This study evaluates the impact of crown density on Soil Organic Carbon (SOC) in Shivapuri Nagarjun National Park (SNNP), Nepal. SOC levels vary across forest types due to differences in tree species, soil, and topographic factors. The research quantifies carbon stocks and develops predictive models incorporating crown density as key variables. While trees are known to enrich soil carbon, the specific role of crown density in SOC dynamics remains insufficiently understood, requiring further research for effective management. In this study, a total of 108 plots were randomly selected using stratified random sampling, extracting ~400 grams of soil with 5.7 cm diameter, 10 cm height soil corer. Samples were collected in zipped polythene bags and analyzed in the laboratory. Field measurements followed FRA (2015) guidelines, and SOC concentrations were determined using the Walkley-Black wet oxidation method (1934). Data analysis in R included ANOVA tests for model validation. Total SOC stock was 219.403 t/ha across four forest types in SNNP, with a significant positive correlation between SOC and crown density (p = 2.2*10⁻¹⁶). Among nine models tested, Model 5 (SOC_Stock = A + B * CrownDensity²) was the best fit, with higher adj. R² and lower RMSE. Results showed crown density significantly influences SOC stock across all forest types. These findings provide valuable insights to the local communities, park authorities, and researchers by enhancing understanding of soil and vegetation characteristics. Forest management should also consider tree variables like height, diameter, and stand age to optimize carbon stock assessment and management.

The Gaur (Bos gaurus), a globally vulnerable and protected priority species in Nepal, has experienced habitat loss and fragmentation, poaching, and zoonotic diseases. As a consequence, their population is isolated significantly in Parsa National Park and Chitwan National Park. However, their distribution even in these protected areas are limited with topographical features. This study focuses on habitat suitability modeling of the Gaur in Parsa National Park utilizing the ensemble modeling approach to identify key eco-geographical and climatic variables influencing gaur suitable habitat and estimate suitability in and around Parsa National Park, Nepal. Potential eco-geographical variables, after multicollinearity test were integrated with ground presence points for analysis. The model achieved an Area Under Curve (AUC) and True Skill Statistics (TSS) value of 0.981 and 0.867 respectively indicating its effectiveness in predicting a suitable habitat for Gaur. It revealed that isothermality, waterholes, mean diurnal range, mean temperature of wettest quarter, settlements, slope, and river, influenced highly in Gaur’s habitat suitability in and around Parsa National Park. Study identified only 35.84% (327.09 km2) area was categorized as a suitable area (low-medium: 102.92 km2 (11.28%), medium to high: 101.08 km2 (11.07%) and optimum: 123.09 km2 (13.49%)) for gaur distribution. Eastern part of park (newly extended area around Halkhoriya lake) and south-central section of park (around Bhedaha, Mahadev, Bhata Khola) show the suitable habitat for Gaur. However, wildlife-friendly infrastructure in the East-West Highway (that fragments the park) within park can facilitate Gaur’s movement among these crucial habitat patches. These findings highlight priority to restore water sources to maintain long-term protection of species considering existing geological condition and climate change scenario in the park.

Soil erosion represents a significant environmental challenge, threatening natural resources and diminishing soil productivity and quality. In Nepal, this issue is exacerbated by both natural factors, such as excessive rainfall, weak geology, and earthquakes, and human activities, including deforestation, overgrazing, intensive agriculture, and poorly planned infrastructure construction. This research was conducted in the Manahari Khola Sub-watershed of the Makwanpur district. The primary aim was to evaluate the extent of soil erosion under current Land Use and Land Cover (LULC) conditions. Essential data sets, including LULC parameters, soil properties, rainfall data, and Digital Elevation Models (DEMs), were generated using Landsat images, FAO guideline-based landform maps, data from the Hydrology Department, and Google Earth. The analysis was carried out using ArcGIS 10.8 and ILWIS 3.3 Academic software. The RMMF soil erosion modeling results indicated a range of soil erosion risks, from Very Low to Very High. Forested and bush areas showed lower rates of soil erosion, while barren lands exhibited significantly higher erosion rates. The erosion susceptibility map further demonstrated that forested regions had a very low risk of soil erosion, agricultural areas had low to moderate risk, and barren lands faced moderate to very high susceptibility. The study underscored the necessity for effective conservation measures, particularly in cutting and cliff areas and barren lands, due to their high erosion potential. Recommendations for future action include afforestation of barren areas, implementation of conservation farming practices in agricultural zones, and adoption of appropriate road stabilization measures to mitigate soil erosion risks.

