基于高分辨率遥感影像的滑坡灾害危险性评价研究
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摘要
本文在借助已有滑坡灾害空间危险性评价方法的基础上,应用高分辨率的航片和多光谱影像、数字地质图,以及数字高程模型等数据,从处理后的遥感影像中提取滑坡灾害分布信息,以此作为滑坡灾害空间危险性评价的本底数据和验证数据;同时研究滑坡灾害影响因子提取模型及空间危险性评价方法,发展适合四川地震灾区北川羌族自治县的滑坡灾害空间危险性评价模型,探索利用高分辨率遥感数据和其他辅助数据实现灾害危险性评价的技术方法和理论依据。
研究初期,我们选取了信息丰富、便于滑坡识别的真彩色航片作为解译的基础地图,在航片缺失的地区,采用多光谱ALOS数据辅助解译灾区的滑坡灾害;采用SVM影像分类方法试验解译滑坡灾害的可行性,最终确定滑坡在影像上的位置等信息。
结合目前已有的栅格和矢量数据,提取滑坡灾害影响因子:地形因子、地层岩性因子、地表植被因子、河流侵蚀因子,道路影响因子,居民地影响因子以及地震影响因子。
在分析目前常用的滑坡灾害危险性评价的基础上,结合研究区的实际情况,选定了Newamrk位移模型以及比值相关分析模型对研究区的滑坡灾害进行空间危险性评价,综合应用多种影响因子,总结分析因子等级以及滑坡概率的关系,研究地理信息系统空间地形分析、叠加分析、地理统计分析方法,对北川县的滑坡灾害进行危险性评价。
本文有两个创新的地方:一个是滑坡灾害体信息提取方法的扩展:采用人机交互解译和SVM算法计算机自动分类实验提取结果,总体效果较好。另一个是比值相关分析模型的建立:整合概率分析模型和主成分分析模型发展了比值相关分析模型,结果显示该模型具有一定的应用价值。
Based on common used landslide risk evaluation methods, this paper applies high resolution aerial and multi-spectral imagery, digital geological map, digital elevation map, etc., to extract landslide hazard distribution, which is also taken as the sample data with the prior landslide statistics; the model of landslide triggering factors extraction and spatial landslide risk evaluation will be studied, while spatial risk evaluation model of landslide hazard that fits Beichuan County is developed, so the technique methods and theory reference to realize landslide risk evaluation by using high resolution remote sensing imagery and other assistant data are completed.
At the beginning of this study, true color aerial imageries with abundant spectral information and easy landslide recognition are chosen as the basic map to interprete landslide. ALOS imagery is used in the place without aerial imageries. Imagery classification method of SVM is tested to landslide interpretation, so the situation of landslides is determined.
After analyzing the geology, construction, topography and geomorphology in detail, landslide triggering factors are extracted together with the raster and vector data. Then, we choose the following landslide triggering factors, which conclude: topography, lithology, vegetation, river erosion, road, resident, and earthquake.
Newmark displacement model and ratio-relative model are chosen in Beichuan County, in which we will apply many kinds of landslide triggering factors to classify factor grades and analyze the relationship between them and landslide occurrence probability. After that, spatial overlay analysis, buffer analysis, geography statistics in GIS will be used to evaluate spatial landslide hazard risk.
There are two innovative points in this paper. One is methods improvement of landslide interpretation: human-computer interaction and SVM classification are used to get a better result; the other is model building of ratio-relative model: probability model and principal component analysis model are intergrated and developed to be ratio-relative model for certation application.
研究初期,我们选取了信息丰富、便于滑坡识别的真彩色航片作为解译的基础地图,在航片缺失的地区,采用多光谱ALOS数据辅助解译灾区的滑坡灾害;采用SVM影像分类方法试验解译滑坡灾害的可行性,最终确定滑坡在影像上的位置等信息。
结合目前已有的栅格和矢量数据,提取滑坡灾害影响因子:地形因子、地层岩性因子、地表植被因子、河流侵蚀因子,道路影响因子,居民地影响因子以及地震影响因子。
在分析目前常用的滑坡灾害危险性评价的基础上,结合研究区的实际情况,选定了Newamrk位移模型以及比值相关分析模型对研究区的滑坡灾害进行空间危险性评价,综合应用多种影响因子,总结分析因子等级以及滑坡概率的关系,研究地理信息系统空间地形分析、叠加分析、地理统计分析方法,对北川县的滑坡灾害进行危险性评价。
本文有两个创新的地方:一个是滑坡灾害体信息提取方法的扩展:采用人机交互解译和SVM算法计算机自动分类实验提取结果,总体效果较好。另一个是比值相关分析模型的建立:整合概率分析模型和主成分分析模型发展了比值相关分析模型,结果显示该模型具有一定的应用价值。
Based on common used landslide risk evaluation methods, this paper applies high resolution aerial and multi-spectral imagery, digital geological map, digital elevation map, etc., to extract landslide hazard distribution, which is also taken as the sample data with the prior landslide statistics; the model of landslide triggering factors extraction and spatial landslide risk evaluation will be studied, while spatial risk evaluation model of landslide hazard that fits Beichuan County is developed, so the technique methods and theory reference to realize landslide risk evaluation by using high resolution remote sensing imagery and other assistant data are completed.
At the beginning of this study, true color aerial imageries with abundant spectral information and easy landslide recognition are chosen as the basic map to interprete landslide. ALOS imagery is used in the place without aerial imageries. Imagery classification method of SVM is tested to landslide interpretation, so the situation of landslides is determined.
After analyzing the geology, construction, topography and geomorphology in detail, landslide triggering factors are extracted together with the raster and vector data. Then, we choose the following landslide triggering factors, which conclude: topography, lithology, vegetation, river erosion, road, resident, and earthquake.
Newmark displacement model and ratio-relative model are chosen in Beichuan County, in which we will apply many kinds of landslide triggering factors to classify factor grades and analyze the relationship between them and landslide occurrence probability. After that, spatial overlay analysis, buffer analysis, geography statistics in GIS will be used to evaluate spatial landslide hazard risk.
There are two innovative points in this paper. One is methods improvement of landslide interpretation: human-computer interaction and SVM classification are used to get a better result; the other is model building of ratio-relative model: probability model and principal component analysis model are intergrated and developed to be ratio-relative model for certation application.
引文
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