Abstract
Aboveground biomass (AGB) estimation is crucial for assessing forest productivity, carbon sequestration, and functional diversity. Integrating optical remote sensing data with field inventory to estimate AGB saturates at high biomass. However, transformed optical imagery is effective in tropical forest AGB estimation, especially for eliminating saturation effects. Recent AGB mapping research uses diverse predictor data fusion and advanced machine learning models. In this research, we extracted texture parameters using the Gray-level co-occurrence Matrix from the first two principal components of Sentinel-2's multispectral bands. We also calculated the Normalized Difference Vegetation Index, Visible Atmospheric Resistance Index, and Leaf Area Index for the analysis. Elevation, slope, and aspect data from SRTM DEM and GEDI-Landsat tree height product were used as ancillary datasets due to their significant impact on AGB. Neighborhood statistics (3 × 3 pixels) of predictor variables were calculated to account surrounding contributions of the focal plot. A total of 60 plots of 0.1 ha were established across the landscape where 70% and 30% of randomly selected plots were used for random forest model development and validation respectively. The final model explained AGB variability significantly (correlation coefficients = 0.72, root mean square error (RMSE) = 69.18 Mg/ha, mean absolute error (MAE) = 58.22 Mg/ha) with an uncertainty observation of 41.3 percent relative RMSE (rRMSE). The study concluded that combinations of texture and spectral variables derived from Sentinel-2 optical imagery along with physical variables are found effective in AGB mapping. The method and obtained results were promising and appeals to its replicability for building a generalized AGB model.
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Acknowledgements
The study was carried out as a part of the “Biodiversity characterization at community level using EO data” project supported by the Department of Biotechnology (DBT), New Delhi and the Department of Space (DOS), Bengaluru. The authors are thankful to the European Space Agency (ESA) for Sentinel-2 data. The authors are thankful to Director, National Remote Sensing Centre and Head, Department of Botany, Andhra University for encouragement and facilities. The authors acknowledge the support of field assistants and locals of Gudem, Sapparla, Raghavendra nagar regions of Andhra Pradesh, India. The authors also acknowledge the support provided by the Andhra Pradesh Forest Department and Police department during field data collection.
Funding
This work was supported by the Department of Biotechnology and Department of Space, Government of India (Grant no.-BT/Cood.II/10/02/2016), as a part of a project ‘Biodiversity characterization at community level in India using Earth observation data.'
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Behera, D., Kumar, V.A., Rao, J.P. et al. Estimating Aboveground Biomass of a Regional Forest Landscape by Integrating Textural and Spectral Variables of Sentinel-2 Along with Ancillary Data. J Indian Soc Remote Sens 52, 917–929 (2024). https://doi.org/10.1007/s12524-023-01740-x
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DOI: https://doi.org/10.1007/s12524-023-01740-x