Object-oriented Extraction and Spatial Heterogeneity Analysis of Loess Collapse-pits Using UAV Remote Sensing and Machine Learning

Abstract:

Loess collapse-pits, as key triggers of soil erosion in China??s Loess Plateau, require accurate identification for effective land degradation control. An integrated framework combining UAV remote sensing with object-oriented image analysis (OBIA) and machine learning (KNN and CART algorithms) was developed to map and analyze loess collapse-pits in the Zhoutungou Basin. Key findings included optimized multi-scale segmentation ( scale was 30 ) with KNN classifier achieved superior accuracy (Kappa coefficient was 0. 896, extraction quality was 82. 6% ). Compared with the CART algorithm, the Kappa coefficient increased by 0. 069, and the extraction quality improved by 1. 9% . Morphological parameter inversion reveals advantages in capturing linear features, with standard errors for the major axis (R2 = 0. 773) and perimeter (R2 = 0. 842) lower than 1. 1 m and 4. 7 m, respectively. Spatial analysis revealed 453 collapse-pits showing distinct topographic preferences: 84. 99% occurred on slopes greater than 15° with density increased 1. 8-fold per 10° slope increment ( R2 = 0. 91), semi-shaded slopes hosted 2. 76 times more pits (5. 8 pits/ km2 ) than sunny slopes, and 71. 1% concentrated at mid- elevations (1 125 ~ 1 210 m). Morphometric analysis indicated 75% of pits were within 9. 02 m2, with elongated shapes ( aspect ratio was 2. 05 ± 1. 13) suggesting lateral erosion dominance, while depth exhibited bimodal distribution reflecting different formation mechanisms. The research established the first 3D morphometric threshold system for loess collapse-pits, providing critical data for soil conservation and geohazard prevention.