School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 211800, China.
Int J Environ Res Public Health. 2023 Mar 6;20(5):4636. doi: 10.3390/ijerph20054636.
Identifying and extracting check dams is of great significance for soil and water conservation, agricultural management, and ecological assessment. In the Yellow River Basin, the check dam, as a system, generally comprises dam locations and dam-controlled areas. Previous research, however, has focused on dam-controlled areas and has not yet identified all elements of check dam systems. This paper presents a method for automatically identifying check dam systems from digital elevation model (DEM) and remote sensing images. We integrated deep learning and object-based image analysis (OBIA) methods to extract the dam-controlled area's boundaries, and then extracted the location of the check dam using the hydrological analysis method. A case study in the Jiuyuangou watershed shows that the precision and recall of the proposed dam-controlled area extraction approach are 98.56% and 82.40%, respectively, and the F1 score value is 89.76%. The completeness of the extracted dam locations is 94.51%, and the correctness is 80.77%. The results show that the proposed method performs well in identifying check dam systems and can provide important basic data for the analysis of spatial layout optimization and soil and water loss assessment.
识别和提取淤地坝对于水土保持、农业管理和生态评估具有重要意义。在黄河流域，淤地坝系统一般包括坝址和坝控区。然而，以往的研究主要集中在坝控区，尚未识别淤地坝系统的所有要素。本文提出了一种从数字高程模型（DEM）和遥感图像中自动识别淤地坝系统的方法。我们将深度学习和基于对象的图像分析（OBIA）方法相结合，提取了坝控区的边界，然后利用水文分析方法提取了淤地坝的位置。在九元沟流域的案例研究中，所提出的坝控区提取方法的精度和召回率分别为 98.56%和 82.40%，F1 值为 89.76%。提取的坝址的完整性为 94.51%，正确性为 80.77%。结果表明，该方法在识别淤地坝系统方面表现良好，可为空间布局优化和水土流失评估分析提供重要的基础数据。
