Probing into the techniques recognizing potential debris flow formation regions
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摘要: 提前对泥石流可能发生和造成影响的区域进行预测和防范，一直是地质灾害预测中的重要课题。为充分发挥国产高分影像的空间分辨率优势，利用NNDiffuse和Gram-Schmidt两种融合方法实现多光谱和全色波段的融合并作为研究数据，结合常用的支持向量机（SVM）和基于土壤亮度指数特征的动态聚类（ISODATA）两种分类方法对泥石流潜在形成区的自然地表覆盖和人类影响区域进行识别和提取，再利用泥石流隐患沟和集水区的空间和属性关系预测泥石流形成区。研究表明，不同融合方法会对泥石流形成区的预测产生影响，本文基于NNDiffuse融合方法进行预测的总体效果最好；SVM方法有最好的效果，表明先验知识对预测形成区的重要意义，但无先验知识的ISODATA方法结合有效的指数特征在泥石流形成区识别和预测中有较好的表现，预期未来能在测绘部门有很大的应用潜力。Abstract: It is an important task to predict and prevent debris flow and its impacted areas in advance. In this paper, RS and GIS technology were adopted to predict the potential debris flow. Taking advantage of the spatial resolution of domestic high-resolution images, using NNDiffuse and Gram-Schmidt methods to realize the fusion of remote sensing images into research data, combining with support vector machine (SVM) and dynamic clustering based on soil brightness index (ISODATA), the natural surface coverage and human impact area of debris flow formation region are identified and extracted, and then the formation region of debris flow is predicted by using the spatial and attribute relationship of hidden gully and catchment. The experiment shows that different fusion methods will affect the result of debris flow extraction. NNDiffuse fusion method has the best overall effect in this paper; SVM method has the best effect, the prior knowledge has the significance in the prediction of forming region, ISODATA method without prior knowledge has better performance in the identification and prediction of debris flow, there is potential application prospect in the future.
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