benchmark {{LiDAR}} Dataset},
  shorttitle = {Instance Segmentation of Individual Tree Crowns with {{YOLOv5}}},
  author = {Straker, Adrian and Puliti, Stefano and Breidenbach, Johannes and Kleinn, Christoph and Pearse, Grant and Astrup, Rasmus and Magdon, Paul},
  year = {2023},
  month = aug,
  journal = {ISPRS Open Journal of Photogrammetry and Remote Sensing},
  volume = {9},
  pages = {100045},
  issn = {26673932},
  doi = {10.1016/j.ophoto.2023.100045},
  urldate = {2023-11-21},
  langid = {english}
}

@article{strimbuEstimatingBiomassSoil2023,
  title = {Estimating Biomass and Soil Carbon Change at the Level of Forest Stands Using Repeated Forest Surveys Assisted by Airborne Laser Scanner Data},
  author = {Str{\^i}mbu, Victor F. and N{\ae}sset, Erik and {\O}rka, Hans Ole and Liski, Jari and Petersson, Hans and Gobakken, Terje},
  year = {2023},
  month = may,
  journal = {Carbon Balance and Management},
  volume = {18},
  number = {1},
  pages = {10},
  issn = {1750-0680},
  doi = {10.1186/s13021-023-00222-4},
  urldate = {2023-07-24},
  abstract = {Abstract                            Background               Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85\%) is stored in the soil organic (SO) matter. While this very important carbon pool is ``invisible'' to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data.                                         Results                                ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50~km                 2                 ) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha                 -1                 ~yr                 -1                 . The biomass carbon change was 0.405 (0.13) Mg ha                 -1                 ~yr                 -1                 , the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha                 -1                 ~yr                 -1                 , and the change in SO carbon was -~0.01 (0.003) Mg ha                 -1                 ~yr                 -1                 .                                                        Conclusions               Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.},
  langid = {english}
}

@article{strunkLargeAreaForest2019,
  title = {Large {{Area Forest Yield Estimation}} with {{Pushbroom Digital Aerial Photogrammetry}}},
  author = {Strunk, Jacob and Packalen, Petteri and Gould, Peter and Gatziolis, Demetrios and Maki, Caleb and Andersen, Hans-Erik and McGaughey, Robert J.},
  year = {2019},
  month = may,
  journal = {Forests},
  volume = {10},
  number = {5},
  pages = {397},
  issn = {1999-4907},
  doi = {10.3390/f10050397},
  urldate = {2023-02-15},
  abstract = {Low-cost methods to measure forest structure are needed to consistently and repeatedly inventory forest conditions over large areas. In this study we investigate low-cost pushbroom Digital Aerial Photography (DAP) to aid in the estimation of forest volume over large areas in Washington State (USA). We also examine the effects of plot location precision (low versus high) and Digital Terrain Model (DTM) resolution (1 m versus 10 m) on estimation performance. Estimation with DAP and post-stratification with high-precision plot locations and a 1 m DTM was 4 times as efficient (precision per number of plots) as estimation without remote sensing and 3 times as efficient when using low-precision plot locations and a 10 m DTM. These findings can contribute significantly to efforts to consistently estimate and map forest yield across entire states (or equivalent) or even nations. The broad-scale, high-resolution, and high-precision information provided by pushbroom DAP facilitates used by a wide variety of user types such a towns and cities, small private timber owners, fire prevention groups, Non-Governmental Organizations (NGOs), counties, and state and federal organizations.},
  langid = {english}
}

@article{tatsumiForestScannerMobileApplication2022,
  title = {{{ForestScanner}} : {{A}} Mobile Application for Measuring and Mapping Trees with {{LiDAR}}-equipped {{iPhone}} and {{iPad}}},
  author = {Tatsumi, Shinichi and Yamaguchi, Keiji and Furuya, Naoyuki},
  year = {2022},
  month = jun,
  journal = {Methods in Ecology and Evolution},
  pages = {2041-210X.13900},
  issn = {2041-210X, 2041-210X},
  doi = {10.1111/2041-210X.13900},
  urldate = {2023-03-20},
  langid = {english}
}

@article{toivonenAssessingBiodiversityUsing2023,
  title = {Assessing Biodiversity Using Forest Structure Indicators Based on Airborne Laser Scanning Data},
  author = {Toivonen, Janne and Kangas, Annika and Maltamo, Matti and Kukkonen, Mikko and Packalen, Petteri},
  year = {2023},
  month = oct,
  journal = {Forest Ecology and Management},
  volume = {546},
  pages = {121376},
  issn = {03781127},
  doi = {10.1016/j.foreco.2023.121376},
  urldate = {2023-10-30},
  langid = {english}
}

