Please wait a minute...
img

官方微信

遥感技术与应用  2022, Vol. 37 Issue (1): 17-23    DOI: 10.11873/j.issn.1004-0323.2022.1.0017
青促会十周年专栏     
基于NDVI变化检测的滑坡遥感精细识别
郭擎1(),朱丽娅1,2,李安1,顾铃燕1,2
1.中国科学院空天信息创新研究院,北京 100094
2.南京大学 金陵学院,江苏 南京 210000
Landslide Identification Method based on NDVI Change Detection
Qing Guo1(),Liya Zhu1,2,An Li1,Lingyan Gu1,2
1.Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
2.Jinling College,Nanjing University,Nanjing 210000,China
 全文: PDF(2911 KB)   HTML
摘要:

随着遥感技术的发展,高分辨率的卫星影像数据逐渐丰富,滑坡灾害的信息提取被进一步推进,当前滑坡灾害应急调查主要以目视解译和野外调查为主,费时费力,难以满足灾后救援的迫切需求。面向像元和面向对象的单时相滑坡遥感信息提取方法等存在着滑坡过识别、误识别的问题。因此,在此提出以滑坡前后多时相遥感影像为数据源的变化检测滑坡识别方法,首先根据归一化植被指数(NDVI)进行基于像元的变化检测确定滑坡预选区,再结合面向对象的几何规则完成滑坡的精细识别,这种基于变化检测和几何规则相结合的方法能有效排除道路、建筑、裸地等光谱特征与滑坡相似的非滑坡部分。以九寨沟滑坡为例,采用高分一号8 m 分辨率多光谱相机2015年8月1日的影像(滑前)以及2017年8月16日的影像(滑后)作为数据源,进行滑坡识别实验。结果表明,和面向对象的单时相方法相比,基于变化检测和几何规则相结合的多时相方法滑坡提取的精度较高,制图精度高达88.80%,用户精度高达81.19%,都大幅超过面向对象单时相法的精度,漏分误差及错分误差分别下降23.22%和11.72%,可为有效组织滑坡灾后救援与重建工作提供可靠依据。

关键词: 灾害信息提取九寨沟滑坡归一化植被指数滑坡识别多时相遥感变化检测    
Abstract:

With the development of the remote sensing technology, the high-resolution satellite data is gradually enriched, and the information extraction of landslide disaster is further promoted. The current emergency investigation of the landslide disaster mainly focuses on the visual interpretation and field investigation, which is time-consuming, laborious, and difficult to meet the urgent need of the rescue after disaster. The single-phase landslide information extraction methods by using remote sensing based on the pixel-oriented or object-oriented have problems of over-recognition or mis-recognition of landslides. Therefore, the multi-temporal landslide information extraction method is worth studying and is expected to achieve good results, especially through the notable NDVI change in landslide. First, multi-temporal remote sensing images before and after the landslide are used as the data source. The landslide pre-selection area is determined using the pixel-oriented NDVI change detection. Then, the object-oriented geometric rules are used to complete the fine identification of landslides. This method based on the combination of the change detection and geometric rules effectively eliminates non-landslide parts which are with the spectral characteristics similar to landslides, such as roads, buildings, and bare land. Taking Jiuzhaigou landslide as the study case, the Gaofen-1 multi-spectral images of August 1, 2015 (before Jiuzhaigou earthquake) and the images of August 16, 2017 (after the earthquake) are used as data sources to conduct landslide identification experiments. The experimental results show that the multi-phase method has high accuracy in landslide identification. Compared with the object-oriented single-phase method, the former method has a mapping accuracy of up to 88.80% and the user accuracy up to 81.19%, both of which greatly exceed the accuracy of the object-oriented single-phase method. Moreover, the omission error and the mis-classification error decreased by 23.22% and 11.72%, respectively. This method determines landslides through the change of NDVI and has high timeliness in landslide identification, which does not need to consider the restrictions of excessive topographic and geomorphic factors and can be applied to most areas. It is believed that our method can provide a reliable basis for the effective organization of rescue and reconstruction work after landslide disaster.

