Carbon sequestration in terrestrial ecosystems is gaining a global attention, including Nepal, to address the issues of climate change. Since, the quantification of carbon stock under different land use systems with focus on both biomass and soil profile is lacking, objective of this paper is to quantify carbon stock in biomass and in soil profile under different land use regimes, namely community forest, leasehold forest and agricultural land of Chitwan district. The carbon stock in biomass was calculated using the standard allometric equations, and Dry Combustion Method was used to determine the Soil Organic Carbon (SOC). The carbon content in above ground tree biomass (AGTB) was found to be higher (81.25 t/ha) in community forest than in leasehold forest (80.09 t/ha). The carbon stock in above ground sapling biomass (AGSB) was calculated only for the community forest, and was found to be 3. 67 t/ha. Similarly, the density of leaf litter, herbs and grasses (LHG) was also found to be higher (9. 25 t/ha) in the community forest in comparison to leasehold forest (6.45 t/ha). Further, the root carbon stock density was also higher (16.25 t/ha) in the community forest than in the leasehold forest (16.02 t/ha). However, the SOC density was highest in the agricultural land (73.42t/ha) followed by the community forest (66.38 t/ha)and the leasehold forest (52. 62 t/ha). Overall, the carbon stock was highest in the community forest (176.8 t/ha) then in leasehold forest (155.18 t/ha) followed by the agricultural land (73.42 t/ha). Hence, this study shows that well managed community forest can contribute significantly in offsetting global carbon emission.

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. The amount of carbon in different carbon pools also varies at regional to local scales depending upon the environmental factors and forest management practices. This study was carried out to quantify and compare the carbon stocks in the Sal (S. robusta) forest in the core and buffer zones of Shuklaphanta National Park in Kanchanpur district of Nepal. A total of 50 sample plots with 25 in each core and buffer zone were laid in the field. The total carbon stock in the core zone was estimated to be 258.56 t/ha with 75.64% in biomass and 24.36% in the soil. In the buffer zone, the total carbon stock was almost 25% lower than that at the core zone but a slightly higher composition of biomass (i.e, 80.41% of 193.3 t/ha). These differences are likely due to the effect of the differences in management practice in the core and buffer zones. These estimates suggest that national parks have the great potential to sequester more carbon than the buffer zones. Findings from this study provide useful information on how different management practices could alter forest carbon stocks in Nepal.

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 model’s 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 10.7657 t·h−1. Finally, the developed regression equation was used to map AGB in the study area. The AGB per pixel ranges from 0 to 129.18 t·h−1, whereas the amount of CS ranges from 0 to 61.01 t·h−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.