2024, Vol. 7, Special Issue 7

author(s)
Nithin PS, Devagiri GM, Sathish BN, Maheswarappa V, Ravikumar D, Pooja PS, Surekha S and Syed Ali

abstract
Forests are nature’s most attractive and versatile renewable resources, providing a wide range of social, economic, environmental services and benefits. The modeling of carbon balance depends heavily on forests, as they are considered as carbon reservoir. Accurate measurements of biomass and other forest bio physical parameters are necessary to better understand the global carbon cycle. Remote sensing (RS) based AGB estimation approaches have become increasingly popular as non-destructive means of biomass estimation but constitute limited applications, since the green felling in natural forests and plantations is prohibited. The primary sources for AGB estimation are optical data, radio detection and ranging (RADAR) and light detection and ranging (LIDAR) systems. In this context the study was carried out in the Dubare forest of moist deciduous vegetation type of Kushalnagar taluk, Kodagu district to assess the above-ground biomass carbon using optical and microwave remote sensing data. Totally 25 field sampling plots were randomly used for field data collection. Previously developed relevant allometric equations were used to estimate above- ground tree biomass (AGTB) using GBH and height in each plot. The results revealed that the AGTB ranged from 129.05 Mg ha^-1 to 355.54 Mg ha^-1, total carbon stocks ranged from 60.65 Mg ha^-1 to 167.11 Mg ha^-1, while CO2e was found to be ranging from 222.4 Mg ha^-1 to 612.7 Mg ha^-1. The regression model obtained between above-ground tree biomass and Synthetic Aperture Radar (SAR) backscatter value performed well with VH polarization than VV polarization with an R² value of 0.69 and RMSE of 31.87 Mg ha^-1. The best fit regression model was obtained between above-ground tree biomass and Normalized Difference Vegetation Index (NDVI) with the highest R² value of 0.75 and a low RMSE value of 28.20 Mg ha^-1.