基于CBERS-02卫星数据的地震滑坡识别——以青川县为例
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摘要
以5.12地震后四川省青川县为研究区域,以该区域CBERS-02 CCD数据及DEM数据为基础数据,结合国内外滑坡遥感识别数据处理方法,通过九个特征指标(CBERS-02 CCD五个波段、亮度、绿度、湿度及坡度)组合对青川县进行滑坡识别。首先经过镶嵌裁剪得到研究区影像图,用1、2、3波段合成真彩色图像,对该区域遥感影像滤波增强处理,减少图像斑点,改善图像质量,同时应用穗帽变换生成研究区的亮度、绿度、湿度指标;然后结合地震滑坡分布的地质地貌、构造岩性、形状纹理特点,对九个特征指标进行最大似然法监督分类;最后利用青川县的现场调查资料,对该滑坡识别结果进行了验证,同时结合滑坡发生的机理对滑坡分布进行了分析。研究区域的滑坡分布具有以下特点:1)分布范围广泛,且河谷两岸较多;2)地震产生的滑坡大多发生在坡度40°～50°、海拔700～1 100 m的山坡上。研究结果表明,利用中巴卫星进行滑坡识别的分类精度为76.4676%,Kappa系数为0.7130,抽取初步识别的96个滑坡进行野外校验,正确率为89%。证明中巴卫星适应于山区地震滑坡的识别。
Taking Qingchuan County after 5.12 Wenchuan Earthquake in Sichuan Province as study area and CBERS-02 CCD data and DEM data as main data,making reference to landslide remote sensing data processing and interpreting methods at home and abroad,the study established a set of better precision solution to landslide identification,that is combining CBERS-02 CCD five bands,brightness,greenness,wetness and gradient.Firstly,we adjust histogram of the raw data so that the differences between the different surface features were obvious;and then select the adjusted 3 bands to synthetic true color images,and then the images filtering to improve the image quality;through the tasseled cap transformation we got the brightness(Brightness),greenness(Greenness) and humidity(Wetness) three vegetation indices of the study area in order to increase the amount of information of landslide identification;Secondly,combine geology and physiognomy,structure and lithology of landslide distribution,shape and texture characteristics to landslide supervised classification;Finally,verify the landslide classification results based on fieldwork,also analyze landslide distribution combining landslide mechanism.Landslide distribution of the study area has the following characteristics:(1) the distribution of a wide range,especially cross-strait valley;(2) landslide induced by earthquakes occurred in most of the slope of 40°-50°,the altitude of 700-1100m.The research results indicate that using CBERS satellite image for landslide interpretation has accuracy of 76.4676%,Kappa Coefficient is 0.7130.96 preliminary identified samples for field surveying prove veracity of 89%.The results show that CBERS-02 CCD data is propitious to landslide identification.Despite in comparison with high-resolution images of foreign countries,CBERS image is slightly inferior,but as independent intellectual property rights of domestic satellites,we should try to play to their price and spatial resolution advantages for the reconstruction work service.
Taking Qingchuan County after 5.12 Wenchuan Earthquake in Sichuan Province as study area and CBERS-02 CCD data and DEM data as main data,making reference to landslide remote sensing data processing and interpreting methods at home and abroad,the study established a set of better precision solution to landslide identification,that is combining CBERS-02 CCD five bands,brightness,greenness,wetness and gradient.Firstly,we adjust histogram of the raw data so that the differences between the different surface features were obvious;and then select the adjusted 3 bands to synthetic true color images,and then the images filtering to improve the image quality;through the tasseled cap transformation we got the brightness(Brightness),greenness(Greenness) and humidity(Wetness) three vegetation indices of the study area in order to increase the amount of information of landslide identification;Secondly,combine geology and physiognomy,structure and lithology of landslide distribution,shape and texture characteristics to landslide supervised classification;Finally,verify the landslide classification results based on fieldwork,also analyze landslide distribution combining landslide mechanism.Landslide distribution of the study area has the following characteristics:(1) the distribution of a wide range,especially cross-strait valley;(2) landslide induced by earthquakes occurred in most of the slope of 40°-50°,the altitude of 700-1100m.The research results indicate that using CBERS satellite image for landslide interpretation has accuracy of 76.4676%,Kappa Coefficient is 0.7130.96 preliminary identified samples for field surveying prove veracity of 89%.The results show that CBERS-02 CCD data is propitious to landslide identification.Despite in comparison with high-resolution images of foreign countries,CBERS image is slightly inferior,but as independent intellectual property rights of domestic satellites,we should try to play to their price and spatial resolution advantages for the reconstruction work service.
