多源异构遥感影像的半干旱区洪涝水体识别与变化监测

王燕婷; 杨耘; 刘艳; 程镕杰; 刘文蕾; 廖能; 陈修全

doi:10.13928/j.cnki.wrahe.2025.02.004

水利水电技术（中英文） ›› 2025, Vol. 56 ›› Issue (2) : 45 -58. DOI: 10.13928/j.cnki.wrahe.2025.02.004

复合极端天气气候事件与洪涝灾害机理专栏

多源异构遥感影像的半干旱区洪涝水体识别与变化监测

王燕婷 ¹ ,
杨耘 ¹ ,
刘艳 ²^,³^,⁴^,⁵^,⁶ ,
程镕杰 ¹ ,
刘文蕾 ¹ ,
廖能 ¹ ,
陈修全 ¹

作者信息 +

Flood water body identification and change monitoring in semi-arid areas using multi-source heterogeneous remote sensing images

Yanting WANG ¹ ,
Yun YANG ¹ ,
Yan LIU ²^,³^,⁴^,⁵^,⁶ ,
Rongjie CHENG ¹ ,
Wenlei LIU ¹ ,
Neng LIAO ¹ ,
Xiuquan CHEN ¹

Author information +

文章历史 +

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摘要

【目的】针对西北干旱区洪涝遥感监测中影像时相分辨率低、光谱异质性高导致洪涝区域识别精度不高,无法提取更详细的洪灾变化信息的问题,提出了一种基于空-谱特征融合及多源异构遥感影像关联分析的洪涝区域识别及变化监测方法。【方法】以新疆阿勒泰地区萨尔胡松乡发生的洪灾事件为例,获取了洪涝事件前后Landsat8、HJ-1A、Sentinel-2A和GF-1七个时相的多光谱影像,并从中提取了水体指数(NDWI、NDMBWI、WI2021)以及影像光谱反射率,熵和同质性纹理特征形成多维特征向量,并采用PCA降维;进而,利用多特征融合的随机森林(RF)分类器分别对不同时期的遥感影像进行水体识别。最后,通过比较相邻时相影像的水体识别结果提取了洪涝区域的信息,实现了洪水淹没区域变化过程的动态监测。【结果】经验证,Landsat影像水体提取结果虚警率分别为0.21%、0.28%和0.32%,漏警率为2.17%、3.37%和0.110%,最大洪涝面积为355.1 km²,其中淹没耕地和草地面积分别为134.3 km²、229.2 km²。【结论】结果表明:通过PCA技术实现多特征融合的RF识别算法显著改善了单时相Landsat影像中零散水体识别精度低等问题,水体识别总体精度比NDWI水体指数法提高了13.7%、10.8%和2.03%;采用多源遥感影像数据使监测周期最短可提升至1周,使提取的洪水演变过程信息更详细,弥补了卫星过境时间的不足;此外,洪灾动态变化遥感监测结果与气象、水文观测数据发展趋势基本一致。通过对萨尔胡松乡洪涝水体识别与变化监测,充分展示了在半干旱地区,多源光学遥感影像能够有效地识别水淹区域,为应急救灾提供了重要的数据支持。

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 extracted 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 sensing 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 algorithm with PCA for multi-feature fusion significantly improves the low recognition accuracy of scattered water bodies in single temporal 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 transit 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.