@article{tompalskiCombiningMultiDateAirborne2018,
  title = {Combining {{Multi-Date Airborne Laser Scanning}} and {{Digital Aerial Photogrammetric Data}} for {{Forest Growth}} and {{Yield Modelling}}},
  author = {Tompalski, Piotr and Coops, Nicholas and Marshall, Peter and White, Joanne and Wulder, Michael and Bailey, Todd},
  year = {2018},
  month = feb,
  journal = {Remote Sensing},
  volume = {10},
  number = {3},
  pages = {347},
  issn = {2072-4292},
  doi = {10.3390/rs10020347},
  urldate = {2023-03-06},
  langid = {english}
}

@article{tompalskiDemonstratingTransferabilityForest2019,
  title = {Demonstrating the Transferability of Forest Inventory Attribute Models Derived Using Airborne Laser Scanning Data},
  author = {Tompalski, Piotr and White, Joanne C. and Coops, Nicholas C. and Wulder, Michael A.},
  year = {2019},
  month = jun,
  journal = {Remote Sensing of Environment},
  volume = {227},
  pages = {110--124},
  issn = {00344257},
  doi = {10.1016/j.rse.2019.04.006},
  urldate = {2023-02-24},
  langid = {english}
}

@article{tompalskiEstimatingChangesForest2021,
  title = {Estimating {{Changes}} in {{Forest Attributes}} and {{Enhancing Growth Projections}}: A {{Review}} of {{Existing Approaches}} and {{Future Directions Using Airborne 3D Point Cloud Data}}},
  shorttitle = {Estimating {{Changes}} in {{Forest Attributes}} and {{Enhancing Growth Projections}}},
  author = {Tompalski, Piotr and Coops, Nicholas C. and White, Joanne C. and Goodbody, Tristan R.H. and Hennigar, Chris R. and Wulder, Michael A. and Socha, Jaros{\l}aw and Woods, Murray E.},
  year = {2021},
  month = mar,
  journal = {Current Forestry Reports},
  volume = {7},
  number = {1},
  pages = {1--24},
  issn = {2198-6436},
  doi = {10.1007/s40725-021-00135-w},
  urldate = {2023-02-15},
  abstract = {Abstract                            Purpose of Review               The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure.                                         Recent Findings               Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories--- -- approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies.                                         Summary               To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.},
  langid = {english}
}

@article{tompalskiModelingSiteIndex2022,
  title = {Modeling Site Index of Selected Poplar Clones Using Airborne Laser Scanning Data},
  author = {Tompalski, Piotr and Coops, Nicholas C. and Achim, Alexis and Cosgrove, Cameron F. and Lapointe, Eric and {Brochu-Marier}, Felix},
  year = {2022},
  month = jul,
  journal = {Canadian Journal of Forest Research},
  volume = {52},
  number = {7},
  pages = {1088--1097},
  issn = {0045-5067, 1208-6037},
  doi = {10.1139/cjfr-2021-0257},
  urldate = {2023-03-01},
  abstract = {Accurate growth and yield projection for plantations is critical for evaluating management decisions and anticipating future yields. Development of site index (SI) models is often costly and can be problematic when new, short-rotation species are introduced, for example, hybrid poplar plantations, which are increasingly common due to their very fast growth and high productivity. Airborne laser scanning (ALS) allows accurate measurement of tree and stand height and is increasingly being used to develop top height models. In this paper, we demonstrate an approach to develop SI models from ALS data across hybrid poplar plantations in Quebec, Canada. We exploit a single time step ALS acquisition to generate top height estimates at 10~m grid level. Using existing information on planting date and management practices, we developed top height models for unique classes of fertilization treatment and clone. The generic models for unfertilized and fertilized stands showed good fit statistics, with R               2               of 0.71 and 0.82, respectively. Clone-specific models showed similar goodness of fit, with the best model resulting in an R               2               of 0.89 and relative root mean square error (RMSE) of 16.6\%. Analysis of variance (ANOVA) results showed that clone, fertilization status, and the interaction term between clone and fertilization were significant. Our results confirm the development of top height models from a chronosequence of ALS data was successful and offers a new approach to derive SI models in single-species plantation sites.},
  langid = {english}
}

