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Time Series Alos-2/Palsar-2 Sar Data and Multi-Temporal Icesat-2 Lidar Data for Forest Above-Ground Biomass Retrieval
Journal
2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021 - Proceedings
Date Issued
2021-01-01
Author(s)
Musthafa, Mohamed
Singh, Gulab
Nela, Bala Raju
Abstract
Measurement of forest above-ground biomass is vital to estimate the terrestrial carbon sink contribution to the carbon budget. This study investigates the optimal combination of Synthetic aperture radar (SAR) and Lidar footprint data for forest biomass estimation. Forest canopy height product (ATL08) from Ice, Cloud, and land Elevation (ICESat-2) data is used to interpolate forest stand height to create a two-dimensional surface using geostatistical kriging. Modeled forest stand height is then validated with ground truth samples, and its accuracy is quantified with an RMSE of 2.74 m and r2 of 0.76. A year-wise average backscatter of SAR imagery acquired from 2014 to 2018 was computed and used as a predictor variable along with the interpolated forest stand height in support vector machine regression analysis to estimate forest above ground biomass. The results showed that the average backscatter computed for acquisitions during 2017 combined with interpolated forest stand height performed better with RMSE less than 40 Mg/ha and r2 above 0.70. The study emphasized that the imagery acquired during the rainy season contributed to poor biomass estimation accuracy.
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