Carbon-Stock Estimation Using High-Resolution Remote Sensing Imagery at Universitas Padjadjaran: A Spatial–Temporal Analysis to Support Sustainable and Green Campus Initiatives

Estimating carbon stocks in semi-urban ecosystems remains challenging due to spatial heterogeneity and the scale limitations of conventional datasets. This study aims to estimate and analyse the spatial and temporal distribution of carbon stocks at Universitas Padjadjaran using high-resolution remote sensing imagery and to support sustainable campus and green campus initiatives. Multi-temporal data from WorldView-2 (2015, 2017), WorldView-3 (2021), and Legion-03 (2025) were used to derive vegetation indices, followed by aboveground biomass (AGB) modelling through regression analysis. Carbon stock was calculated using a standard conversion factor of 0.5. The results show a consistent increase in vegetation density and carbon stock, with average values rising from 20.381 tonnes/ha in 2015 to 29.160 tonnes/ha in 2025. The use of the MSAVI produced an accurate model for predicting AGB (R2 = 0.987–0.993). This study introduces a novel integration of high-resolution imagery using MSAVI to improve AGB estimation at the campus scale, providing a more detailed and reliable approach for carbon assessment in heterogeneous semi-urban environments and contributing to the implementation of sustainable, environmentally friendly campus management strategies.