浏览全部资源
扫码关注微信
Published： 2018-3 ，
Accepted： 30 August 2017
扫 描 看 全 文
王小标, 谢顺平, 都金康. 2018. 水体指数构建及其在复杂环境下有效性研究. 遥感学报, 22(2): 360–372
Wang X B, Xie S P and Du J K. 2018. Water index formulation and its effectiveness research on the complicated surface water surroundings. Journal of Remote Sensing, 22(2): 360–372
针对复杂环境下水体提取精度易受到低反射率地表影响的问题，本文以秦淮河流域为实验区，选用2015年10月12日ETM+影像，在水体、低反射率地表和其他地表纯净像元平均反射率基础上构建Multi-Band Water Index (MBWI)。搜集1985年—2016年已有的12种水体指数，选取南京、南宁和烟台地区3景影像中不同地表环境的6个测试点，采用基于K均值聚类的水体指数法提取水体后分析水体指数在复杂环境下的有效性。结果表明，MBWI以平均总体精度、Kappa系数、错分和漏提误差分别为98.62%、0.95、3.46%和3.74%，总体上较其他水体指数具有一定的优势；实验发现TCW(Tasseled Cap Wetness index)不能有效地消除山体阴影，TCW和AWEInsh(Automated Water Extraction Index with no shadow)误将白色高反射率建筑噪声分为水体，水体提取结果中均有低反射率非水体信息存在；水体在可见光而非水体在红外反射率较高，基于二者的差异及从绿到红外波段水体似呈递减现象构建的MBWI能有效的抑制低反射率噪声，对水文水资源的研究与应用具有一定的实际价值。
Surface water includes irreplaceable and nonrenewable resources for terrestrial life. However
the rapid urbanization is causing diverse changes in size
amount
and quality of surface water. Accurately extracting surface water from remote sensing images is important for water environmental conservation and water resource management. This study aims to formulate a Multi-Band Water Index (MBWI) that consistently improves surface water extraction accuracy in the presence of various environmental noises. A new MBWI is designed to improve the accuracy of surface water extraction by increasing spectral separability between water and non-water surfaces. The method uses the average reflectance of the pure pixel of the seven land cover types
namely
surface water
forest
mountainous shadow
high-reflectance building
low-reflectance building
farmland
and bare soil
obtained from a Landsat 7 image of the Qinhuai River basin acquired on October 12
2015. In general
the common threshold method is a popular approach to obtaining the results of surface water extraction from remote sensing imagery. However
determining the optimal threshold is an iterative
complicated
and challenging process. The K-means clustering method is applied to automatically extract surface water to avoid the artificial errors in determining the optimum threshold from the MBWI map. Surface water mapping outputs derived from water index methods that are based on K-means cluster are used to analyze the extraction accuracy of water indexes under complicated land cover types. To validate the availability of MBWI
twelve existing water indexes are collected from 1985 to 2016 as the comparable methods of surface water extraction. Furthermore
six test sites with various impact aspects for extracting surface water
e.g.
mountainous shadows
high building shadows
and dark built-up areas that are usually sources of surface water extraction errors
are selected from three images (one Landsat 7 image and two Landsat 8 images) from Nanjing
Nanning
and Yantai. Compared with the existing water indexes
our proposed MBWI yields acceptable surface water mapping outputs. The assessment factors
namely
average overall accuracy (98.62%)
Kappa coefficients (0.95)
commission errors (3.46%)
and omission errors (3.74%)
are better than those of the existing surface water extraction methods. Results show that certain water indexes are weak in identifying surface water from land cover types. The Tasseled Cap Wetness (TCW) index is not effective in eliminating mountainous shadows. TCW and Automated Water Extraction Index with no shadow (AWEInsh) inaccurately identify white high-reflectance building noises with surface water. The accuracy of surface water extraction is usually constrained by land cover types that display similar reflectance to surface water. Therefore
low-reflectance non-water surfaces exist more or less in surface water mapping outputs. The maximum reflectance of surface water is presented in visible light bands
whereas that of the non-water surfaces is in infrared bands. Moreover
surface water reveals similarly decreasing trends from green to infrared bands. A new water index
named MBWI
is formulated according to the band difference of water and non-water surfaces. This difference has important practical significance for water resource studies and applications.
