Retraction Note: Research on non-point source pollution and prevention countermeasures of river basin based on remote sensing and GIS

Zheng, Gengbin; Cui, Xiong; Li, Shiyao; Zhu, Feiyun; Li, Shuangjiang

doi:10.1007/s12517-021-09327-x

Cited by 1 publication

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“…Remote‐sensing technology enables long‐term, large‐scale and high‐efficiency monitoring. The data of remote‐sensing image must be preprocessed, spliced and cut, and information extracted in traditional way, and these steps must be repeated for each image with low efficiency (Al‐Hameedawi, 2022; Zheng et al, 2021). The cloud geospatial analysis platform of Google Earth Engine (GEE) integrates data acquisition, image processing, computational analysis and spatial visualization.…”

Section: Introductionmentioning

confidence: 99%

The study on the comparative responses of aquatic vegetation to the interannual water level variation in different hydrologically connected sub‐lakes based on GEE technology

et al. 2022

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Water level variation is considered as a main controlling environment factor affecting the growth of aquatic vegetation in Poyang Lake, the largest freshwater lake in China. It is significant to study the influences of water level change on aquatic vegetation under various hydrological conditions. Taking the free connected sub-lake Bang Lake and partially controlled sub-lake Dahuchi Lake of Poyang Lake as research objects and based on the cloud computing platform of remote sensing of Google Earth Engine (GEE), the pixel binary model method was used to estimate the aquatic vegetation cover from 2000 to 2019, and the spatial and temporal variation characteristics were discovered first. Meanwhile, the combined method of Sen + M-K (Theil-Sen Median method and Mann-Kendall trend test) was conducted to simulate the trends of water fluctuation. Second, the hydrological parameters indicating water level fluctuation were established, and the relationship between these parameters and the area covered by aquatic vegetation in different sub-lakes was explored. The results were as follows. (1) The aquatic vegetation cover of the freely connected sublake Bang Lake was more susceptible to water level changes, while the partially controlled sub-lake Dahuchi Lake was relatively stable underwater level change. (2) The aquatic vegetation was patchily and sporadically distributed in years with low vegetation cover, while in the years with high vegetation cover, it was distributed in a ringlike pattern, spreading from the centre of the lake to the shore. (3) The aquatic vegetation cover of the freely connected sub-lake Bang Lake was more likely to be influenced by the water level fluctuation (WLF), while the aquatic vegetation cover of the partially controlled sub-lake Dahuchi Lake was more likely to be influenced by the flooding duration of 17 m water level (L17). The flooding duration of 19 m water level (L19) had a strong negative correlation with the predicted aquatic vegetation cover of both Bang Lake and Dahuchi Lake. It suggests that there was a certain degree of time lag in the effect of water level changes on the free connected sublakes, but there was no obvious time lag on the partially controlled sub-lakes. (4) The aquatic vegetation of the freely connected sub-lake Bang Lake were dominated by stabilization and slight improvement, while aquatic vegetation in the partially controlled sub-lake Dahuchi Lake were dominated by stabilization and significant

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Section: Introductionmentioning

confidence: 99%