The Modeling of above Ground Biomass in Ranges of Corbett Tiger Reserve using Dual-Polarization ALOS PALSAR Data - Kumar, Y.; Singh, S.; Chatterjee, R.S. (Journal of Tropical Forestry and Environment, 2017)
The study has been carried out in the Pauri Garhwal district of Uttarakhand keeping the focus on Corbett Tiger Reserve (CTR). The total area of CTR covered in the scene is 889 sq. km. The main aim of the paper is to develop a model by establishing a relationship between backscatter coefficients generated from dual polarization L-band ALOS PALSAR data acquired in July 2008 and the field inventory data collected by Forest Survey of India team in 2010. A total of 120 sample plots data were collected in the area out of which 60 plots were used for the training of the model and the remaining 60 plots were left for the validation of the most significant model. The Simple regression analysis was computed between HH & HV backscatter as independent variable and per plot biomass as dependent variable. The Linear, Logarithmic and Polynomial best fit regression models were analyzed. It was found that the coefficient of determination is more with HV backscatter (R2=0.75) using logarithmic model as compared among HV in linear and polynomial on one hand and HH in linear, logarithmic and polynomial on the other hand. To improve the accuracy and to know the combined effects of both the polarizations, multiple linear regression analysis (MLR) was applied. There was a significant improvement in correlation coefficients (R2=0.86).The in-situ field inventory data shows that the biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha. The simple regression modelled biomass ranges from 26.2 t/ha to 401.43 t/ha, whereas the MLR modelled biomass ranges from 10.96 t/ha to 312.64 t/ha. The majority of the area was found to be in the range of 100 t/ha to 150 t/ha biomass. The coefficient of determination (R2) between observed and predicted biomass was found to be 0.734 with simple regression, whereas it was found to be 0.83 with MLR. Key words: biomass, modeling and remote sensing