Comparative assessment of satellite and unmanned aerial vehicles data for landslide susceptibility mapping

Remote sensing methods enable the rapid study of large, hard-to-reach areas prone to hazardous gravitational geological processes (landslides, rockfalls). Regional mapping of high-risk landslide areas based on satellite remote sensing data is already well-developed. In recent decades, the theory and practice of using unmanned aerial vehicle (UAV) technologies for remote sensing have rapidly evolved. In both cases, multispectral survey data and digital elevation models are used to analyze geological risks. The purpose of the presented research is to compare the results of landslide susceptibility mapping based on available satellite data and unmanned aerial vehicles data and to identify the advantages and limitations of both methods. The key predictors for satellite data (slope angles, profile curvature, normalized difference vegetation index, wetness index and length-slope factor) were derived from ALOS AW3D30 digital evaluation models and Sentinel-2 data. Unmanned aerial vehicle data were obtained using a photogrammetric method with multispectral cameras. To determine the weights of factors, the analytical hierarchy process was used through pairwise comparisons. Landslide susceptibility maps were generated for the same area using QGIS. The comparative analysis has clearly demonstrated how differences in input data resolution and survey methodology impact the predictive value of the results. The major finding is that due to resolution generalization (in the case of satellite imagery) small, unstable rock blocks can artificially increase the vulnerability of adjacent slopes, on the other hand, some small, but hazardous rocks can remain completely unnoticed. At the same time, it is shown that high-resolution data from unmanned aerial vehicles do not replace satellite remote sensing data, but rather complement it. The methods serve different spatial scales and research objectives. The results confirm that satellite data and data from unmanned aerial vehicles should be complementary. Satellite data are suitable for regional landslide susceptibility mapping, while data from unmanned aerial vehicles are essential for detailed studies of individual areas identified using satellite data. Therefore, a hybrid methodology is recommended: satellite data for the initial zoning of hazardous areas, and data from unmanned aerial vehicles for the detailed study of hazardous areas.

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