浏览全部资源
扫码关注微信
Published： 07 February 2022 ，
扫 描 看 全 文
刘宇晨，高永年.2022.Sentinel时序影像的长江流域地表水体提取.遥感学报，26（2）： 358-372
Liu Y C and Gao Y N. 2022. Surface water extraction in Yangtze River Basin based on sentinel time series image. National Remote Sensing Bulletin， 26（2）：358-372
传统水体提取算法大多基于某一时期单景遥感影像，无法表现出水体随着时间和空间高度可变的特性，虽然国内外已出现部分时序水体数据产品，但其空间分辨率及水体边界的精度仍无法满足一些研究和应用的需要。本文以地表环境复杂的长江流域为研究区，基于GEE（Google Earth Engine）云平台，使用Sentinel-2 MSI年内长时序影像集结合像元的“时间特征”，提出一种在大尺度环境下更具普适性、可操作性且效果更好的高精度水体提取算法，即基于时序影像数据结合多指数和“时间特征”并融合DEM的算法。该算法选择自动提取水体指数（AWEI）、改进型归一化差值水体指数（MNDWI）、归一化植被指数（NDVI）和增强型植被指数（EVI）进行多指数逻辑组合来提取水体；同时利用NIR波段反射率值和SRTM数字高程模型生成的坡度数据集来辅助抑制高反射率噪声和阴影噪声。通过目视解译采取验证样本点进行全流域水体精度验证，正确提取率达96%以上；在亚像元层面进行精度评估，混合边缘像元占像元总数的3.37%，错分误差0.46%，漏分误差0.21%，表明本文算法对混合像元具有较好的抑制效果；对比传统基于光谱特征的NDWI、MNDWI水体指数，多指数结合时间特征的算法在抑制阴影噪声方面效果更佳；对比现有部分水体数据产品，本文算法在一定程度上既能保证水体区域整体的完整性，也保留了水体的局部细节，在细小水体的提取上具有一定优势。由长江流域水体遥感提取结果可得，流域内水体空间分布不匀，且各水体类型时空变化特征明显，2017年—2020年永久性水体增加的67.41%是由季节性水体转化而来，季节性水体与非水体之间的相互转化最为显著，季节性水体增加的74.64%是由非水体转化而来，同时季节性水体减少的56.25%转化为了非水体。实验结果表明：本研究算法在提取不同时空位置和不同环境下的水体具有一定的普适意义，可有效避免水体与其他地物混合造成的“同物异谱”和“同谱异物”现象，同时对复杂背景噪声有着良好的抑制作用，具有较高的准确度和精度。
Traditional water extraction algorithms are mostly based on single-scene remote sensing image of a certain period and cannot show the highly variable characteristics of water bodies over time and space. Although some time series water products have appeared in China and abroad
their spatial resolution and water boundary accuracy still cannot meet the needs of some studies and applications. This paper takes the Yangtze River Basin with complex surface environment as the research area based on the Google Earth Engine (GEE) cloud platform. The Sentinel-2 MSI annual long time series image sets are combined with the “temporal characteristics” of pixels
and a high-precision water extraction algorithm with more universality
operability
and better effect in large-scale environment is proposed. Specifically
an algorithm based on time series image data is combined with multi-index and “temporal characteristic” fusion Digital Elevation Model (DEM). This algorithm selects automated water extraction index
Modified Normalized Difference Water Index (MNDWI)
normalized difference vegetation index
and enhanced vegetation index for multi-index logical combination to extract water bodies. Near-infrared band reflectivity value and slope data set generated by SRTM DEM are used to assist in suppressing high reflectivity noise and shadow noise. The accuracy of water bodies in the whole basin is verified with the validation sample points
and the correct extraction rate is more than 96% through visual interpretation. The accuracy evaluation at the subpixel level shows that the mixed edge pixels account for 3.37% of the total pixels
the misclassification error is 0.46%
and the omission error is 0.21%
indicating that the proposed algorithm has a good inhibitory effect on the mixed pixels. Compared with the traditional NDWI and MNDWI water index based on spectral characteristics
the multi-index combined with temporal characteristic algorithm has better effect in suppressing shadow noise. Compared with some existing water products
the proposed algorithm can ensure the integrity of the whole water area and retain the local details of the water body. It has certain advantages in the extraction of small water bodies. Results of the remote sensing extraction of water bodies in the Yangtze River Basin show that the spatial distribution of water bodies in the basin is uneven
and the temporal and spatial changes in various water body types are obvious. From 2017 to 2020
67.41% of the increase in permanent water bodies is transformed from seasonal water bodies
and the mutual conversion between seasonal water bodies and nonwater bodies is the most obvious. In addition
74.64% of the increase in seasonal water bodies is converted from nonwater bodies
and 56.25% of the decrease in seasonal water bodies is converted to nonwater bodies. Experimental results show that the proposed algorithm has a certain universal importance in extracting water bodies in different spatiotemporal locations and different environments and can effectively avoid the phenomenon of “the same objects with different spectra” and “the same spectra with different objects” caused by the mixing of water and other ground objects. This algorithm has a good inhibitory effect on complex background noise and has high accuracy and precision.
