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全文摘要次数: 154 全文下载次数: 81
引用本文:

DOI:

10.11834/jrs.20243275

收稿日期:

2023-07-01

修改日期:

2023-12-20

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基于多要素耦合的长江中下游地区地表水体提取与变化分析
郭山川, 杜培军, 夏子龙, 方宏, 唐鹏飞
南京大学 地理与海洋科学学院
摘要:

全球气候变化和水土资源长期高强度利用背景下,长江中下游地区地表水体变化突显复杂性、极端性、破坏性,加剧了区域地表水资源遥感调查与可持续管理的挑战。针对区域地类复杂、结构破碎、要素特征不一致等因素带来的地表水体遥感提取难题,研究构建了面向复杂地理条件的多要素耦合和水体大范围精细监测方法。该方法通过挖掘复杂背景不同地物间的关键影像特征,发展了植被-地表水体耦合的增强型遥感水体指数。结合地形、水文、不透水面等基础地理信息数据,构建了多源地理空间数据融合的水体自动分层提取规则。利用地表水体季节变化特征,实现了水体识别频率引导的稳定水体、变化水体分类。利用提出方法和Google Earth Engine(GEE)平台,提取了长江中下游地区1984–2020年30 m空间分辨率稳定水体与季节水体分布范围,研究并揭示了长江中下游地区地表水体时空分布及其变化趋势特征。面向大范围、长时序的地表水体遥感时序监测方法及数据成果,有助于提高对地表水时空分布、演变过程及其环境效应的认识,可为地表水资源时空调查、配置优化、统筹开发、灾害风险评估及预警提供空间数据和技术支撑。

Remote sensing extraction and change analysis of surface water body over the Middle and Lower Reaches of the Yangtze River based on a multi-element coupling framework
Abstract:

Background: Under global climate change and long-term high-intensity exploitation of resources, surface water body in the Middle and Lower Reaches of the Yangtze River Region (MLRYRR) exhibits to be more complex, extreme, and hazardous in recent decades. However, technical inadequacy in remote sensing water extraction still exists caused by the complexity of regional land cover, fragmented structures, and inconsistent feature characteristics. Objective: This paper aims to propose a framework combining multi-source data fusion to extract water body against complex geographical conditions at a basin scale, and accordingly show the spatiotemporal pattern of surface water body over the MLRYRR. Method: Firstly, spectral features of several land objects with water-like spectrum were explored based on multi-spectral remote sensing images. An enhanced remote sensing water index for large spatial and temporal scales water extraction was introduced, which can differentiate water bodies with aquatic plant and vegetation in subtropical regions more effectively. Secondly, the proposed index was incorporated into an automatic water extraction model by decision-level fusion of multi-source geographic information data (i.e., topography, hydrology, and impervious surfaces). Thirdly, considering the seasonal variation of surface water bodies, a frequency-based classification scheme was then introduced to estimate the yearly distribution of stable and seasonal water at 30 m spatial resolution in the MLRYRR from 1984 to 2020. Result: Based on the proposed framework and Google Earth Engine platform, annual spatial distribution data of stable water bodies and seasonal water bodies at a spatial resolution of 30 meters in the MLRYRR from 1984 to 2020 were obtained. The produced data was validated by a total of 9000 validation samples in different scenes (e.g. urban scene, agricultural scene, and lacustrine scene) and achieved a 98.4% recall. The results showed the spatio-temporal distribution of surface water and their trends demonstrate regional heterogeneity, where water area in Jiangsu and Zhejiang provinces were expanding at 35.1 km2 yr-1 and 6.5 km2 yr-1, respectively, and water area in Anhui, Jiangxi, and Hunan provinces was reducing at 46.53 km2 yr-1, 35.6 km2 yr-1, and 26 km2 yr-1, respectively. Besides, the results of the annual water body area in the MLRYRR can reflect the drought and flood situation in different watersheds spatially. The change trends of water area in Hubei Province and Shanghai were insignificant. The mode and intensity of human disturbance and geo-climatic factors were the driving factors of the pattern differentiation of water evolution. Conclusion: The proposed surface water extraction framework and data results contribute to improving our understanding of the spatial-temporal distribution, evolution processes, and environmental effects of surface water. The results can provide spatial data and monitoring techniques to support surface water resource spatial investigation, optimization of resource allocation, coordinated development, disaster risk assessment, and early warning. Future studies will focus on the dynamic observation method on surface water bodies through collaborative processing of optical and SAR images to break through limitations from continuously cloudy and rainy conditions in subtropical regions.

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