ANNOUNCEMENTS
The Yamuna River, which is among the most vital tributaries of the Ganges in India, has experienced considerable morphological changes over the past few decades, especially in the Delhi region. The present research focuses on identifying and measuring the lateral migration of the river between 1994 and 2024 based on multi-temporal Landsat satellite images and geospatial tools. Utilizing the ability of the Digital Shoreline Analysis System (DSAS) in ArcGIS and applying machine learning techniques for land use classification, this study assesses changes via measurements of Net River Migration (NRM), End Point Rate (EPR), and Linear Regression Rate (LRR) over 1,000 transects.
The results indicate prominent migration areas around Akshardham, Rajghat, and Bhalswa with mean shifts of about 135 meters and a maximum shift of 410 meters. The interaction of urban encroachment, geomorphic processes, and hydrological alterations is identified as key factors for channel instability by analysis. The research shows the effectiveness of remote sensing and machine learning techniques in urban river monitoring and suggests integrated management plans for urban resilience, ecological restoration, and sustainable planning.
Keywords: Yamuna River, Digital Shoreline Analysis System (DSAS), Net River Migration (NRM), End Point Rate (EPR), Linear Regression Rate (LRR), Land Use Land Cover (LULC)
