Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills

Forest aboveground biomass (AGB) plays an indispensable role in the terrestrial carbon cycle and its dynamics. It also provides baseline data for developing sustainable management strategies in the region. In the present study, a decision-tree based random forest (RF) algorithm was used to estimate AGB for the different forest types in Doon valley, situated in the Himalayan foothills of India. Fifty-one spectral and textural variables were initially extracted using Landsat 8 Operational Land Imager and Sentinel-1A, which were further reduced to twenty optimum variables using the recursive feature elimination (RFE) method. These optimum variables were finally used to map AGB. Results showed that the spatial distribution of AGB ranged from 46.36 to 596.15 Mg ha−1 with good correlation (R2 = 0.87, RMSEr = 18.7%, RMSE = 62.56 Mg ha−1) between the observed and predicted AGB. This study validated the synergistic use of remote sensing, field data, and RF algorithm to precisely predict the spatial distribution of AGB.