@article{tompalskiQuantifyingPrecisionForest2021,
  title = {Quantifying the Precision of Forest Stand Height and Canopy Cover Estimates Derived from Air Photo Interpretation},
  author = {Tompalski, Piotr and White, Joanne C and Coops, Nicholas C and Wulder, Michael A and Leboeuf, Antoine and Sinclair, Ian and Butson, Christopher R and Lemonde, Marc-Olivier},
  year = {2021},
  month = oct,
  journal = {Forestry: An International Journal of Forest Research},
  volume = {94},
  number = {5},
  pages = {611--629},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpab022},
  urldate = {2023-02-15},
  abstract = {Abstract             Quality information on forest resources is fundamental for sustainable forest management. Manual aerial photointerpretation is used as a cost-effective source of data for forest inventories; however, the process of photointerpretation is inherently subjective and is often undertaken by multiple photointerpreters for a given forest management area. In contrast, airborne laser scanning (ALS) data enable characterization of forest structure in a systematic fashion with quantifiable levels of accuracy and precision that often exceed required targets and standards. However, the gains associated with the use of new technologies for forest inventory are difficult to measure because the quality of existing photointepreted inventories have rarely been quantified. Using ALS data as reference, the objective of this study was to quantify the precision of photointerpreted estimates of forest stand height and canopy cover (CC). We examined forest inventories from three study sites in three different forest regions of Canada. Each of the study sites was located within a different provincial jurisdiction with unique photointerpretation standards and forest ecosystems. Stand-level estimates of forest height and cover were compared to reference estimates generated from the ALS data. Overall, our results indicated that precision was greater for photointerpreted estimates of height, with a relative standard deviation ranging from 22 per cent to 29 per cent among our three sites, compared to estimates for CC, with precision ranging from 28 per cent to 59 per cent. While the relationship between photointerpreted estimates of height and ALS estimates of height were generally linear and consistent for all study sites, relationships for CC were non-linear. We found that precision for both stand height and cover varied by dominant species, inventory stand structure, age, and ALS canopy complexity, and that in the majority of cases, the differences between the photointerpreted estimate and the ALS estimate were statistically significant. It is also noted that the variability in photointerpretation precision as a function of the aforementioned factors was not consistent among our three study sites, indicating that site-specific forest conditions and photointerpretation procedures influence the precision of photointerpreted estimates. The influence of local forest conditions and interpretation procedures are therefore important considerations when seeking to quantify the potential relative gains in precision, which may be afforded by technologies such as ALS for forest inventory programs. Moreover, approaches to improve consistency in photointerpreted estimates of cover would be useful for operational inventory programs.},
  langid = {english}
}

@article{tompalskiSimulatingImpactsError2014,
  title = {Simulating the Impacts of Error in Species and Height upon Tree Volume Derived from Airborne Laser Scanning Data},
  author = {Tompalski, Piotr and Coops, Nicholas C. and White, Joanne C. and Wulder, Michael A.},
  year = {2014},
  month = sep,
  journal = {Forest Ecology and Management},
  volume = {327},
  pages = {167--177},
  issn = {03781127},
  doi = {10.1016/j.foreco.2014.05.011},
  urldate = {2023-11-21},
  langid = {english}
}

@article{trierTreeSpeciesClassification2018,
  title = {Tree Species Classification in {{Norway}} from Airborne Hyperspectral and Airborne Laser Scanning Data},
  author = {Trier, {\O}ivind Due and Salberg, Arnt-B{\o}rre and Kermit, Martin and Rudjord, {\O}ystein and Gobakken, Terje and N{\ae}sset, Erik and Aarsten, Dagrun},
  year = {2018},
  month = jan,
  journal = {European Journal of Remote Sensing},
  volume = {51},
  number = {1},
  pages = {336--351},
  issn = {2279-7254},
  doi = {10.1080/22797254.2018.1434424},
  urldate = {2023-02-15},
  langid = {english}
}

@article{valbuenaClassificationMultilayeredForest2016,
  title = {Classification of Multilayered Forest Development Classes from Low-Density National Airborne Lidar Datasets},
  author = {Valbuena, Rub{\'e}n and Maltamo, Matti and Packalen, Petteri},
  year = {2016},
  month = aug,
  journal = {Forestry},
  volume = {89},
  number = {4},
  pages = {392--401},
  issn = {0015-752X, 1464-3626},
  doi = {10.1093/forestry/cpw010},
  urldate = {2023-05-11},
  langid = {english}
}

@article{vandewieleMappingSpatialMicroclimate2023,
  title = {Mapping Spatial Microclimate Patterns in Mountain Forests from {{LiDAR}}},
  author = {Vandewiele, Michiel and Geres, Lisa and Lotz, Annette and Mandl, Lisa and Richter, Tobias and Seibold, Sebastian and Seidl, Rupert and Senf, Cornelius},
  year = {2023},
  month = oct,
  journal = {Agricultural and Forest Meteorology},
  volume = {341},
  pages = {109662},
  issn = {01681923},
  doi = {10.1016/j.agrformet.2023.109662},
  urldate = {2023-08-23},
  langid = {english}
}

