Spatiotemporal Assessment of Ecosystem Services in a Tier-II Indian City: A Case Study of Visakhapatnam (1995–2022)

Ecosystem services (ESs) represent the essential ecological contributions that support human well-being and socioeconomic subsistence. This study employs multi-temporal remote sensing (RS) datasets from 1995 - 2022 to quantify the Ecosystem Service Value (ESV) of key ecosystem functions within a representative Tier-2 Indian city. Land Use/Land Cover (LULC) classification is performed using a Random Forest (RF) supervised machine learning algorithm to delineate ecosystem units, producing high-precision classification results with strong overall accuracy and optimized Kappa coefficients. Valuation is conducted using benefit transfer methods, with values expressed in million US dollars per year. The results indicate that, after vegetative cover, built-up areas, croplands, waterbodies, and barren land are the next major contributors to the total ESV. The key findings of the study are that Vishakapatnam, Tier-2 city in India is highly sensitive to LULC transitions, where rapid urbanization significantly alters the trajectory of provisioning, supporting, regulatory, and cultural ecosystem services. In addition, the study examines spatio-temporal relationships between ecosystem service trade-offs and synergies, demonstrating that high-resolution ESV mapping serves as a reliable diagnostic tool for assessing the impacts of human overexploitation and poor resource management. Overall, the study provides a robust quantitative framework for ecological valuation, offering a critical foundation for evidence-based policy interventions and sustainable urban planning in rapidly transforming urban environments.