Understanding forest biomass and net primary productivity in Himalayan ecosystem using geospatial approach

Forest operates as sink–source of the atmospheric CO2; hence, they form the integral part of terrestrial global carbon cycle. Biomass and primary productivity are the crucial dynamic biophysical parameters for understanding the ecosystem functioning in any forested landscape. The present study was performed in Aglar watershed situated in outer Indian Himalayan range. We performed geospatial modeling of plot-level field data on forest above ground biomass (AGB) by correlating it with textural, spectral and linearly transformed variables retrieved from Landsat 8 OLI data using of random forest (RF) machine learning algorithm. We also applied recursive feature elimination function (RFE) to obtain the variables contributing most in AGB prediction. A combination of 24 among 96 variables was identified as the most effective variables. Ground-based AGB varied from 62.54 to 470.98 Mg ha−1, whereas RF-modeled AGB ranged from 48.5 to 407.73 Mg ha−1. Results indicated that RFE selected variables were able to predict AGB with R2 of 0.84, RMSE of 42.03 Mg ha−1, MAE of 34.68 and %RMSE of 19.49 Mg ha−1 which was accepted considering the terrain complexity. Light use efficiency approach was used to model monthly NPP using Landsat 8 OLI data. The results indicated that Quercus mixed forest had highest carbon assimilation (95,148,073.9 gC) followed by Pinus roxburghii (1,863,187.7 gC), Cedrus deodara (5,752,954.1 gC) and mixed forest (2,634,737.1 gC). The seasonal pattern of NPP indicated that its strike peaked in October, whereas December and January were the lean months, suggesting that NPP is governed by climatic factors, viz. PAR, precipitation and temperature. Such watershed-level study in complex Himalayan terrain would help to understand complex biogeochemical processes in basins and ecosystem services provided by the forests.