基于GEE的中国西南地区水体变化分析
Analysis of Water Body Change in Southwest China Based on Google Earth Engine
摘要: 湖泊河流等水体是区域气候和环境变化的敏感指示器，其面积变化能客观反映内陆流域内的水量平衡过程，基于遥感大数据平台，快速提取水体变化信息，分析影响因素分析，具有较重要的研究意义。研究以Landsat数据与MODIS数据为数据源，通过水体指数模型与随机森林分类法进行西南地区的水体提取与分析，结果表明：(1) Landsat数据水体提取的效果好于MODIS数据，误提率与过提率均低于MODIS数据，Landsat数据水体指数模型方法提取水体的总体精度为86.1%，误提率为16.8%，过提率为11.0%，而MODIS数据水体提取模型的总体精度为81.9%，误提率为21.8%，过提率为14.4%；(2) 水体指数模型法所提取水体效果较随机森林分类法精度更高，随机森林方法在Landsat数据下总体精度为85.7%，误提率与19.2%，高于水体提取模型法的16.8%，但过提率为9.4%，较水体指数模型法低；(3) 基于Landsat数据通过M-K趋势检验法得出了西南地区2000~2020年水体面积呈现显著增加的趋势，总面积从2000年的28963.14平方公里增长到2020年的33859.17平方公里，水体主要分布于青藏高原地区与云贵高原地区湖泊与其发育的众多河流的中上游流域范围内。
Abstract: Lakes, rivers, and other water bodies are sensitive indicators of regional climate and environmental change. Their area variations can objectively reflect the water balance processes within inland basins. Based on a big data platform for remote sensing, rapidly extracting information on water body changes and analyzing the influencing factors holds significant research importance. Taking Landsat data and MODIS data as data sources, the water body in Southwest China is extracted and analyzed using the water body index model and random forest classification. The results show that: (1) the effect of water body extraction from Landsat data is better than MODIS data, and the false extraction rate and over extraction rate are lower than MODIS data. The overall accuracy of water body extraction from Landsat data is 86.1%, the false extraction rate is 16.8%, and the over extraction rate is 11.0%. The overall accuracy of MODIS data water extraction model is 81.9%, the false extraction rate is 21.8%, and the over extraction rate is 14.4%; (2) The effect of water body extracted by water body index model method is higher than that of random forest classification method. Under Landsat data, the overall accuracy of the random forest method is 85.7%, and the false extraction rate is 19.2%, which is higher than 16.8% of water body extraction model method, but the over extraction rate is 9.4%, which is lower than that of water body index model method; (3) The M-K trend test method shows that the water area in Southwest China shows a significant increase trend based on Landsat data from 2000 to 2020, with the total area increasing from 28963.14 km2 in 2000 to 33859.17 km2 in 2020. The water body is mainly distributed in the lakes in the Qinghai-Xizang Plateau and the Yunnan-Kweichow Plateau and lots of middle and upper reaches of the river basin.
文章引用：刘缘. 基于GEE的中国西南地区水体变化分析[J]. 地理科学研究, 2025, 14(2): 197-209. https://doi.org/10.12677/gser.2025.142020

参考文献

[1] 马艳敏, 郭春明, 王颖, 等. 吉林省西部主要水体面积动态变化遥感监测[J]. 水土保持通报, 2018, 38(5): 249-255.
[2] Chen, Q.L., Zhang, Y.Z., Ekroos, A. and Hallikainen, M. (2004) The Role of Remote Sensing Technology in the EU Water Framework Directive (WFD). Environmental Science & Policy, 7, 267-276. [Google Scholar] [CrossRef] 
[3] 刘清, 吴君峰, 王浩, 等. 基于Google Earth Engine云平台的黑龙江流域长时序常年和季节性水面提取及变化分析[J]. 环境工程, 2021, 39(1): 80-88.
[4] 刘垚燚, 田恬, 曾鹏, 等. 基于Google Earth Engine平台的1984-2018年太湖水域变化特征[J]. 应用生态学报, 2020, 31(9): 3163-3172.
[5] 唐海龙. 中亚地区湖泊水体变化遥感监测及影响因素分析[D]: [硕士学位论文]. 成都: 成都理工大学, 2020.
[6] Work, JR., E.A. and Gilmer, D.S. (1976) Utilization of Satellite Data for Inventorying Prairie Ponds and Lakes. Photogrammetric Engineering and Remote Sensing, 42, 685-694.
[7] Klein, I., Dietz, A.J., Gessner, U., Galayeva, A., Myrzakhmetov, A. and Kuenzer, C. (2014) Evaluation of Seasonal Water Body Extents in Central Asia over the Past 27 Years Derived from Medium-Resolution Remote Sensing Data. International Journal of Applied Earth Observation and Geoinformation, 26, 335-349. [Google Scholar] [CrossRef] 
[8] 赵金玉. 近三十年淮河流域地表水时空演变遥感监测研究[D]: [硕士学位论文]. 开封: 河南大学, 2020.
[9] 周成虎, 骆剑承, 杨晓梅, 等. 遥感影像地学理解与分析[M]. 北京: 科学出版社, 2001.
[10] 张明华. 用改进的谱间关系模型提取极高山地区水体信息[J]. 地理与地理信息科学, 2008, 24(2): 14-16+22.
[11] 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, 1425-1432. [Google Scholar] [CrossRef] 
[12] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005, 9(5): 589-595.
[13] 张德军, 杨世琦, 王永前, 郑伟. 基于GF-1数据的三峡库区水体信息精细化提取[J]. 人民长江, 2019, 50(9): 233-239.
[14] Hu, W.J., Liu, H.L., Bao, A.M. and El-Tantawi, A.M. (2018) Influences of Environmental Changes on Water Storage Variations in Central Asia. Journal of Geographical Sciences, 28, 985-1000. [Google Scholar] [CrossRef] 
[15] 中华人民共和国水利部. 2019年中国水资源公报[R]. 北京: 中华人民共和国水利资源部, 2019.
[16] 王佃来, 刘文萍, 黄心渊. 基于Sen + Mann-Kendall的北京植被变化趋势分析[J]. 计算机工程与应用, 2013, 49(5): 13-17.
[17] 张东艳, 吴运卿, 李妮. 基于Mann-Kendall检验的尼洋河流域水文变量演变趋势分析[J]. 中国农村水利水电, 2017(12): 86-89.
[18] 杨春艳, 沈渭寿, 林乃峰. 西藏高原近50年气温和降水时空变化特征研究[J]. 干旱区资源与环境, 2013, 27(12): 167-172.
[19] Zhang, G.Q., Luo, W., Chen, W.F. and Zheng, G.X. (2019) A Robust but Variable Lake Expansion on the Tibetan Plateau. Science Bulletin, 64, 1306-1309. [Google Scholar] [CrossRef] [PubMed]
[20] Sun, X.D., Cao, B., Pan, B.T., Li, K.J., Zhao, X.R. and Guan, W.J. (2022) Identification of Hazardous Glacial Lakes in the Yarlung Zangbo River Basin Based on Lakes Changes Determined Using Google Earth Engine. Frontiers in Earth Science, 10, Article 825482. [Google Scholar] [CrossRef] 

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