Evaluation of Water Contaminants in Lakes Using Remote Sensing Techniques (Case Study: Lake Zarivar)
Subject Areas :
Fateme dalvand
1
,
Peyman Tahmasebi
2
1 - Department of Water Science and Engineering, Agriculture, Bu- Ali Sina, Hamadan and Iran.
2 -
Keywords: Zaribar Lake, Sentinel-2, Chlorophyll-A concentration, Water quality, Pollution,
Abstract :
Population growth and the discharge of various wastewaters have increased the pollution of water resources, especially surface waters, leading to algal growth, decreased dissolved oxygen, and reduced water quality. Due to the cost and time-consuming nature of field methods, remote sensing technology is used as an efficient method for monitoring water quality. In this study, Sentinel-2 satellite imagery was used to investigate the water quality of Zaribar Lake and identify pollution sources. First, the water body was separated from other features by applying the NDWI index, and then the NDCI index was used to estimate chlorophyll-a concentration, which was evaluated with ground data with R²=0.90 and RMSE=0.07 accuracy. Also, using the random forest machine learning algorithm, the land use of the region was classified into five classes of forest, water body, urban areas, agriculture, and a combination of forest and pasture, which showed an overall accuracy of 92.04% and a kappa coefficient of 88.78%. The results indicated that the coastal areas of the lake have the highest pollution due to the entry of polluted waterways, while the springs at the bottom of the lake have reduced pollution in its center. Also, the eastern part of the lake was more polluted than other parts due to its proximity to agricultural lands, urban wastewater discharge, and recreational activities. The findings showed that the use of Sentinel-2 images and machine learning algorithms is an effective and cost-effective method for assessing the quality of water resources, which enables rapid and accurate monitoring of quality indicators and helps to better manage water resources and reduce pollution.
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