@article{vanleeuwenAssessmentStandingWood2011,
  title = {Assessment of Standing Wood and Fiber Quality Using Ground and Airborne Laser Scanning: {{A}} Review},
  shorttitle = {Assessment of Standing Wood and Fiber Quality Using Ground and Airborne Laser Scanning},
  author = {{van Leeuwen}, Martin and Hilker, Thomas and Coops, Nicholas C. and Frazer, Gordon and Wulder, Michael A. and Newnham, Glenn J. and Culvenor, Darius S.},
  year = {2011},
  month = may,
  journal = {Forest Ecology and Management},
  volume = {261},
  number = {9},
  pages = {1467--1478},
  issn = {03781127},
  doi = {10.1016/j.foreco.2011.01.032},
  urldate = {2023-02-24},
  langid = {english}
}

@article{vastarantaAirborneLaserScanning2013,
  title = {Airborne Laser Scanning and Digital Stereo Imagery Measures of Forest Structure: Comparative Results and Implications to Forest Mapping and Inventory Update},
  shorttitle = {Airborne Laser Scanning and Digital Stereo Imagery Measures of Forest Structure},
  author = {Vastaranta, Mikko and Wulder, Michael A. and White, Joanne C. and Pekkarinen, Anssi and Tuominen, Sakari and Ginzler, Christian and Kankare, Ville and Holopainen, Markus and Hyypp{\"a}, Juha and Hyypp{\"a}, Hannu},
  year = {2013},
  month = nov,
  journal = {Canadian Journal of Remote Sensing},
  volume = {39},
  number = {5},
  pages = {382--395},
  issn = {0703-8992, 1712-7971},
  doi = {10.5589/m13-046},
  urldate = {2023-03-03},
  langid = {english}
}

@article{verhelstForestEdgeStructure2023,
  title = {Forest Edge Structure from Terrestrial Laser Scanning to Explain Bird Biophony Characteristics from Acoustic Indices},
  author = {Verhelst, Tom E. and Vangansbeke, Pieter and De Frenne, Pieter and D'hont, Barbara and Ponette, Quentin and Willems, Luc and Verbeeck, Hans and Calders, Kim},
  year = {2023},
  month = may,
  journal = {Remote Sensing in Ecology and Conservation},
  pages = {rse2.334},
  issn = {2056-3485, 2056-3485},
  doi = {10.1002/rse2.334},
  urldate = {2023-05-09},
  langid = {english}
}

@article{vincentMultisensorAirborneLidar2023,
  title = {Multi-Sensor Airborne Lidar Requires Intercalibration for Consistent Estimation of Light Attenuation and Plant Area Density},
  author = {Vincent, Gr{\'e}goire and Verley, Philippe and Brede, Benjamin and Delaitre, Guillaume and Maurent, Eliott and Ball, James and Clocher, Ilona and Barbier, Nicolas},
  year = {2023},
  month = mar,
  journal = {Remote Sensing of Environment},
  volume = {286},
  pages = {113442},
  issn = {00344257},
  doi = {10.1016/j.rse.2022.113442},
  urldate = {2023-02-17},
  langid = {english}
}

@article{virtanenNationwidePointCloud2017,
  title = {Nationwide {{Point Cloud}}---{{The Future Topographic Core Data}}},
  author = {Virtanen, Juho-Pekka and Kukko, Antero and Kaartinen, Harri and Jaakkola, Anttoni and Turppa, Tuomas and Hyypp{\"a}, Hannu and Hyypp{\"a}, Juha},
  year = {2017},
  month = aug,
  journal = {ISPRS International Journal of Geo-Information},
  volume = {6},
  number = {8},
  pages = {243},
  issn = {2220-9964},
  doi = {10.3390/ijgi6080243},
  urldate = {2023-02-15},
  langid = {english}
}

@article{wagnerSubMeterTreeHeight2023,
  title = {Sub-{{Meter Tree Height Mapping}} of {{California}} Using {{Aerial Images}} and {{LiDAR-Informed U-Net Model}}},
  author = {Wagner, Fabien H and Roberts, Sophia and Ritz, Alison L and Carter, Griffin and Dalagnol, Ricardo and Favrichon, Samuel and Hirye, Mayumi CM and Brandt, Martin and Ciais, Philipe and Saatchi, Sassan},
  year = {2023},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.2306.01936},
  urldate = {2024-01-04},
  abstract = {Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery (60 cm) from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km\${\textasciicircum}2\$ sites across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered {\textasciitilde} 19.3\% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.},
  copyright = {Creative Commons Attribution 4.0 International},
  keywords = {{FOS: Electrical engineering, electronic engineering, information engineering},92-08,Computer Vision and Pattern Recognition (cs.CV),FOS: Computer and information sciences,I.4.9; I.5.4,Image and Video Processing (eess.IV)}
}

