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Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques
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Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques
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Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques
Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques
Journal Article

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

2023
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Overview
The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO 3 -N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the “fair” status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land ( r  = 0.68, P  < 0.01) and a significant negative association with the built-up area ( r  =  − 0.94, P  < 0.01). To the best of the authors’ knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.