Comparative Assessment Using Different Topographic Change Detection Algorithms for Gravity Erosion Quantification Based on Multi-Source Remote Sensing Data

Gravity erosion is one of the main physical processes of soil erosion and sediment sources in catchments, and its spatiotemporal patterns and driving mechanisms are seriously understudied, mainly due to the the great difficulties in monitoring and quantifying. This study obtained gravity erosion amounts by runoff scouring experiments on the field slope of the hilly–gully region of the Chinese Loess Plateau. The terrain point cloud before and after gravity erosion was obtained based on the TLS, SfM and the fusion of single-scan TLS and SfM, and then the gravity erosion was estimated by four terrain change detection algorithms (DoD, C2C, C2M and M3C2). Results showed that the M3C2 algorithm plus fused data had the highest quantization accuracy among all the algorithms and data sources, with a relative error of 14.71%. The fused data combined with M3C2 algorithm performed much better than other algorithms and data sources for the different gravity erosion magnitudes (mean relative error < 17.00%). The DoD algorithm plus TLS data were preferable for collapse areas, while the M3C2 algorithm plus TLS was suitable for the alcove area. This study provides a useful reference for the monitor and quantitative research of gravity erosion in complex topographic areas.