Abstract
Forests play a vital role in the global climate and carbon budget regulation. The present study aims to map the forest canopy height and aboveground biomass (AGB) by integrating Global Ecosystem Dynamics Investigation (GEDI) and Sentinel data using Random Forest (RF), a machine learning algorithm in a part of the Pauri Garhwal district of Uttarakhand, India. The vegetation types/land uses of the study area were mapped using Sentinel-2 data with an overall accuracy of 91% using the RF algorithm. GEDI was integrated with 16 texture and 2 backscatter variables derived from Sentinel-1 SAR data to map the height of the forest canopy. A combination of LiDAR and SAR variables was found to be efficient in predicting forest canopy height. The forest canopy height map was validated using field-measured canopy height values from outside the LiDAR footprints with an R2 of 0.81, an RMSE of 1.61 m, and a %RMSE of 8.20. The modelled canopy height was further used with Sentinel-2 data to estimate forest AGB in the study area. The forest AGB was mapped by integrating field-measured AGB with Sentinel-2 data-derived spectral and texture variables and modelled forest canopy height derived from GEDI and Sentinel-1 data, using the RF model. The combination of spectral, texture, and height variables was able to predict the spatial distribution of AGB with an R2 = 0.88, RMSE = 22.05 Mg ha−1, and %RMSE = 15.11%. The study highlighted that a synergistic approach involving multi-sensor data can effectively predict the forest canopy height and AGB. It also highlighted the utility of machine learning algorithm in mapping forest biophysical parameters. The study presented an effective approach for forest canopy height and AGB mapping using multi-sensor earth observation data.
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Acknowledgements
The authors are thankful to the Head, Forestry and Ecology Department of Indian Institute of Remote Sensing (IIRS), ISRO; Group Head, Agriculture & Forestry and Ecology Group, IIRS; Dean and Director, IIRS, for their encouragement and support in this study. The authors wish to acknowledge Divisional Forest Officer, Pauri Garhwal Forest Division, Forest Range Officer and staff of Nagdev Forest range, Pauri Garhwal Forest Division, Government of Uttarakhand, India, for field support.
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KB contributed to data curation, investigation, formal analysis, validation, field data collection, visualization, and writing—original draft. RS was involved in data curation, investigation, formal analysis, validation, visualization, and writing—review and editing. SN contributed to conceptualization, methodology, supervision, investigation, formal analysis, validation, field data collection, visualization, and writing—review and editing.
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Bhandari, K., Srinet, R. & Nandy, S. Forest Height and Aboveground Biomass Mapping by synergistic use of GEDI and Sentinel Data using Random Forest Algorithm in the Indian Himalayan Region. J Indian Soc Remote Sens 52, 857–869 (2024). https://doi.org/10.1007/s12524-023-01792-z
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DOI: https://doi.org/10.1007/s12524-023-01792-z