Suspended Sediment Concentrate Estimation From Landsat Imagery and Hydrological Station in Poyang Lake Using Machine Learning

• Published in: IEEE access Vol. 12; pp. 85411 - 85422
• Main Authors: Liao, Kaitao, Song, Yuejun, Nie, Xiaofei, Liu, Lingjia, Qi, Shuhua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Artificial satellites; Back propagation networks; Dredgers; Earth; Government regulations; Hydrology; Lake management; Lakes; Landsat; Landsat 5; Landsat satellites; Machine learning; Mining industry; Nature reserves; Neural networks; Poyang Lake; Regression analysis; Regression models; Regression tree analysis; Remote sensing; Sand; Sand & gravel; sand mining; Satellite imagery; Sediment transport; Sediments; Support vector machines; suspended sediment; Suspended sediments; Water quality; Poyang Lake
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3414996

Poyang Lake is not only a globally important stopover for migratory birds and a habitat for fish, but also one of the main sand mining areas in China. However, sand mining activities significantly affect the suspended sediment concentration in Poyang Lake, thereby impacting water quality, altering lake bed topography, and potentially disturbing the living environment of plants and animals. Therefore, understanding the suspended sediment concentration and the spatiotemporal patterns of sand mining activities in Poyang Lake holds vital significance for effective lake management and biodiversity conservation. Utilizing surface reflectance (SR) data derived from Landsat satellite imagery sourced via Google Earth Engine spanning from 1989 to 2018, coupled with SSC data recorded by daily observation at Hukou hydrological stations, we conducted a comparative analysis of five distinct methods: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Back Propagation Neural Network (BPNN). Moreover, the applicability of machine learning methods was analyzed and compared across four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04, Landsat 5, 7, and 8). Using the optimal SSC estimation model, we investigated the spatial and temporal trends of sand mining activities in Poyang Lake from 1989 to 2021, along with their potential impact on suspended sediment levels in the water body. The results showed that the prediction accuracy of machine learning is better than that of linear regression models, and the RF model has the best performance. The RF model generated R<inline-formula> <tex-math notation="LaTeX">^{2} \gt 0.9 </tex-math></inline-formula> for four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04 Landsat 5, 7, and 8) and showed little to no overfitting. The generated SSC map can clearly show the distribution of SSC in Poyang Lake, unveiling the impact of sand mining activities on suspended sediment, especially around sand dredgers where SSC can exceed 0.15 g/L. Sand mining activities in Poyang Lake emerged after 2000 and gradually shifted southward and expanded, reaching a peak in 2016. Fortunately, under government regulation, illegal sand mining has been effectively controlled and is currently concentrated near Songmenshan Island. Despite the fact that sand mining has been brought under control, there remains a necessity for heightened oversight to prevent any impact on national nature reserves. The Random Forest (RF) model demonstrates significant potential in utilizing Landsat satellite data to predict SSC in Poyang Lake, as well as to monitor sand mining activities in the area.