地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (12): 1554-1562.doi: 10.3724/SP.J.1047.2015.01554

• 遥感科学与应用技术 • 上一篇    

基于信息量模型的涪陵区地质灾害易发性评价

谭玉敏1(), 郭栋1, 白冰心1, 许波2   

  1. 1. 北京航空航天大学交通科学与工程学院, 北京 100191
    2. 美国加州州立大学San Bernardino分校地理与环境系,CA 92407
  • 收稿日期:2015-03-24 修回日期:2015-06-10 出版日期:2015-12-20 发布日期:2015-12-20
  • 作者简介:

    作者简介:谭玉敏(1977-),女,博士,副教授,研究方向为遥感与GIS应用。E-mail:tanym@buaa.edu.cn

  • 基金资助:
    2013国家卫星及应用高技术产业化专项“基于国产卫星应用技术的三峡库区生态环境动态监测与应急服务示范”

Geological Hazard Risk Assessment Based on Information Quantity Model in Fuling District, Chongqing City, China

TAN Yumin1,*(), GUO Dong1, BAI Bingxin1, XU Bo2   

  1. 1. Department of Civil Engineering, Beihang University, Beijing 100191, China
    2. Department of Geography & Environmental Studies, California State University San Bernardino, CA 92407, USA
  • Received:2015-03-24 Revised:2015-06-10 Online:2015-12-20 Published:2015-12-20
  • Contact: TAN Yumin E-mail:tanym@buaa.edu.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

本文以重庆涪陵区为研究区域,选取坡度、坡向、累计汇水面积、地层岩性、水域、降雨量、植被和土地利用分类8个影响因子,提取高分一号遥感数据(2013.12.24)动态影响因子,引入信息量模型,分别计算上述影响因子对应的信息量,对该时期示范区的地质灾害危险性进行评价,并引入ROC曲线和AUC评价指标,对得到的区域地质灾害易发性评价结果进行精度评估。结果显示,2013年12月研究区内高易发区面积占总面积的9.73%,该易发区内含有104个地质灾害点,占所有灾害点的52.7%,灾积比为5.42,明显大于其他易发等级类别。利用ROC评价方法,计算成功率曲线AUC为0.796,预测率曲线AUC为0.748(74.8%),具有较高的可靠性,证明本文方法在该区域地质灾害易发性评价的适应性良好。

关键词: 地质灾害, 易发性评价, 信息量模型, 动态影响因子

Abstract:

Using geospatial technologies to assess geological hazard risk has been proved feasible, effective and important in the southwest of China, which is featured by mountainous landscape and the population density is very large. The main objective of this study is to make the risk assessment of the geological hazards in Fuling district using information quantity model, and eight triggering factors are used, including slope, aspect, cumulative catchment area, formation lithology, distances to water, precipitation, vegetation, and land use/land cover type respectively. GaoFen-1 image of December 24, 2013 is used to extract two dynamic triggering factors, vegetation and land use, and precipitation is also taken as a dynamic triggering factor. All triggering factors were then used to construct an information model to assess and predict the geological hazards in the study area in December 2013, producing a geological hazard risk distribution map. Finally, ROC curve was used to validate the information model. The statistical results indicate that the areas with high risk zone is about 9.73% of the entire area and that the percentage of the geological hazards sites is about 52.7% of the entire geological hazards sites. And it shows a satisfactory consistency between the susceptibility map and the geological hazard locations. The AUC of success-rate ROC of 0.796 and the AUC of prediction-rate ROC of 0.748 demonstrate the robustness and relatively good reliability of the information quantity model. Above all, the model can be applied to interpret and predict the geological hazard occurrences in the study area.

Key words: Geological hazard, risk assessment, information quantity, dynamic triggering factors