Soil erosion poses a significant environmental concern and threatens natural resources, resulting in decreased productivity and quality of soil. In Nepal, soil erosion arises from both natural factors such as excessive rainfall, weak geology, earthquakes, and human activities including deforestation, overgrazing, intensive agriculture, and unplanned infrastructure construction. A research study titled ”Soil Erosion Assessment using the Revised Morgan, Morgan Finney (RMMF) Model in a GIS Framework” was conducted in the Manahari Khola Sub-watershed of the Makwanpur district. The primary objective of the study was to evaluate the extent of soil erosion under the current Land Use and Land Cover (LULC) conditions. To perform the model, essential databases such as LULC parameters, soil parameters, rainfall parameters, and Digital Elevation Model (DEM) were generated using Landsat Images, landform maps based on FAO guidelines, data from the Hydrology Department, and Google Earth. The software tools ArcGIS 10.8 and ILWIS 3.3 Academic were utilized. The results of the RMMF soil erosion modeling indicated varying levels of soil erosion risk, ranging from Very Low to Very High. It was observed that forest and bush areas experienced lower rates of soil erosion, while barren land showed higher erosion rates. Additionally, the erosion susceptibility map illustrated that forested regions had a very low risk of soil erosion, followed by low to moderate risk in agricultural areas. Barren areas exhibited moderate to very high susceptibility to soil erosion. The study emphasized the need for proper conservation of cutting and cliff areas as well as barren land within the watershed due to their high to very high potential for soil erosion risk. Recommendations for the future included afforestation in barren areas, implementation of conservation farming practices in agricultural regions, and adoption of appropriate road stabilization measures.

Aim: To model and estimate the total above ground biomass (AGB) of forest with the best model out of five different regression models Location: Shreenagar Hill Forest, Tansen Municipality, Nepal Time Period: During the month of July, 2023 Major Taxa Studies: Pinus roxburghii Methods: Sentinel-2 satellite imagery and field-measured AGB at plot level were used. Field data were collected from a total of 26 sample. Randomly chosen 18 sample plots (SPs) (70%) were used to generate the model and remaining 8 SPs (30%) for validation of developed model. Using various bands with 10m spatial resolution, eleven VIs were calculated & correlated with field measured AGB at plot level. Results & Main Conclusions: Evaluating the fit statistics, quadratic regression model using NDVI with correlation coefficient (R) 0.92, coefficient of determination (R^2) 0.86, AIC (161.13) & BIC (164.69) was found as the best model. Predicted value of AGB from best model and observed value of AGB from field were used for model validation. Root mean square error (RMSE), R & R^2 were found as 13.3594 t.ha-1per plot, 0.9597 and 0.9211 respectively during the model validation. Therefore, the quadratic regression model with NDVI as best fit model was used to estimate the total AGB and carbon stock (CS) of study area. The average value of AGB & CS (including no vegetation area) for total study area were found 192.403 & 90.429 t.ha-1 respectively. The value of AGB & CS range from 0 to 233.451 & 0 to 109.722 t.ha-1 per pixel respectively. The benefits, possibilities, and effectiveness of combining Sentinel-2 VIs with field data to forecast biomass are demonstrated by this work. To reduce the estimation error & make wider application of research, very large sample size can be chosen by future researchers.

A document by Divya Bhattarai. Click on the document to view its contents.