The concerns about climate change in recent decades have heightened the need for effective methods for assessing and reporting forest biomass and Carbon Stocks (CS) at local, national, continental, and global scales. Accurate assessment of Aboveground Biomass (AGB) is critical for the sustainable management of forests, especially in the Chure region, a fragile and young mountainous in the lesser Himalaya of Nepal. This paper presents the modeling and mapping approach and shows how medium-resolution Sentinel-2 multispectral instrument (MSI) data can be used instead of hyperspectral data in inaccessible areas of the Chure region. The data were collected and analyzed from 72 circular sample plots. 60 (43 random sample plots) were used to create the model, while the remaining 40 (29 plots) were used for model validation. This study involved calculating 12 different vegetation indices and correlating them with plot-level AGB. Five models, including linear, logarithmic, quadratic, power, and exponential, were created, but the best model was found to be the quadratic model using normalized difference vegetation indices (NDVIs) with an R² value of 0.777 and a correlation coefficient of 0.881. The models AIC and BIC values were 313.60 and 320.65, respectively. The validity of the model was performed using observed and predicted AGB values, resulting in an r value of 0.9128, an R² value of 0.8332, and an RMSE value of 10.7657th⁻¹. Finally, the developed regression equation was used to map AGB in the study area. The AGB per pixel ranges from 0 to 129.18th⁻¹, whereas the amount of CS ranges from 0 to 61.01th⁻¹. Among the different vegetation indices used in the study, NDVI was found to be more precise in estimating and mapping biomass and carbon stocks in this study. Therefore, the study recommends using the quadratic model of NDVI for accurate estimation of AGB and CS in the Chure region of Sainamaina municipality.

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.