水利水电技术（中英文）2025，Vol.56Issue(2)：45-58,14.DOI:10.13928/j.cnki.wrahe.2025.02.004
多源异构遥感影像的半干旱区洪涝水体识别与变化监测
Flood water body identification and change monitoring in semi-arid areas using multi-source heterogeneous remote sensing images
摘要
Abstract
[Objective]In order to solve the problem that the low temporal resolution and high spectral heterogeneity of remote sensing images in flood monitoring in arid areas of northwest China lead to low recognition accuracy of flood areas and inability to extract more detailed flood change information,a flood area recognition and change monitoring method based on spatial-spectral feature fusion and multi-source heterogeneous remote sensing images correlation analysis is proposed.[Methods]Taking the flood event in Sarhusong Township,Altay Prefecture,Xinjiang as an example,seven temporal multispectral images of Landsat8,HJ-1A,Sentinel-2A and GF-1 before and after the flood event are obtained.Then multi-dimensional feature vectors,including water index(NDWI,NDMBWI,WI2021)of image spectral reflectance,entropy,homogeneity texture features and as well as are ex-tracted from them.PCA technology is used to reduce feature dimension;Finally,the random forest(RF)classifier is used to fuse multi-dimensional spatial-spectral features so as to identify water bodies and to recognize the flooding areas from remote sens-ing images acquired in every periods.After comparing the water body recognition result of adjacent temporal images,the dynamic change information of flood submerged areas is obtain.[Results]Through experimental verification,the result indicate that the false alarm rates for water extraction from Landsat images are 0.21％,0.28％,and 0.32％,with corresponding the miss rates of 2.17％,3.37％,and 0.110％.The maximum flooded area is 355.1 km2,with submerged farmland and grassland covering areas of 134.3 km2 and 229.2 km2,respectively.[Conclusion]The following conclusion can be obtained that the RF recognition algo-rithm with PCA for multi-feature fusion significantly improves the low recognition accuracy of scattered water bodies in single tem-poral Landsat8 images,and the overall accuracy of water body recognition is 13.7％,10.8％and 2.03％,higher than that of NDWI water body index method;The use of multi-source remote sensing image data makes the monitoring cycle as short as 1 week,which makes the extracted flood evolution process information more detailed and makes up for the shortage of satellite tran-sit time;In addition,the remote sensing monitoring result of flood dynamic changes are basically consistent with the development trend of meteorological and hydrological observation data.Through the identification and change monitoring of flood water bodies in Sarhusong Township,it is fully demonstrated that in semi-arid areas,multi-source optical remote sensing images can effectively identify flooded areas,providing important data support for emergency disaster relief.关键词
多源遥感影像/水体识别/空-谱特征融合/随机森林/时空变化分析/洪灾监测/洪水/气候变化Key words
multi-source remote sensing images/water body identification/spatial-spectrum features fusion/random forest/time-spatial change analysis/flood disaster monitoring/flood/climate change分类
测绘与仪器引用本文复制引用
王燕婷,杨耘,刘艳,程镕杰,刘文蕾,廖能,陈修全..多源异构遥感影像的半干旱区洪涝水体识别与变化监测[J].水利水电技术（中英文）,2025,56(2):45-58,14.基金项目
"天山英才"培养计划(2023TSTCCX0079) （2023TSTCCX0079）
陕西省自然科学基础研究计划项目(2022JM-163) （2022JM-163）
中央高校基本科研业务费项目(300102269205) （300102269205）