Key words: Disaster information extraction    Jiuzhaigou landslide    NDVI    Landslide identification    Multi-temporal remote sensing    Change detection
收稿日期: 2020-08-10 出版日期: 2022-04-08
ZTFLH:  TP79  
基金资助: 国家自然科学基金面上项目“多源多时相遥感图像光谱特征鲁棒性融合研究”(61771470)
作者简介: 郭擎(1980-),女,河南驻马店人,博士,研究员,主要从事遥感信息提取与滑坡灾害监测研究。E?mail:guoqing@aircas.ac.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
郭擎
朱丽娅
李安
顾铃燕

引用本文:

郭擎,朱丽娅,李安,顾铃燕. 基于NDVI变化检测的滑坡遥感精细识别[J]. 遥感技术与应用, 2022, 37(1): 17-23.

Qing Guo,Liya Zhu,An Li,Lingyan Gu. Landslide Identification Method based on NDVI Change Detection. Remote Sensing Technology and Application, 2022, 37(1): 17-23.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0017        http://www.rsta.ac.cn/CN/Y2022/V37/I1/17

图 1  研究区概况
图 2  影像预处理流程
I(植被扰动参数)Npost <NmaxNpost<0.6Npost<0.2
I>1.05147
I>1.10258
I>1.15369
表1  Behling方法中植被扰动分类规则
图3  滑坡识别结果
图4  部分道路的排除效果
图5  部分建筑的排除效果
评价指标变化检测与规则的多时相法面向对象和规则的单时相法
制图精度88.80%65.58%
用户精度81.19%69.47%
漏分误差11.20%34.42%
错分误差18.81%30.53%
表2  混淆矩阵精度评价结果
1 Wan Baofeng, Yuan Shuihua, Su Jianping. Remote sensing image recognition of landslide based on texture analysis[J]. Surveying and Mapping of Geology and Mineral Resources, 2009(2):11-14.
1 万保峰, 袁水华, 苏建平. 基于纹理分析的滑坡遥感图像识别[J]. 地矿测绘, 2009(2):11-14.
2 Zhu Jiao. Research on landslide information extraction in Baoxing base on GF-1 image[D]. Chengdu: Chengdu University of Technology, 2015.朱娇.基于高分一号影像的宝兴县滑坡信息提取研究[D].成都:成都理工大学, 2015.
3 Li Song, Li Yiqiu, An Yulun. Automatic recognition of landslides based on change detection[J], Remote Sensing Information, 2010,25(1):27-31.
3 李松, 李亦秋, 安裕伦. 基于变化检测的滑坡灾害自动识别[J]. 遥感信息, 2010,25(1):27-31.
4 Hu Deyong, Li Jing, Zhao Wenji, et al. Object -oriented landslide detection from remote sensing imageries with high resolution[J]. Journal of Natural Disasters, 2008,17(6):42-46.
4 胡德勇, 李京, 赵文吉,等. 基于对象的高分辨率遥感图像滑坡检测方法[J]. 自然灾害学报, 2008, 17(6):42-46.
5 Behling R, Roessner S, Kaufmann H, et al. Automated spatiotemporal landslide mapping over large areas using RapidEye time series data[J]. Remote Sensing, 2014, 6(9):8026-8055. DOI:10.3390/rs6098026 .
doi: 10.3390/rs6098026
6 Hu Zhenxing, Xu Hong, Wang Chaoliang,et al. A Landslides detection method based on time series remote sensing images[J]. Spacecraft Recovery & Remote Sensing, 2018,39(2):104-114.
6 虎振兴,徐泓,汪超亮,等.基于时间序列遥感影像的滑坡检测方法[J].航天返回与遥感,2018,39(2):104-114.
7 Tang Shichao, Chen Chao, Tan Yi. Research on extraction method of post-earthquake landslide information based on SAR image[J]. Laser Journal, 2020, 41(10):58-62.
7 唐世超, 陈超, 谭毅. 基于SAR图像的震后滑坡信息提取方法研究[J].激光杂志,2020, 41(10):58-62.
