The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation

Python code to detect land surface displacements on the basis of distance point clouds derived from UAV.

Prerequisites

Python 3.6+ with the following packages installed: affine, Fiona, GDAL, geopandas, matplotlib, numpy, pandas, rasterio, scikit-image, scikit-learn, Shapely

Distance Point Clouds after Multiscale Model to Model Cloud Comparison (M3C2 after Lague et al., 2013) in CloudCompare in a .csv format

post-processing level I: raw point clouds compared
post-processing level II: denoised point clouds compared
post-processing level III: denoised and Iterative Closest Point (ICP) aligned clouds compared
post-processing level IV: denoised, AROSICS shifted and ICP aligned point clouds compared

The provided python script:

calculates the displacement vectors Dx, Dy, Dz for each distance point cloud
saves the output in a .csv file
rasterizes attribute "vertical displacement (Dz)" of the distance point cloud with highest accuracy (post-processing level IV)
applies a Sobel edge detection filter to highlight high image gradients
applies kMeans algorithm to cluster image into two categories: change (high image gradient) and no change (low image gradient)
clips distance point cloud to areas of change (high image gradient)