@article{wangFieldmeasuredTreeHeight2019,
  title = {Is Field-Measured Tree Height as Reliable as Believed -- {{A}} Comparison Study of Tree Height Estimates from Field Measurement, Airborne Laser Scanning and Terrestrial Laser Scanning in a Boreal Forest},
  author = {Wang, Yunsheng and Lehtom{\"a}ki, Matti and Liang, Xinlian and Py{\"o}r{\"a}l{\"a}, Jiri and Kukko, Antero and Jaakkola, Anttoni and Liu, Jingbin and Feng, Ziyi and Chen, Ruizhi and Hyypp{\"a}, Juha},
  year = {2019},
  month = jan,
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {147},
  pages = {132--145},
  issn = {09242716},
  doi = {10.1016/j.isprsjprs.2018.11.008},
  urldate = {2023-03-06},
  langid = {english}
}

@article{wastlundForestVariableEstimation2018,
  title = {Forest {{Variable Estimation Using}} a {{High Altitude Single Photon Lidar System}}},
  author = {W{\"a}stlund, Andr{\'e} and Holmgren, Johan and Lindberg, Eva and Olsson, H{\aa}kan},
  year = {2018},
  month = sep,
  journal = {Remote Sensing},
  volume = {10},
  number = {9},
  pages = {1422},
  issn = {2072-4292},
  doi = {10.3390/rs10091422},
  urldate = {2023-03-22},
  abstract = {As part of the digitalization of the forest planning process, 3D remote sensing data is an important data source. However, the demand for more detailed information with high temporal resolution and yet still being cost efficient is a challenging combination for the systems used today. A new lidar technology based on single photon counting has the possibility to meet these needs. The aim of this paper is to evaluate the new single photon lidar sensor Leica SPL100 for area-based forest variable estimations. In this study, it was found that data from the new system, operated from 3800 m above ground level, could be used for raster cell estimates with similar or slightly better accuracy than a linear system, with similar point density, operated from 400 m above ground level. The new single photon counting lidar sensor shows great potential to meet the need for efficient collection of detailed information, due to high altitude, flight speed and pulse repetition rate. Further research is needed to improve the method for extraction of information and to investigate the limitations and drawbacks with the technology. The authors emphasize solar noise filtering in forest environments and the effect of different atmospheric conditions, as interesting subjects for further research.},
  langid = {english}
}

@article{weidenbach3DForstinventurIm2015,
  title = {{{3D Forstinventur}} Im {{Nordschwarzwald}}},
  author = {Weidenbach, Markus},
  year = {2015},
  journal = {AFZ-Der Wald},
  volume = {23},
  pages = {48--52},
  urldate = {2023-02-28}
}

@article{weidenbachErfassungEinzelbaumparameternMit2012,
  title = {{Erfassung von Einzelbaumparametern mit Airborne-Laser-Scanning-Daten}},
  author = {Weidenbach, Markus and Wezyk, Piotr and Tompalski, Piotr and Hoffmann, Karina and Martens, Sven},
  year = {2012},
  journal = {AFZ-Der Wald},
  volume = {21},
  pages = {12--15},
  langid = {ngerman}
}

@misc{weiserhannahTerrestrialUAVborneAirborne2022,
  title = {Terrestrial, {{UAV-borne}}, and Airborne Laser Scanning Point Clouds of Central {{European}} Forest Plots, {{Germany}}, with Extracted Individual Trees and Manual Forest Inventory Measurements},
  author = {Weiser, Hannah and Sch{\"a}fer, Jannika and Winiwarter, Lukas and Kra{\v s}ovec, Nina and Seitz, Christian and Schimka, Marian and Anders, Katharina and Baete, Daria and Braz, Andressa Soarez and Brand, Johannes and Debroize, Denis and Kuss, Paula and Martin, Lioba Lucia and Mayer, Angelo and Schrempp, Torben and {Schwarz, Lisa-Maricia} and Ulrich, Veit and Fassnacht, Fabian E and H{\"o}fle, Bernhard},
  year = {2022},
  pages = {12 data points},
  publisher = {PANGAEA},
  doi = {10.1594/PANGAEA.942856},
  urldate = {2023-02-15},
  abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W{\"u}rttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.},
  copyright = {Creative Commons Attribution Share Alike 4.0 International},
  langid = {english},
  keywords = {3D point clouds,Binary Object,Binary Object (File Size),Central Europe,ecology,Event label,forest,forest inventory,Germany,Lidar,Multiple investigations,Synthetic structural remote sensing data for improved forest inventory models (SYSSIFOSS),tree,vegetation}
}

