Machine Learning-Based Spatial Assessment of the Impact of Urbanization and LULC Dynamics on Urban Ecological Health in the Kolkata Metropolitan Area

Rapid urbanisation and land use transformation pose significant threats to long-term ecological sustainability, particularly in the eco-environmental conditions of the fastest-growing metropolitan areas in developing nations. The Kolkata metropolitan area has posed a critical ecological health quality over the past three decades due to fast-growing urbanisation and human activities. The study aims to assess the impact of urbanization, land use land cover (LULC) dynamics on the ecological health quality of the Kolkata Metropolitan Area (KMA). Multidate Landsat5 TM for 1990, 2000, and 2010, and Landsat8 OLI for 2020 were utilised in this study. First, four Machine Learning (ML) algorithms were tested, and the best algorithm was used to generate the LULC maps from 1990 to 2020. Land use transition and the estimation of the 2030 projection of LULC were carried out using Cellular Automata-Artificial Neural Network. Second, multidate Remote Sensing-based Ecological Health Index (RSEHI) was developed by integrating four spectral indices and the Analytic Hierarchy Process to evaluate the spatiotemporal variability of the quality of ecological health. Lastly, multivariate fractional regression was used to predict the RESHI and it establish comprehensive assessment of how urbanization and LULC changes directly influence the ecological health quality of the KMA. The results indicate a consistent decline in ecological health due to rapid urban expansion. The built-up area experienced a significant increase, strongly and negatively correlated (r = -0.857) with RSEHI, whereas vegetation cover shows perfect and positive correlation (r = 0.994), and is identified as the most promising factor, which is rapidly declining over time. The city centre resulting a zone with low ecological health quality, expected to expand to 1032.27 km² (57.51 This graphical abstract illustrated the integrated methodology adopted for spatial assessment of urbanization and land use land cover dynamics on urban ecological health in the Kolkata Metropolitan Area (KMA) using machine learning algorithms and geospatial techniques. The investigation commenced with acquisition of the multidate Landsat imagery in specifically multidate Landsat5 TM for 1990, 2000, and 2010, and Landsat8 OLI for 2020 were utilized for the study. Following this, four Machine learning (ML) algorithms such as SVM, RF, DT and KNN are used for the LULC classification from 1990 to 2020. On the other hand, multidate spectral indices including NDVI, MNDWI, NDBSI and LST are generated using the Landsat imagery from 1990 to 2020. The integration of these four spectral indices has led to development multidate Remote Sensing-based Ecological Health Index (RSEHI) Analytic Hierarchy Process (AHP), which shows the spatiotemporal variability of the quality of ecological health in the KMA. The future simulation of LULC is conducted through Cellular Automata-Artificial Neural Network, while RESHI future prediction utilizes multivariate fractional regression techniques. The correlation matrix illustrates the relationship between LULC dynamics and RSEHI in KMA. The finding demonstrated a steady decoration in ecological health as a result of rapid urban expansion where built-up area experienced a significant increase and vegetation covers rapidly declining over time. It endorses the immediate need for sustainable ecological management strategies to address rapid ecological degradation and enhance resilience in rapidly urbanising landscapes. Machine learning and geospatial models demonstrate the rapid urbanization and large-scale spatiotemporal dynamics of land use and land cover in the Kolkata Metropolitan Area. The Remote Sensing-based Ecological Health Index (RSEHI) shows robustness in quantifying the quality of ecological health of the KMA. Urbanization and land use and land cover dynamics are strongly and inversely correlated with ecological health. The low ecological health zone is expected to increase to 1032.27 km² reflecting a significant deterioration in the ecological sustainability of the KMA till 2030.