基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例

doi:10.13474/j.cnki.11-2246.2022.0109

测绘通报 ›› 2022, Vol. 0 ›› Issue (4): 51-55.doi: 10.13474/j.cnki.11-2246.2022.0109

基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例

丁永辉¹, 张勤¹, 杨成生¹, 王猛², 丁辉³

1. 长安大学地质工程与测绘学院, 陕西西安 710054;
2. 四川省地质调查院, 四川成都 610081;
3. 中国地质调查局西安调查中心, 陕西西安 710054

收稿日期:2021-06-15 出版日期:2022-04-25 发布日期:2022-04-26
通讯作者: 张勤。E-mail:dczhangq@chd.edu.cn
作者简介:丁永辉(1997-),男,硕士,研究方向为高分遥感滑坡识别解译。E-mail:chddyh@126.com
基金资助:
国家自然科学基金重点项目（41731066）；国家自然科学基金重大项目（41790445）；中央高校领军人才项目（300102261302）

Landslide identification in Jinsha River basin based on high-resolution remote sensing:taking Wangdalong village of Batang county as an example

DING Yonghui¹, ZHANG Qing¹, YANG Chengsheng¹, WANG Meng², DING Hui³

1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
2. Sichuan Institute of Geological Survey, Chengdu 610081, China;
3. Xi'an Center of China Geological Survey, Xi'an 710054, China

Received:2021-06-15 Online:2022-04-25 Published:2022-04-26

摘要/Abstract

摘要： 金沙江流域因两岸地势陡峭、软弱岩层发育、降雨集中等，使得流域内滑坡灾害分布密集。高分辨率遥感是滑坡识别的重要手段，但通过目视解译法开展的大范围滑坡灾害识别，具有工作量大、效率低的特点。针对此问题，本文采用基于面向对象的分类方法，提出了利用滑坡灾害的光谱、形状、空间等特征进行区域内滑坡灾害的快速识别。同时，选取金沙江流域巴塘县王大龙村区段进行了滑坡识别提取试验，区域内利用面向对象分类方法识别出滑坡18处，其中12处与目视解译结果相同，一致性为75%；发现3处目视解译未识别出的隐蔽性滑坡。结果表明，该方法识别效果较好，可为后续的金沙江流域乃至川藏铁路沿线的大范围滑坡识别提取及滑坡编目工作提供参考。

关键词: 金沙江滑坡, 光学遥感, 面向对象分析, 滑坡识别, 防灾减灾

Abstract: Because of the steep terrain,the development of soft rock,and the concentration of rainfall on both sides of the Jinsha River basin,the landslide disasters are densely distributed in the basin.High-resolution remote sensing is an important means of landslide identification,but the visual interpretation method for large-scale landslide hazard identification has the characteristics of heavy workload and low efficiency.In this paper,the object-oriented classification method is used to identify the large-scale landslides in Jinsha River basin.The spectrum,shape and space characteristics of landslides are used to identify the landslides in the region.At the same time,the Wangdalong village section of Batang county in the Jinsha River basin is selected for the identification and extraction of landslides.In this area,18 landslides are identified by object-oriented classification method,12 of which are the same as the results of visual interpretation,and the consistency is 75%.In addition,3 hidden landslides are found which are not identified by visual interpretation.The results show that the recognition effect of this method is good,which can provide a reference for the subsequent large-scale landslide identification extraction and landslide cataloging work in the Jinsha River basin and even along the Sichuan-Tibet railway.

Key words: Jinsha River landslide, optical remote sensing, object-oriented analysis, landslide identification, disasterprevention and reduction

中图分类号:

P237

丁永辉, 张勤, 杨成生, 王猛, 丁辉. 基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例[J]. 测绘通报, 2022, 0(4): 51-55.

DING Yonghui, ZHANG Qing, YANG Chengsheng, WANG Meng, DING Hui. Landslide identification in Jinsha River basin based on high-resolution remote sensing:taking Wangdalong village of Batang county as an example[J]. Bulletin of Surveying and Mapping, 2022, 0(4): 51-55.

