Analyzing the Relationship Between Above Ground Biomass and Different Vegetation Indices of Chure Region of Sainamaina Municipality, Nepal

Abstract

For REDD+ (reducing emissions from deforestation and forest degradation), sustainable management of forests, and protection and enhancement of forest carbon stocks procedures to be successful, accurate measurement of forest above-ground biomass (AGB) are essential. Sentinel imaging that was launched since 2014 provides an opportunity for mapping and monitoring AGB in forests. The aim of this study is to analyze the relationship between AGB and vegetation indices (VIs) derived from Sentinel-2 imagery in the Chure region of Sainamaina municipality. For this, we used 72 sample plots and 7 different VIs. The ARVI(Atmospherically Resistant Vegetation Index) and EVI2 (Enhanced Vegetation Index - 2) shows strong correlation (i.e. r = 0.861 and 0.861) and coefficient of determination value (R2=0.7414 and 0.7415) respectively. Overall, Sentinel-2 multispectral images vegetation indices can produce good results for reporting the AGB.