Abstract
The Sentinel-2A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial, spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data. Our study was aimed at exploring the potential of Sentinel-2A imagery to estimate Above Ground Biomass (AGB) of Subtropical Pine Forest in Pakistan administered Kashmir. We developed an AGB predictive model using field inventory and Sentinel 2A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model (DEM). Field inventory data was collected from 108 randomly distributed plots (0.l ha each) across the study area. The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data. Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R (0.86), R2 (0.74), adjusted R2 (0.72) and RMSE value of 33 t/ha. Our results showed that first order textures (mean, standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain. Our study leads to the conclusion that Sentinel-2A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability, higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.
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Acknowledgement
We are grateful to the Department of Forests, State of AJK (Pakistan) for providing logistic support during field inventory. We would also like to express our gratitude to the reviewers for improving the manuscript with their thoughtful suggestions.
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Khan, M.R., Khan, I.A., Baig, M.H.A. et al. Exploring the potential of Sentinel-2A satellite data for aboveground biomass estimation in fragmented Himalayan subtropical pine forest. J. Mt. Sci. 17, 2880–2896 (2020). https://doi.org/10.1007/s11629-019-5968-8
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DOI: https://doi.org/10.1007/s11629-019-5968-8