8 Long Yujie, Li Weile, Huang Runqiu, et al. Automatic extraction and evolution trend analysis of landslides in Mianyuan River Basin in the 10 years after Wenchuan Earthquake[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11):1792-1800.
8 龙玉洁,李为乐,黄润秋,等. 汶川地震震后10 a绵远河流域滑坡遥感自动提取与演化趋势分析[J]. 武汉大学学报(信息科学版),2020,45(11):1792-1800.
9 Wu Weiying, Wang Xiaoqing, Deng Fei. Compilation and spatial analysis of co seismic landslide inventory by using high resolution remote sensing images in earthquake emergency response——An example of the Jiuzhaigou Ms 7.0 earthquake on august 8, 2017[J]. Technology for Earthquake Disaster Prevention,2017, 12(4):815-825.
9 吴玮莹,王晓青,邓飞. 基于高分卫星遥感影像的地震应急滑坡编目与分布特征探讨——以2017年8月8日九寨沟7.0级地震为例[J]. 震灾防御技术, 2017, 12(4):815-825.
10 Yong Wanling. The research of multi-scale segmentation,object-oriented extraction of target Information[D]. Lanzhou: Lanzhou Jiaotong University, 2016.雍万铃.基于面向对象多尺度分割的目标信息提取研究[D].兰州:兰州交通大学, 2016.
11 Deng Shubin, Chen Qiujin, She Huijian, et al. ENVI remote sensing image processing method, 2nd edition[M]. Beijing: Hi-gher Education Press,2014.邓书斌,陈秋锦,社会建,等.ENVI遥感图像处理方法,2版[M].北京:高等教育出版社,2014.
12 Chen Yunhao, Feng Tong, Shi Peijun, et al. Classification of remote sensing image based on object oriented and class rules[J]. Geomatics and Information Science of Wuhan University, 2006, 31(4):316-320.
12 陈云浩, 冯通, 史培军,等. 基于面向对象和规则的遥感影像分类研究[J]. 武汉大学学报(信息科学版), 2006, 31(4):316-320.
[1] 李晓东,闫守刚,宋开山. 遥感监测东北地区典型湖泊湿地变化的方法研究[J]. 遥感技术与应用, 2021, 36(4): 728-741.
[2] 陈康明,朱旭东. 基于Google Earth Engine的南方滨海盐沼植被时空演变特征分析[J]. 遥感技术与应用, 2021, 36(4): 751-759.
[3] 王崇阳,田昕. 基于GF⁃1 PMS数据的森林覆盖变化检测[J]. 遥感技术与应用, 2021, 36(1): 208-216.
[4] 张新平,乔治,李皓,闫杰,张芳芳,赵栋锋,王得祥,康海斌,杨航,冯扬. 基于Landsat影像和不规则梯形方法遥感反演延安城市森林表层土壤水分[J]. 遥感技术与应用, 2020, 35(1): 120-131.
[5] 罗家顺,邱建秀,赵天杰,王大刚. 基于Sentinel-1数据的黑河中游土壤水分反演[J]. 遥感技术与应用, 2020, 35(1): 23-32.
[6] 何浩,刘修国,沈永林. 基于视差的高分辨率遥感影像建筑物变化检测[J]. 遥感技术与应用, 2019, 34(6): 1315-1323.
[7] 王源,陈富龙,胡祺,唐攀攀. COSMO-SkyMed时序影像南京城市变化检测研究[J]. 遥感技术与应用, 2019, 34(5): 1054-1063.
[8] 张因果,陈芸芝. 一种基于改进土地覆盖更新方法的新增建设用地自动提取[J]. 遥感技术与应用, 2019, 34(5): 1073-1081.
[9] 申祎,王超,胡佳乐. 一种结合空间与光谱信息的改进CVA变化检测方法[J]. 遥感技术与应用, 2019, 34(4): 799-806.
[10] 冀新莹, 韦玉春, 王问尧, 方宏. 城镇区域高分辨率遥感影像地表覆盖变化检测的误差分析[J]. 遥感技术与应用, 2018, 33(5): 932-941.
[11] 孟梦,牛铮. 近30 a内蒙古NDVI演变特征及其对气候的响应[J]. 遥感技术与应用, 2018, 33(4): 676-685.
[12] 杨朦朦,汪汇兵,欧阳斯达,范奎奎,戚凯丽. 基于双树复小波分解的BP神经网络遥感影像分类[J]. 遥感技术与应用, 2018, 33(2): 313-320.
[13] 姜爱辉,刘国林,陈富龙. 基于PALSAR-1影像的汉函谷关遗迹变化检测研究[J]. 遥感技术与应用, 2017, 32(5): 787-793.
[14] 张祥,陈报章,赵慧,汪磊. 基于时序Sentinel-1A数据的农田土壤水分变化检测分析[J]. 遥感技术与应用, 2017, 32(2): 338-345.
[15] 张婕,张文煜,冯建东,王宏义,于泽,宋玮. 基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究[J]. 遥感技术与应用, 2016, 31(5): 886-892.