参考文献 16

[1]	LIU Xiaojie,ZHAO Chaoying, ZHANG Qin, et al.Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China[J].Engineering Geology, 2021, 284:106.
[2]	BAATZ M, SCHÄPE A.Object-oriented and multi-scale image analysis in semantic networks[C]//Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing.Enschede,Netherlands:ITC, 1999:1-7.
[3]	MARTHA T R, KERLE N, JETTEN V, et al.Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods[J].Geomorphology, 2010, 116(1):24-36.
[4]	周志华, 林维芳, 许高程, 等.基于面向对象的滑坡快速识别技术研究[J].安徽农业科学, 2012, 40(5):3017-3018.
[5]	丁辉, 张茂省, 朱卫红, 等.黄土滑坡高分辨率遥感影像识别:以陕西省延安市地区为例[J].西北地质, 2019, 52(3):231-239.
[6]	杨成生, 董继红, 朱赛楠, 等.金沙江结合带巴塘段滑坡群InSAR探测识别与形变特征[J].地球科学与环境学报, 2021, 43(2):398-408.
[7]	张成龙,李振洪,余琛,等.利用GACOS辅助下InSAR Stacking对金沙江流域进行滑坡监测[J].武汉大学学报(信息科学版),2021,46(11):1649-1657.
[8]	陆会燕, 李为乐, 许强, 等.光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J].武汉大学学报(信息科学版), 2019, 44(9):1342-1354.
[9]	唐尧, 王立娟, 马国超, 等.利用国产遥感卫星进行金沙江高位滑坡灾害灾情应急监测[J].遥感学报, 2019, 23(2):252-261.
[10]	吕澜清.藏东贡觉地区滑坡的空间分布与控制因素研究[D].成都:成都理工大学,2020.
[11]	朱赛楠, 殷跃平, 王猛, 等.金沙江结合带高位远程滑坡失稳机理及减灾对策研究:以金沙江色拉滑坡为例[J].岩土工程学报, 2021, 43(4):688-697.
[12]	伍先国, 蔡长星.金沙江断裂带新活动和巴塘6.5级地震震中的确定[J].地震研究, 1992, 15(4):401-410.
[13]	范雷, 张琪.金沙江苏洼龙-奔子栏河段滑坡灾害发育分布规律[J].长江科学院院报, 2016, 33(3):38-41.
[14]	马浩然.基于多层次分割的遥感影像面向对象森林分类[D].北京:北京林业大学, 2014.
[15]	于欢, 张树清, 孔博, 等.面向对象遥感影像分类的最优分割尺度选择研究[J].中国图象图形学报, 2010, 15(2):352-360.
[16]	LANDIS J R, KOCH G G.The measurement of observer agreement for categorical data[J].Biometrics, 1977, 33(1):159-174.

基于高分遥感的金沙江流域滑坡识别——以巴塘县王大龙村为例

Landslide identification in Jinsha River basin based on high-resolution remote sensing:taking Wangdalong village of Batang county as an example

可视化

摘要/Abstract

引用本文

使用本文

参考文献 16

相关文章 4

编辑推荐

Metrics

本文评价

[1]	吴绿川, 王剑辉, 符彦. 基于InSAR技术和光学遥感的贵州省滑坡早期识别与监测[J]. 测绘通报, 2021, 0(7): 98-102.
[2]	章豪, 温剑峰, 陈温清, 徐刚, 史瑞洁, 戴欣宜, 陈杰, 朱安峰, 范全龙. 对陡坡陡崖引发地质灾害的潜在影响范围进行建模分析[J]. 测绘通报, 2021, 0(6): 117-121,133.
[3]	黄磊, 陈尔东昊. 基于多特征U-Net网络的工程活动图斑快速识别[J]. 测绘通报, 2021, 0(12): 75-78.
[4]	任晓春, 李伟, 王玮. 高分光学遥感影像在铁路勘察中的应用及展望[J]. 测绘通报, 2019, 0(5): 44-47.