秦淮河流域多波段水体指数K均值聚类法精度分析
Qinhuai River Basinmulti-band water indexK-means clusteraccuracy analysis
毕海芸, 王思远, 曾江源, 赵岩, 王辉, 殷慧. 2012. 基于TM影像的几种常用水体提取方法的比较和分析. 遥感信息, 27(5): 77–82
Bi H Y, Wang S Y, ZengJ Y, Zhao Y, Wang H and Yin H. 2012. Comparison and analysis of several common water extraction methods based on TM image. Remote Sensing Information, 27(5): 77–82 (
曹荣龙, 李存军, 刘良云, 王纪华, 阎广建. 2008. 基于水体指数的密云水库面积提取及变化监测. 测绘科学, 33(2): 158–160
Cao R L, Li C J, Liu L Y, Wang J H and Yan G J. 2008. Extracting Miyun reservoir’s water area and monitoring its change based on a revised normalized different water index. Science of Surveying and Mapping, 33(2): 158–160 (
陈文倩, 丁建丽, 李艳华, 牛增懿. 2015. 基于国产GF-1遥感影像的水体提取方法. 资源科学, 37(6): 1166–1172
Chen W Q, Ding J L, Li Y H and Niu Z Y. 2015. Extraction of water information based on China-made GF-1 remote sense image. Resources Science, 37(6): 1166–1172 (
Crist E P. 1985. A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17(3): 301–306
Danaher T and Collett L. 2006. Development,optimisation and multi-temporal application of a simple Landsat based water index//Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference.Canberra, Australia: Spatial Sciences Institute.
丁凤. 2009. 基于新型水体指数(NWI)进行水体信息提取的实验研究. 测绘科学, 34(4): 155–157
Ding F. 2009. Study on information extraction of water body with a new water index (NWI). Science of Surveying and Mapping, 34(4): 155–157 (
杜云艳, 周成虎. 1998. 水体的遥感信息自动提取方法. 遥感学报, 2(4): 264–269
Du Y Y and Zhou C H. 1998. Automatically extracting remote sensing information for water bodies. Journal of Remote Sensing, 2(4): 264–269 (
Feyisa G L, Meilby H, Fensholt R and Proud S R. 2014. Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23–35
Fisher A, Flood N and Danaher T. 2016. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment, 175: 167–182
高泽润. 2012. 基于ETM+影像的洞庭湖水体富营养化监测研究. 长沙: 中南大学
Gao Z R. 2012. Monitoring for Eutrophication of Dongting Lake based on ETM+ Image. Changsha: Central South University
韩龙飞, 许有鹏, 邵玉龙, 石怡. 2013. 城市化对水系结构及其连通性的影响——以秦淮河中、下游为例. 湖泊科学, 25(3): 335–341
Han L F, Xu Y P, Shao Y L and Shi Y. 2013. Effect of urbanization on the stream structure and connectivity——a case study in the mid-lower reaches of the Qinhuai River. Journal of Lake Sciences, 25(3): 335–341 (
黄春龙. 2009. 基于纹理的水系信息提取及其特征分析. 长春: 吉林大学
Huang C L. 2009. Extracting the Information of Water System based on Texture and Analysis of the Water System Features. Changchun: Jilin University
姜浩, 冯敏, 肖桐,王昌佐. 2014. 基于线状特征增强的TM遥感影像细小河流提取方法. 测绘学报, 43(7): 705–710
Jiang H, Feng M, Xiao T and Wang C Z. 2014. A narrow river extraction method based on linear feature enhancement in TM image. ActaGeodaetica et CartographicaSinica, 43(7): 705–710 (
刘桂林, 张落成, 刘剑, 李广宇. 2013. 基于Landsat TM影像的水体信息提取. 中国科学院大学学报, 30(5): 644–650
Liu G L, Zhang L C, Liu J and Li G Y. 2013. Water body information extraction based on Landsat TM remote sensing imagery. Journal of University of Chinese Academy of Sciences, 30(5): 644–650 (
李辉, 代侦勇, 张利华, 蒋志勇. 2011. 利用数学形态学的遥感影像水系提取方法. 武汉大学学报(信息科学版), 36(8): 956–959
