Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

Abstract

As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km2 normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region.

Editorial for Special Issue: “Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors”

“…Additionally, some studies have focused on surface water dynamics monitoring by using multi-temporal remote sensing imagery. For example, Wang et al [31] utilized a 0.5 m resolution remote sensing image of Yiwu city in the Chinese province of Zhejiang to develop a river skeleton line extraction model based on both the DeepLabv3+ deep learning model and the conventional visual interpretation method, which consumes less than 1% of the memory of the classical method (i.e., Zhang and Suen [32] developed raster-based skeleton line extraction algorithms) and improves the computational efficiency by more than 10-fold. Sun et al [33] used multi-temporal Landsat imagery to obtain the lake shoreline of Hulun Lake in various years to further estimate lake water storage changes and analyze water balance.…”

Recent variations of water area in the Middle Atlas lakes (Morocco): A response to drought severity and land use changes

“…The identi cation and mapping of lake water areas (LWA) and the detection of their changes using remote sensing, have drawn signi cant attention from researchers in different domains (Wang et al 2020). Remote sensing technology is widely accepted as an effective and suitable means to extract the evolution of water bodies in various areas and temporal scales (Bastawesy et Wang et al 2023). For this reason, signi cant efforts have been made to develop robust techniques for lake monitoring using available satellite images, such as Landsat (TM, OLI, OLI-2) and Sentinel-2 (Urbanski 2022).…”