引文
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[22]赵文慧,赵文吉,李小娟,等.中巴资源二号卫星影像在土地利用变化中的应用——以包头固阳县土地利用变化为例[J].首都师范大学学报:自然科学版,2007,28(6):78-82.
[23]魏锋华,李才兴,扎西央宗.基于中巴资源卫星数据的积雪监测研究[J].国土资源遥感,2007,(3):31-35.
[24]李四海,刘振民,王华,等.中巴卫星数据在海岸带环境监测中的应用[J].遥感技术与应用,2003,12(8):66-72.
[25]施晶晶.中巴资源一号卫星湖泊信息提取?——以南京景为例[J].湖泊科学,2001,13(3):280-284.
[26]何宇华,史良树,张荣慧,等.中巴资源卫星数据(CBERS-02)在土地调查中的应用[J].中国土地科学,2007,21(2):51-57.
[27]苏凤环,刘洪江,韩用顺.汶川地震山地灾害遥感快速提取及其分布特点分析[J].遥感学报,2008,12(6):956-963.
[28]花利忠,崔胜辉,李新虎,等.汶川大地震滑坡体遥感识别及生态服务价值损失评估[J].生态学报,2008,28(12):5809-5816.
[29]姚文波,刘文兆,侯甬坚.汶川大地震陇东黄土高原崩塌滑坡的调查分析[J].生态学报,2008,28(12):5917-5926.
[30]王世新,周艺,魏成阶,等.汶川地震重灾区堰塞湖次生灾害危险性遥感评价[J].遥感学报,2008,12(6):900-907.
[31]范建容,张建强,田兵伟,等.汶川地震次生灾害毁坏耕地的遥感快速评估方法——以北川县唐家山地区为例[J].遥感学报,2008,12(6):917-924.
[32]陈世荣,马海建,范一大,等.基于高分辨率遥感影像的汶川地震道路损毁评估[J].遥感学报,2008,12(6):949-955.
[2]李向东,陈玉萍.滑坡灾害危险性研究现状与展望[J].国土资源情报,2008,(7):43-47.
[3]张学文.张家庄滑坡危害与防治技术[J].甘肃水利水电技术,2008,44(5):328-329,351.
[4]宋杨,范湘涛,陆现彩.利用多时相遥感影像与DEM数据的滑坡灾害调查——以新滩地区为例[J].安徽师范大学学报:自然科学版,2006,(29)3:276-280.
[5]王治华.滑坡、泥石流遥感回顾与新技术展望[J].国土资源遥感,1999,3:10-15.
[6]赵建华,杨树锋,陈汉林,等.浙江庆元地区滑坡灾害的多要素评价[J].高校地质学报,2002,8(4):460-465.
[7]朱博勤,聂跃平.易贡巨型高速滑坡卫星遥感动态监测[J].自然灾害学报,2001,10(3):103-106.
[8]Fabbir A G,Chung C J F,Cendrero A,et al Is prediction of future landslides possible with a GIS[J].Natural Hazards,2002,30(3):487-499.
[9]Wang Z H.Preliminary study for digital landslide,towards digital earth-proceedings of the international symposium on digital earth[J].Science Press,1999,(1):718-722.
[10]Nichol J,Wong M S.Detection and interpretation of landslides using satellite images[J].Land Degradation&Development Land Degrad Develop.2005,16:243–255.
