The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure

Dataset and Python code complementing the publication Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. Remote Sens. 2022, 14, 6107. https://doi.org/10.3390/rs14236107 AROSICS.zip contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m]. CC_DistancePointClouds.zip contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation. CC_PointCloudProcessing.zip contains the point clouds at post-processing levels II-IV. ODM_Orthomosaics.zip contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap). ODM_PointClouds.zip contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap). PointCloudStatistics.zip contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets. Python_ChangeDetection.zip contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is CC_DistancePointClouds.zip. Subsets.zip contains shapefiles of the two subsets.