@misc{weiserhannahTerrestrialUAVborneAirborne2022a,
  title = {Terrestrial, {{UAV-borne}}, and Airborne Laser Scanning Point Clouds of Central {{European}} Forest Plots, {{Germany}}, with Extracted Individual Trees and Manual Forest Inventory Measurements},
  author = {Weiser, Hannah and Sch{\"a}fer, Jannika and Winiwarter, Lukas and Kra{\v s}ovec, Nina and Seitz, Christian and Schimka, Marian and Anders, Katharina and Baete, Daria and Braz, Andressa Soarez and Brand, Johannes and Debroize, Denis and Kuss, Paula and Martin, Lioba Lucia and Mayer, Angelo and Schrempp, Torben and {Schwarz, Lisa-Maricia} and Ulrich, Veit and Fassnacht, Fabian E and H{\"o}fle, Bernhard},
  year = {2022},
  pages = {12 data points},
  publisher = {PANGAEA},
  doi = {10.1594/PANGAEA.942856},
  urldate = {2023-02-15},
  abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W{\"u}rttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.},
  copyright = {Creative Commons Attribution Share Alike 4.0 International},
  langid = {english},
  keywords = {3D point clouds,Binary Object,Binary Object (File Size),Central Europe,ecology,Event label,forest,forest inventory,Germany,Lidar,Multiple investigations,Synthetic structural remote sensing data for improved forest inventory models (SYSSIFOSS),tree,vegetation}
}

@article{weiserIndividualTreePoint2022,
  title = {Individual Tree Point Clouds and Tree Measurements from Multi-Platform Laser Scanning in {{German}} Forests},
  author = {Weiser, Hannah and Sch{\"a}fer, Jannika and Winiwarter, Lukas and Kra{\v s}ovec, Nina and Fassnacht, Fabian E. and H{\"o}fle, Bernhard},
  year = {2022},
  month = jul,
  journal = {Earth System Science Data},
  volume = {14},
  number = {7},
  pages = {2989--3012},
  issn = {1866-3516},
  doi = {10.5194/essd-14-2989-2022},
  urldate = {2023-02-15},
  abstract = {Abstract. Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field. Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction. The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et~al.,~2022a).},
  langid = {english}
}

@article{whiteBestPracticesGuide2013,
  title = {A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach},
  author = {White, Joanne C. and Wulder, Michael A. and Varhola, Andr{\'e}s and Vastaranta, Mikko and Coops, Nicholas C. and Cook, Bruce D. and Pitt, Doug and Woods, Murray},
  year = {2013},
  month = dec,
  journal = {The Forestry Chronicle},
  volume = {89},
  number = {06},
  pages = {722--723},
  issn = {0015-7546, 1499-9315},
  doi = {10.5558/tfc2013-132},
  urldate = {2023-02-24},
  langid = {english}
}

@article{whiteComparisonAirborneLaser2018,
  title = {Comparison of Airborne Laser Scanning and Digital Stereo Imagery for Characterizing Forest Canopy Gaps in Coastal Temperate Rainforests},
  author = {White, Joanne C. and Tompalski, Piotr and Coops, Nicholas C. and Wulder, Michael A.},
  year = {2018},
  month = apr,
  journal = {Remote Sensing of Environment},
  volume = {208},
  pages = {1--14},
  issn = {00344257},
  doi = {10.1016/j.rse.2018.02.002},
  urldate = {2023-12-06},
  langid = {english}
}

@article{whiteEvaluatingCapacitySingle2021,
  title = {Evaluating the Capacity of Single Photon Lidar for Terrain Characterization under a Range of Forest Conditions},
  author = {White, J.C. and Woods, M. and Krahn, T. and Papasodoro, C. and B{\'e}langer, D. and Onafrychuk, C. and Sinclair, I.},
  year = {2021},
  month = jan,
  journal = {Remote Sensing of Environment},
  volume = {252},
  pages = {112169},
  issn = {00344257},
  doi = {10.1016/j.rse.2020.112169},
  urldate = {2023-02-15},
  langid = {english}
}

@article{whiteModelDevelopmentApplication2017,
  title = {A Model Development and Application Guide for Generating an Enhanced Forest Inventory Using Airborne Laser Scanning Data and an Area-Based Approach},
  author = {White, Joanne C. and Tompalski, Piotr and Vastaranta, Mikko and Wulder, Michael and Saarinen, Ninni and Stepper, Christoph and Coops, Nicholas},
  year = {2017},
  publisher = {13504},
  doi = {10.13140/RG.2.2.26770.96964},
  urldate = {2023-05-23},
  langid = {english}
}