[11]Yugsi F,Eisenbeiss H,Remondino F,et al2006.Multitemporal monitoring of landslidesin archaeol-ogical mountainous environments using optical imagery:the case of El Tambo,Ecu-ador[A].II International Conference Remote Sensing in Archaeology.
[12]赵晓军,潘凤荣,都瑞敏.日本应用数字化地图对滑坡判定的研究[J].水土保持科技情报,2002,3:1-3.
[13]CJ Van Westen Geo-Information tools for landsl-ide risk assessment:an overview of recent devel-opments[A].International Symposium on Landslid-es(ISL'2004):Evaluation and Stabilization vol.1;20040628-0702;Rio de Janeiro(BR).
[14]邓清禄,王学平,刘吉平,等.长江三峡工程库首区滑坡与线性构造的关系[J].长春科技大学学报,2000,30(4):384-387.
[15]马小红,卢凯章,王磊.Quick Bird遥感数据在龙羊峡库区白刺滩滑坡调查中的应用[J].高原地震,2008,20(1):57-60.
[16]洪嘉祥.甘肃东部滑坡遥感调查分析评价[J].中国水土保持,2005,(9):8-9.
[17]王治华,于学证.西藏易贡大滑坡遥感解译[J].遥感信息,2000,(2):24-25.
[18]李铁锋,徐岳仁,潘懋,等.基于多期SPOT-5影像的降雨型浅层滑坡遥感解译研究[J].北京大学学报:自然科学版,2007,1(3):1-7.
[19]姚鑫,戴福初,陈健.金沙江干热河谷区滑坡遥感解译研究[J].中国地质灾害与防治学报,2006,17(3):18-21.
[20]Singhroy V et al.Landslide characterization in Canads using inteiferometric SAR and combined SAR and TM Images[J].Advances in Space Re-search,1998,21(3):465-476.
[21]Iglseder H,Arens-Fischer W,Wolfsberger W.Small sateilite constellations for disaster detection and moitoring.Advances in Space Research[J].1995,15(11):79-85.
[22]赵文慧,赵文吉,李小娟,等.中巴资源二号卫星影像在土地利用变化中的应用——以包头固阳县土地利用变化为例[J].首都师范大学学报:自然科学版,2007,28(6):78-82.
[23]魏锋华,李才兴,扎西央宗.基于中巴资源卫星数据的积雪监测研究[J].国土资源遥感,2007,(3):31-35.
[24]李四海,刘振民,王华,等.中巴卫星数据在海岸带环境监测中的应用[J].遥感技术与应用,2003,12(8):66-72.
[25]施晶晶.中巴资源一号卫星湖泊信息提取?——以南京景为例[J].湖泊科学,2001,13(3):280-284.
[26]何宇华,史良树,张荣慧,等.中巴资源卫星数据(CBERS-02)在土地调查中的应用[J].中国土地科学,2007,21(2):51-57.
[27]苏凤环,刘洪江,韩用顺.汶川地震山地灾害遥感快速提取及其分布特点分析[J].遥感学报,2008,12(6):956-963.
[28]花利忠,崔胜辉,李新虎,等.汶川大地震滑坡体遥感识别及生态服务价值损失评估[J].生态学报,2008,28(12):5809-5816.
[29]姚文波,刘文兆,侯甬坚.汶川大地震陇东黄土高原崩塌滑坡的调查分析[J].生态学报,2008,28(12):5917-5926.
[30]王世新,周艺,魏成阶,等.汶川地震重灾区堰塞湖次生灾害危险性遥感评价[J].遥感学报,2008,12(6):900-907.
[31]范建容,张建强,田兵伟,等.汶川地震次生灾害毁坏耕地的遥感快速评估方法——以北川县唐家山地区为例[J].遥感学报,2008,12(6):917-924.
[32]陈世荣,马海建,范一大,等.基于高分辨率遥感影像的汶川地震道路损毁评估[J].遥感学报,2008,12(6):949-955.
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