The prediction of cropland gully length density in the Songnen black soil region of Northeast China

We built our model using Random Forest algorithm based on high quality gully density samples in 853 watersheds and 37 regional distributed factors. The samples were obtained through visual interpretation of sub-meter imagery and validated in 55 watersheds using centimeter-resolution UAV imagery and field surveys. Then the model was applied to 85,516 sub-watersheds covering the whole domain. Our model demonstrated good predictive accuracy at regional scale, with a Nash-Sutcliffe Efficiency (NSE) of 0.6 for gully density prediction.本研究基于853个流域的高质量沟谷密度样本与37项区域分布因子，采用随机森林（Random Forest）算法构建模型。样本通过亚米级影像目视解译获取，并在55个流域中采用厘米级分辨率无人机（UAV）影像与野外调查进行验证。随后将该模型应用于覆盖整个研究区域的85,516个子流域。本模型在区域尺度上展现出良好的预测精度，其沟谷密度预测的纳什-萨克利夫效率系数（NSE）达0.6。