基于无人机遥感与面向对象分类的黄土陷穴提取及其空间异质性解析

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

摘要:

黄土陷穴作为黄土高原土壤侵蚀的关键触发因子,其精准识别对水土流失治理具有重要意义。提出了融合无人机遥感、面向对象分类与机器学习中的K最近邻(KNN)和分类与回归树(CART)算法的协同解译框架,在周屯沟流域实现陷穴提取与空间异质性解析。结果表明:多尺度分割参数优化显著提升分类效能,分割尺度30下KNN分类器取得最优精度(Kappa系数为0.896,提取质量82.6%),较CART算法Kappa系数提升0.069,提取质量提高1.9%;形态参数反演揭示线性特征捕捉优势,长轴长(R2=0.773)与周长(R2=0.842)标准误差分别低于1.1m与4.7m,满足工程监测需求;陷穴空间分布呈现显著地形耦合性,流域内共识别黄土陷穴453个,其中84.99%黄土陷穴集中于坡度大于15°区域,且坡度每增加10°,陷穴密度提升1.8倍(R2=0.91),半阴坡密度(5.8个/km2)是阳坡的2.76倍,中海拔带(1125~1210m)占比71.1%;形态参数谱系显示75%陷穴面积在9.02m2以内,长宽比(2.05±1.13)呈现陷穴径流冲刷或裂隙扩展驱动的横向扩张特征侵蚀机制,深度呈现冲蚀崩塌双峰分布。

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.