Spatiotemporal Dynamic Analysis of Eutrophication Status Based on Machine Learning-Based Retrieval Algorithm: Case Study in Liangzi Lake, Hubei, China

被引:0
|
作者
Li, Peifeng [1 ]
Hao, Fanghua [2 ]
Wu, Hao [1 ]
Nie, Hanjiang [1 ]
机构
[1] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China
[2] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
eutrophication; trophic level index; machine learning; spatiotemporal change; WATER-QUALITY; VEGETATION INDEXES; SATELLITE DATA; LEAF-AREA; LIGHT;
D O I
10.3390/rs16224192
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R2, RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake's eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management.
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页数:16
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