@article{whiteRemoteSensingTechnologies2016,
  title = {Remote {{Sensing Technologies}} for {{Enhancing Forest Inventories}}: {{A Review}}},
  shorttitle = {Remote {{Sensing Technologies}} for {{Enhancing Forest Inventories}}},
  author = {White, Joanne C. and Coops, Nicholas C. and Wulder, Michael A. and Vastaranta, Mikko and Hilker, Thomas and Tompalski, Piotr},
  year = {2016},
  month = sep,
  journal = {Canadian Journal of Remote Sensing},
  volume = {42},
  number = {5},
  pages = {619--641},
  issn = {0703-8992, 1712-7971},
  doi = {10.1080/07038992.2016.1207484},
  urldate = {2023-02-24},
  langid = {english}
}

@article{whiteUtilityImageBasedPoint2013,
  title = {The {{Utility}} of {{Image-Based Point Clouds}} for {{Forest Inventory}}: {{A Comparison}} with {{Airborne Laser Scanning}}},
  shorttitle = {The {{Utility}} of {{Image-Based Point Clouds}} for {{Forest Inventory}}},
  author = {White, Joanne and Wulder, Michael and Vastaranta, Mikko and Coops, Nicholas and Pitt, Doug and Woods, Murray},
  year = {2013},
  month = jun,
  journal = {Forests},
  volume = {4},
  number = {3},
  pages = {518--536},
  issn = {1999-4907},
  doi = {10.3390/f4030518},
  urldate = {2023-02-24},
  langid = {english}
}

@article{yrttimaaCapturingSeasonalRadial2023,
  title = {Capturing Seasonal Radial Growth of Boreal Trees with Terrestrial Laser Scanning},
  author = {Yrttimaa, T. and Junttila, S. and Luoma, V. and Calders, K. and Kankare, V. and Saarinen, N. and Kukko, A. and Holopainen, M. and Hyypp{\"a}, J. and Vastaranta, M.},
  year = {2023},
  month = feb,
  journal = {Forest Ecology and Management},
  volume = {529},
  pages = {120733},
  issn = {03781127},
  doi = {10.1016/j.foreco.2022.120733},
  urldate = {2023-02-15},
  langid = {english}
}

@article{yunIndividualTreeCrown2021,
  title = {Individual Tree Crown Segmentation from Airborne {{LiDAR}} Data Using a Novel {{Gaussian}} Filter and Energy Function Minimization-Based Approach},
  author = {Yun, Ting and Jiang, Kang and Li, Guangchao and Eichhorn, Markus P. and Fan, Jiangchuan and Liu, Fangzhou and Chen, Bangqian and An, Feng and Cao, Lin},
  year = {2021},
  month = apr,
  journal = {Remote Sensing of Environment},
  volume = {256},
  pages = {112307},
  issn = {00344257},
  doi = {10.1016/j.rse.2021.112307},
  urldate = {2023-03-20},
  langid = {english}
}

