基于双向地形阴影法的黄土侵蚀沟自动提取技术

沟蚀严重危害着土地资源和生态环境，研究高分辨率数字高程模型（Digital Elevation Model，DEM）的侵蚀沟自动提取技术进行侵蚀沟动态监测，以期望能替代目视解译提取。双向地形阴影法是一种兼顾提取精度和效率的侵蚀沟提取方法，但仅针对黄土台/塬地貌，在其他地貌下其提取结果会产生较多误提区和漏提区。为使其适应不同地貌，该研究以陕西吴堡县3.2 m DEM为试验数据，综合沟谷线缓冲区填充法、膨胀腐蚀法以及面积阈值法消除误提区和漏提区，并通过模块化编程及数据分块计算，实现侵蚀沟自动提取，最后在全县范围内均匀选取10个小流域为样本，使用0.65 m影像目视解译的结果进行精度验证。结果表明：1）实现了误提区和漏提区的自动消除，得到了吴堡县侵蚀沟分布图；2）在10个验证小流域中，该研究方法提取侵蚀沟的精度为81.1%～86.3%，平均精度为83.8%。该研究综合应用沟谷线缓冲区填充法、膨胀腐蚀法和面积阈值法消除误提区和漏提区，使该方法能适应非黄土台/塬地貌，并在此基础上研发了能实现大区域侵蚀沟提取的算法及软件。

Automatic extraction technique for the erosion gully in the Loess Plateau based on bidirectional relief-shading method

Gully erosion has been one of the most important reasons for the land degradation in the Loess Plateau. There is a serious threat to the local infrastructure, grain security, and ecological environment. Therefore, it is very necessary to dynamic monitor the erosion gully. However, the traditional methods are very unfavorable for the Loess Plateau with a large area, such as manual measurement and image visual interpretation in this case. In this study, automatic extraction of erosion gully was proposed for the large-region and high-resolution Digital Elevation Model (DEM). Bidirectional Relief-shading served as an excellent erosion gully extraction for the Loess platform/tableland landform using shaded relief, indicating the balanced accuracy and efficiency. The characteristics of outstanding relief shade were used to extract the erosion gully in the area under the simulated light. But there were more false and missing areas in other landforms that caused by special terrains, such as steep slopes and wide flat gully bottom, which was greatly damaged the correctness and continuity of the extraction. The special terrain was first summarized for the false and missing areas in the other landforms. Taking the 3.2 m GF-7 DEM of Wubu County, Shaanxi Province, China, as the research object, the gully networks buffer filling, dilate-erode, and area threshold were applied to eliminate the false and missing areas, and then the erosion gully extraction algorithms were designed for the large-region and high-resolution grid DEM using object-oriented, data partition, image processing, and GIS technology. The 10 small watersheds with an area of 3-6 km2 were evenly selected to verify the extraction accuracy in the study area, and then the validation data was obtained by the visual interpretation with a 0.65 m GF-7 image. The results show that: 1) The false and missing areas were automatically eliminated to ensure the correctness and continuity of the extraction, where the erosion gully map of Wubu County was obtained; 2) In 10 verified watersheds, the extraction was consistent with the reference in general, but the references were better in detail, and the accuracy of the extraction was 81.1%-86.3%, with an average of 83.8%. Therefore, the extraction was supported by Python language, Geospatial Data Abstraction Library (GDAL), OpenCV library, PostGIS database, and Windows 10 operating system. The erosion gully extraction and software were developed for the large-region and high-resolution DEM data with DEM partition, initial extraction, error area, and missing area elimination and splicing as the core in the dynamic monitoring of erosion gully.