GEESentinel-2水体遥感提取时间特征多指数组合阴影噪声
Google Earth Engine(GEE)Sentinel-2remote sensing extraction of water bodiestemporal characteristicsmulti-index combinationshadow noise
Barton I J and Bathols J M. 1989. Monitoring floods with AVHRR. Remote Sensing of Environment, 30(1): 89-94 [DOI: 10.1016/0034-4257(89)90050-3http://dx.doi.org/10.1016/0034-4257(89)90050-3]
Buchhorn M, Lesiv M, Tsendbazar N E, Herold M, Bertels L and Smets B. 2020. Copernicus global land cover layers—collection 2. Remote Sensing, 12(6): 1044 [DOI: 10.3390/rs12061044http://dx.doi.org/10.3390/rs12061044]
Chen H F, Wang J L, Chen Z, Yang L and Xi W J. 2004. Comparison of water extraction methods in Mountainous Plateau region from TM image. Remote Sensing Technology and Application, 19(6): 479-484
陈华芳, 王金亮, 陈忠, 杨柳, 席武俊. 2004. 山地高原地区TM影像水体信息提取方法比较——以香格里拉县部分地区为例. 遥感技术与应用, 19(6): 479-484 [DOI: 10.3969/j.issn.1004-0323.2004.06.009http://dx.doi.org/10.3969/j.issn.1004-0323.2004.06.009]
D’Andrimont R, Lemoine G and Van der Velde M. 2018. Targeted grassland monitoring at parcel level using sentinels, street-level images and field observations. Remote Sensing, 10(8): 1300 [DOI: 10.3390/rs10081300http://dx.doi.org/10.3390/rs10081300]
Deng Y, Jiang W G, Tang Z H, Ling Z Y and Wu Z F. 2019. Long-term changes of open-surface water bodies in the Yangtze River Basin based on the google earth engine cloud platform. Remote Sensing, 11(19):2213 [DOI: 10.3390/rs11192213http://dx.doi.org/10.3390/rs11192213]
Du Y, Zhang Y H, Ling F, Wang Q M, Li W B and Li X D. 2016. Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4): 354 [DOI: 10.3390/rs8040354http://dx.doi.org/10.3390/rs8040354]
Farr T G, Rosen P A, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D and Alsdorf D. 2007. The shuttle radar topography mission. Reviews of Geophysics, 45(2): RG2004) [DOI: 10.1029/2005RG000183http://dx.doi.org/10.1029/2005RG000183]
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 [DOI: 10.1016/j.rse.2013.08.029http://dx.doi.org/10.1016/j.rse.2013.08.029]
Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji L Y, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Ping Suen H, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]
Gu X Z, Zeng Q W, Shen H, Chen E X, Zhao L, Yu F and Tu K. 2019. Study on water information extraction using domestic GF-3 image. International Journal of Remote Sensing, 23(3): 555-566
谷鑫志, 曾庆伟, 谌华, 陈尔学, 赵磊, 于飞, 涂宽. 2019. 高分三号影像水体信息提取. 遥感学报, 23(3): 555-566 [DOI: 10.11834/jrs.20198171http://dx.doi.org/10.11834/jrs.20198171]
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213 [DOI: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2]
Jiang H, Feng M, Zhu Y Q, Lu N, Huang J X and Xiao T. 2014. An automated method for extracting rivers and lakes from Landsat imagery. Remote Sensing, 6(6): 5067-5089 [DOI: 10.3390/rs6065067http://dx.doi.org/10.3390/rs6065067]
Jupp D L B, Mayo K K, Kuchler D A, Claasen D V R, Kenchington R A and Guerin P R. 1985. Remote sensing for planning and managing the great barrier reef of Australia. Photogrammetria, 40(1): 21-42 [DOI: 10.1016/0031-8663(85)90043-2http://dx.doi.org/10.1016/0031-8663(85)90043-2]
Kaplan G and Avdan U. 2017. Object-based water body extraction model using Sentinel-2 satellite imagery. European Journal of Remote Sensing, 50(1): 137-143 [DOI: 10.1080/22797254.2017.1297540http://dx.doi.org/10.1080/22797254.2017.1297540]
Li Y, Niu Z G, Xu Z Y and Yan X. 2020. Construction of high spatial-temporal water body dataset in China based on sentinel-1 archives and GEE. Remote Sensing, 12(15): 2413 [DOI: 10.3390/rs12152413http://dx.doi.org/10.3390/rs12152413]
Lillesand T M, Kiefer R W and Chipman J W. 2015. Remote Sensing and Image Interpretation. 7th ed. Hoboken: John Wiley and Sons.
