High Resolution Remote Sensing for the Identification of Loess Landslides: Example from Yan'an City
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摘要: 中国西部黄土高原地区面积广，滑坡数量多。针对野外调查和目视解译费时、费力、周期较长等问题，利用高空间分辨率卫星遥感数据和DEM，以中国陕西省延安市宝塔区为研究区域，采用面向对象的分类方法，基于波段选择、尺度分析，利用影像分割，结合光谱特征、空间特征和地貌特征进行区域黄土滑坡识别。实验区分析结果表明，滑坡后壁和滑坡体识别精度达78.9%和73.6%，滑坡后壁比滑坡体更易于识别，该方法对研究同类型区域滑坡编目、地学分析和影像理解具有重要的意义。Abstract: The Loess Plateau in West China covers a wide area and has many landslides. Due to problems with field investigations and visual interpretation, such as the time and effort involved, as well as relatively long cycles of data acquisition and update, the high spatial resolution remote sensing data and digital elevation model (DEM) have been used for regional identification of loess landslides in Baota District, Yan'an City, Shaanxi Province, China. The regional loess landslides have been identified by using the object-oriented classification method, this method is to use the image segmentation combined with spectral-spatial and geomorphological features and based on band selection and scale analysis to analyze the landslides. Analysis of the study site showed that the recognition accuracy for the landslide back scarp and landslide mass is 78.9% and 73.6%, respectively; the landslide back scarp is easier to recognize than the landslide mass. This method is important for landslide cataloging, earth science analysis, and image interpretation.
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Keywords:
- loess landslides /
- object-oriented classification /
- set /
- multiple features /
- landslide cataloging
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