Abstract

Ranking 5th at global Climate Vulnerability Index, Pakistan is facing massive decline in the forest cover. Therefore, carbon stock of Pai Forest has been investigated which is converted from a riverine-to-irrigated forest. This study incorporates the direct and indirect carbon stock inventory development in 2018 and 2020, respectively, using geospatial assessments for which field-based carbon stock of nine tree species (232 individuals) is calculated in 2018 using allometric models which is then statistically correlated with the Remote Sensing (RS) and Geographic Information System (GIS) using Landsat-8 satellites. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated for 2018 and 2020 from 8 quadrats of the forest (each of 800m × 800m) and a regression model was developed using SPSS. Using this model, the indirect estimation of carbon stock was conducted to find out carbon stock of the forest in 2020. Dalbergia sissoo demonstrates the highest potential for carbon sequestration. The results revealed that both NDVI and EVI carbon stock are also declined during in the forest. This carbon stock inventory of Pai Forest will be useful for policymakers to adopt geospatial monitoring assessments while planning sustainable forest management strategies to achieve Sustainable Development Goal 13: Climate Action.

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