@phdthesis{zeinerEinsatzAirborneLaserscanning2012,
  title = {{Der Einsatz von Airborne Laserscanning Daten im forstbetrieblichen Informationssystem}},
  author = {Zeiner, Robert},
  year = {2012},
  journal = {Der Einsatz von Airborne Laserscanning Daten im forstbetrieblichen Informationssystem},
  urldate = {2023-02-28},
  abstract = {ger: Kenntnisse {\"u}ber die Verh{\"a}ltnisse wichtiger Bestandesparameter, wie den Holzvorrat, stellen die Grundlage strategischer und operativer {\"U}berlegungen eines Forstbetriebes dar. Die daf{\"u}r ben{\"o}tigten Daten werden traditionell {\"u}ber zeit- und kostenintensive Stichprobeninventuren und Taxationen erhoben. H{\"a}ufig auftretende Wind- und darauf folgende K{\"a}ferkalamit{\"a}ten erfordern oftmals eine kurzfristige Aktualisierung der aufgenommenen Daten. Vor diesem Hintergrund sind Verfahren, welche eine rasche, kosteng{\"u}nstige und gro{\ss}fl{\"a}chige Datenerhebung erm{\"o}glichen, von besonderem Interesse. Die positiven Berichte {\"u}ber die Airborne Laserscanning (ALS) Technologie aus dem skandinavischen Raum legen nahe, die Anwendungsm{\"o}glichkeiten von ALS in den vergleichsweise heterogenen W{\"a}ldern Mitteleuropas zu untersuchen. Die Arbeit besch{\"a}ftigt sich aus {\"o}sterreichischer Sicht mit den Einsatzm{\"o}glichkeiten von ALS im forstbetrieblichen Informationssystem im Allgemeinen und in der Forsteinrichtung im Besonderen. Dazu wird anhand einer Literaturanalyse aufgezeigt, welche Prim{\"a}rdaten aus ALS gewonnen werden k{\"o}nnen und welche f{\"u}r den Forsteinrichtungsprozess relevanten Standorts- und Bestandesparameter direkt daraus abgeleitet oder durch {\"A}quivalente angen{\"a}hert werden k{\"o}nnen. In weiterer Folge werden Faktoren behandelt, die f{\"u}r eine etwaige Implementation dieser Technologie in den Betriebsalltag ausschlaggebend sind. Anhand des Analyserahmens kann insbesondere gezeigt werden, dass Parameter, die nahe den Prim{\"a}rdaten sind, eine hohe Genauigkeit aufweisen, w{\"a}hrend abgeleitete Parameter wie der Holzvorrat deutlich st{\"a}rkere Abweichungen von terrestrisch erhobenen Daten aufweisen. Insgesamt kann festgestellt werden, dass sich besonders ALS-gest{\"u}tzte Gel{\"a}ndemodelle zur Verwendung in der betrieblichen Praxis eignen, w{\"a}hrend bei den meisten abgeleiteten Gr{\"o}{\ss}en noch Forschungsbedarf besteht. Vor allem das Bestandesalter und darauf aufbauend die Bonit{\"a}t lassen sich mit Hilfe von ALS nur unzureichend erschlie{\ss}en. Der Gang in den Wald wird daher auch in Zukunft nicht entbehrlich., eng: Information about stand parameters such as the growing stock is of utmost importance for the strategic as well as the operational planning of a forest enterprise. Typically, such data are derived from forest inventories. Standard sampling techniques are quite expensive and time consuming, however. Frequent wind throws and subsequent beetle damage necessitate a timely update of respective data. Hence, technologies allowing for a quick collection of the data at acceptable cost are of high interest. Airborne Laserscanning (ALS) Technology has been introduced successfully in Scandinavian countries for capturing various forestry features. The thesis investigates the possibilities of applying ALS under Austrian conditions. The question is to what extent ALS could be integrated into the information system of a forest enterprise and forest management planning schemes respectively. A literature review reveals, what kind of primary data can be acquired by means of ALS and which parameters of the terrain and forest stands can be derived. In a further chapter, several factors which are crucial for the implementation of this technology into practical forest management are investigated. Especially the terrain models and several parameters such as tree height, achieve a high level of accuracy. Other parameters of interest which have to be derived by means of various algorithms, such as the growing stock, stand age or yield class, show a greater deviation from reference data. ALS may assist terrestrial inventory procedures but cannot fully replace them.},
  langid = {german},
  lccn = {D-15547},
  keywords = {Forstwirtschaft,Laserscanner,Luftbildauswertung}
}

@article{zhangImprovedAreabasedApproach2023,
  title = {An Improved Area-Based Approach for Estimating Plot-Level Tree {{DBH}} from Airborne {{LiDAR}} Data},
  author = {Zhang, Zhengnan and Wang, Tiejun and Skidmore, Andrew K. and Cao, Fuliang and She, Guanghui and Cao, Lin},
  year = {2023},
  journal = {Forest Ecosystems},
  volume = {10},
  pages = {100089},
  issn = {21975620},
  doi = {10.1016/j.fecs.2023.100089},
  urldate = {2023-02-15},
  langid = {english}
}

@article{zweifelNetworkingForestInfrastructure2023,
  title = {Networking the Forest Infrastructure towards near Real-Time Monitoring -- {{A}} White Paper},
  author = {Zweifel, Roman and Pappas, Christoforos and Peters, Richard L. and Babst, Flurin and Balanzategui, Daniel and Basler, David and Bastos, Ana and Beloiu, Mirela and Buchmann, Nina and Bose, Arun K. and Braun, Sabine and Damm, Alexander and D'Odorico, Petra and Eitel, Jan U.H. and Etzold, Sophia and Fonti, Patrick and Rouholahnejad Freund, Elham and Gessler, Arthur and Haeni, Matthias and Hoch, G{\"u}nter and Kahmen, Ansgar and K{\"o}rner, Christian and Krejza, Jan and Krumm, Frank and Leuchner, Michael and Leuschner, Christoph and Lukovic, Mirko and {Mart{\'i}nez-Vilalta}, Jordi and Matula, Radim and Meesenburg, Henning and Meir, Patrick and Plichta, Roman and Poyatos, Rafael and Rohner, Brigitte and Ruehr, Nadine and Salom{\'o}n, Roberto L. and Scharnweber, Tobias and Schaub, Marcus and Steger, David N. and Steppe, Kathy and Still, Christopher and Stojanovi{\'c}, Marko and Trotsiuk, Volodymyr and Vitasse, Yann and {von Arx}, Georg and Wilmking, Martin and Zahnd, Cedric and Sterck, Frank},
  year = {2023},
  month = may,
  journal = {Science of The Total Environment},
  volume = {872},
  pages = {162167},
  issn = {00489697},
  doi = {10.1016/j.scitotenv.2023.162167},
  urldate = {2023-02-16},
  langid = {english}
}