Li H, Dai Z Y, Zhang L H and Jiang Z Y. 2011. Mathematical morphology based drainage line extraction from remote sensing image. Geomatics and Information Science of Wuhan University, 36(8): 956–959 (
李小涛, 黄诗峰, 郭怀轩. 2010. 基于纹理特征的SPOT5影像水体提取方法研究. 人民黄河, 32(12): 5–6
Li X T, Huang S F and Guo H X. 2010. A study on extraction water information based on texture feature in SPOT5 image. Yellow River, 32(12): 5–6 (
McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425–1432
谭衢霖, 刘正军, 胡吉平, 杨松林. 2006. 应用多源遥感影像提取鄱阳湖形态参数. 北京交通大学学报, 30(4): 26–30
Tan Q L, Liu Z J, Hu J P and Yang S L. 2006. Measuring lake water level using multi-source remote sensing images combined with hydrological statistical data. Journal of Beijing Jiaotong University, 30(4): 26–30 (
肖艳芳, 赵文吉, 朱琳. 2010. 利用TM影像Band1与Band7提取水体信息. 测绘科学, 35(5): 226–227, 216
Xiao Y F, Zhao W J and Zhu L. 2010. A study on information extraction of water body using band1 and band7 of TM imagery. Science of Surveying and Mapping, 35(5): 226–227, 216 (
Xu H Q. 2006. Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025–3033
闫霈, 张友静, 张元. 2007. 利用增强型水体指数(EWI)和GIS去噪音技术提取半干旱地区水系信息的研究. 遥感信息(6): 62–67
Yan P, Zhang Y J and Zhang Y. 2007. A study on information extraction of water system in semi-arid regions with the enhanced water index (EWI) and GIS based noise remove techniques. Remote Sensing Information(6): 62–67 (
杨存建, 徐美. 1998. 遥感信息机理的水体提取方法的探讨. 地理研究, 17(S): 86–89
Yang C J and Xu M. 1998. Study on the water-body extraction methods of remote sensing information mechanism. Geographic Research, 17(S): 86–89 (
Yang K, Li M C, Liu Y X, Cheng L, Huang Q H and Chen Y M. 2015a. River detection in remotely sensed imagery using Gabor filtering and path opening. Remote Sensing, 7(7): 8779–8802
杨树文, 薛重生, 刘涛, 李轶鲲. 2010. 一种利用TM影像自动提取细小水体的方法. 测绘学报, 39(6): 611–617
Yang S W, Xue C S, Liu T and Li Y K. 2010. A method of small water information automatic extraction from TM remote sensing images. ActaGeodaetica et CartographicaSinica, 39(6): 611–617 (
Yang Y H, Liu Y X, Zhou M X, Zhang S Y, Zhan W F, Sun C and Duan Y W. 2015b. Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sensing of Environment, 171: 14–32
张浩彬, 李俊生, 向南平, 申茜, 张方方. 2015. 基于MODIS地表反射率数据的水体自动提取研究. 遥感技术与应用, 30(6): 1160–1167
Zhang H B, Li J S, Xiang N P, Shen Q and Zhang F F. 2015. A study of extracting water bodies automatically based on the MODIS surface reflectance data. Remote Sensing Technology and Application, 30(6): 1160–1167 (
张继平, 刘林山, 张镱锂, 聂勇, 张学儒, 张琴琴. 2010. 面向对象的极高海拔区水体及冰川信息提取——以珠穆朗玛峰国家级自然保护区核心区为例. 地球信息科学学报, 12(4): 517–523
Zhang J P, Liu L S, Zhang Y L, Nie Y, Zhang X R and Zhang Q Q. 2010. Object-oriented information extraction of water bodies and glaciers in extreme high altitude area: a case study of the core area of Mt. Qomolangma (Everest) National Nature Preserve. Journal of Geo-information Science, 12(4): 517–523 (
张哲, 刘云鹤. 2011. 基于TM影像的水域提取方法研究. 地下水, 33(5): 166–167
Zhang Z and Liu Y H. 2011. Study on extraction of water bodies based on TM imagery. Ground Water, 33(5): 166–167 (
相关作者
相关机构
京公网安备11010802024621