Lu J J and Li S H. 1992. Improvement of the techniques for distinguishing water bodies from TM Data. National Remote Sensing Bulletin, 7(1): 17-23
陆家驹, 李士鸿. 1992. TM资料水体识别技术的改进. 环境遥感, 7(1): 17-23
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 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Moêller-Jensen L. 1990. Knowledge-based classification of an urban area using texture and context information in Landsat-TM imagery. Photogrammetric Engineering and Remote Sensing, 56(6): 899-904
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422 [DOI: 10.1038/nature20584http://dx.doi.org/10.1038/nature20584]
Rouse Jr J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS//Proceedings of 3rd Earth Resources Technology Satellite-1 Symposium. Greenbelt: NASA: 309
Soti V, Tran A, Bailly J S, Puech C, Lo Seen D and Bégué A. 2009. Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas. International Journal of Applied Earth Observation and Geoinformation, 11(5): 344-351 [DOI: 10.1016/j.jag.2009.05.005http://dx.doi.org/10.1016/j.jag.2009.05.005]
Sun G Y, Huang H, Weng Q H, Zhang A Z, Jia X P, Ren J C, Sun L and Chen X L. 2019. Combinational shadow index for building shadow extraction in urban areas from sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 78: 53-65 [DOI: 10.1016/j.jag.2019.01.012http://dx.doi.org/10.1016/j.jag.2019.01.012]
Wang G H, Pei L, Du Q Y and Li X. 2020. Spectral relation method for water body extraction from ZY-3 imagery. Remote Sensing Information, 35(3): 117-121
王国华, 裴亮, 杜全叶, 李旋. 2020. 针对资源三号卫星影像水体提取的谱间关系法. 遥感信息, 35(3): 117-121 [DOI: 10.3969/j.issn.1000-3177.2020.03.016http://dx.doi.org/10.3969/j.issn.1000-3177.2020.03.016]
Wang Z F, Liu J G, Li J B and Zhang D D. 2018. Multi-spectral water index (MuWI): a native 10-m multi-spectral water index for accurate water mapping on Sentinel-2. Remote Sensing, 10(10): 1643 [DOI: 10.3390/rs10101643http://dx.doi.org/10.3390/rs10101643]
Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5): 589-595
徐涵秋. 2005. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究. 遥感学报, 9(5): 589-595
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 [DOI: 10.1080/01431160600589179http://dx.doi.org/10.1080/01431160600589179]
Yan M C. 2005. Extraction of information about water area change in Nanjing City in the past ten years based on TM data. Water Resources Protection, 21(6): 31-33
颜梅春. 2005. 基于TM数据的水域变化信息提取研究. 水资源保护, 21(6): 31-33 [DOI: 10.3969/j.issn.1004-6933.2005.06.008http://dx.doi.org/10.3969/j.issn.1004-6933.2005.06.008]
Yang X C, Chen Y and Wang J Z. 2020a. Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine. Remote Sensing Letters, 11(7): 687-696 [DOI: 10.1080/2150704x.2020.1757780http://dx.doi.org/10.1080/2150704x.2020.1757780]
Yang X C, Qin Q M, Grussenmeyer P and Koehl M. 2018. Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing of Environment, 219: 259-270 [DOI: 10.1016/j.rse.2018.09.016http://dx.doi.org/10.1016/j.rse.2018.09.016]
Yang X C, Qin Q M, Yésou H, Ledauphin T, Koehl M, Grussenmeyer P and Zhu Z. 2020b. Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. Remote Sensing of Environment, 244: 111803 [DOI: 10.1016/j.rse.2020.111803http://dx.doi.org/10.1016/j.rse.2020.111803]
Zou Z H, Xiao X M, Dong J W, Qin Y W, Doughty R B, Menarguez M A, Zhang G L and Wang J. 2018. Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016. Proceedings of the National Academy of Sciences of the United States of America, 115(15): 3810-3815 [DOI: 10.1073/pnas.1719275115http://dx.doi.org/10.1073/pnas.1719275115]
相关文章
相关作者
相关机构
京公网安备11010802024621