Forest fires have increased at an alarming rate in recent years, with multiple consequences in Nepal's forest ecosystem and landscapes. The research used remote sensing and GIS technology as well as statistical tools for developing forest fires risk models in two major landscapes of Nepal, i.e., Terai Arc Landscape (TAL) and Chitwan Annapurna Landscape (CHAL). A multi-parametric weighted index model was adopted to derive and demarcate the forest fire-risk map with risk variables such as vegetation, topographic factors, land surface temperature, and proximity to the road and settlements. To enhance the use of a fire risk map, collinearity between variables was checked (VIF <2) and validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots and Kernel Density Estimation (KDE) method. The MODIS hotspot data from 2001 to 2018 was also evaluated which indicates that the number of fire counts has a strong relation (R² =0.82) with the burn area. Broadleaved forest in the pre-monsoon season is highly vulnerable to forest fire. More than half of the total forested area (65%) is in high fire risk, particularly in the TAL region. The study results could assist the decision-makers to implement preventive measures by minimizing the risk and impacts of forest fires.

Each year forest fire causes enormous damage to Nepal's forest ecosystems and landscape. For an active community involvement at the landscape level, policymakers must take the interests of local forest managers into account to increase social acceptability. This research explores the perception of community forest managers, who are constantly managing forests at the grassroots level, to understand the relationship between their priorities, needs, and attitudes toward forest fire management. Eighty–eight key informants from six districts were interviewed using a structured questionnaire. The questionnaire was divided into three major sections, (i) forest fuel management and infrastructure, (ii) forest fire management strategies and actions, and (iii) public education and awareness on forest fire management. The data were analyzed using the Kruskal–Wallis test, where the respondents rated above 4.2 out of 5 for activities like increase of insurance mechanisms, providing training for firefighting volunteer groups, and provisioning firefighting equipment. The majority of the respondents agreed on the activities under the forest fire management strategies and actions section (Kendall's Tau = 0.8501), followed by forest fuel management and infrastructure (Kendall’s Tau= 0.6757). We anticipate that the results of this study will be helpful for the local decision–makers in involving different communities and identifying their priorities while implementing various forest fire management activities in diverse landscapes or provinces of the country.

Livestock grazing is one of the largest sectors for land use globally, and contrasting impacts (negative, neutral, and positive) of livestock grazing on vegetation and soil have been observed. In Koshi Tappu Wildlife Reserve (KTWR), livestock grazing is one of the major problems but has been feebly addressed in previous research. This study, therefore, aims to determine the impacts of livestock grazing on vegetation and soil quality in grasslands of core and buffer zones of KTWR. Less grazing intensity was observed at the core zone compared to the buffer zone. Livestock grazing was observed with negative impacts on the richness and diversity of the plant species causing changes in the community assemblages. Invasive plant species richness, however, was found higher in the low grazed areas. Differences in soil pH, phosphorus, and potassium content between high and low grazed areas were not observed. In contrast, soil electrical conductivity, bulk density, and nitrogen content were significantly higher in the high grazed areas. Controlled grazing is recommended at buffer zone grasslands of KTWR to enhance plant diversity and nutrient availability. The core areas of the reserve should be managed for reducing the abundance and distribution of invasive alien plant species.

Global forests, valued at over USD 150 trillion and supporting the livelihoods of 25% of the world’s population, are threatened by degradation and deforestation, particularly in developing nations. Several forest management systems are in practice, but leasehold forestry (LF) is considered the best for achieving the dual goals of preventing degradation and alleviating poverty. Nepal is a pioneer in LF, prioritizing it since 1978. It is now practiced in 39 districts, covering 43,994 hectares of forests. Using DFID’s sustainable livelihood assessment guidelines (a framework never before applied to LF) and incorporating seven additional indicators identified through extensive literature review and expert consultation, as well as semi-structured interviews with key informants (n = 14) and LF users (n = 228), this study quantifies the contribution of LF in Nepal to 5 core assets and 21 livelihood indicators across three categories: “successful”, “moderately successful”, and “not successful”. The results reveal that LF significantly contributes to natural and financial capital, with a lesser impact on social and physical capital. Among the key indicators, LF has the greatest influence on savings and investment, but the least on infrastructure. This study offers targeted lessons and recommendations for less successful LF initiatives, which can help improve their outcomes. These insights are also valuable for policymakers and stakeholders to refine policies and programs and to optimize livelihood and restoration benefits from LF. Additionally, the baseline data provided will serve as a reference for monitoring and evaluating LF initiatives.

The complex terrain and increasing forest fire incidents in recent years make high-mountain environments vulnerable to forest fires, posing a threat to ecological and economic well-being. However, research related to forest fires remains limited in the mountain region. The study aimed to assess the forest fire risk by integrating ten predictive layers (biophysical, topographical, climatic, and anthropogenic factors) and a Random Forest (RF) model in Rasuwa, a mountainous district of Nepal. The model was validated using AUC and other statistical metrics. With an AUC of 0.9 and TSS of 0.67, the model shows strong predictive performance and high reliability of the generated fire risk map. The most significant factors in the model's ability to predict the forest fire risk were elevation, temperature, NDVI, and proximity to settlements. The study shows that 29.32 % of Rasuwa district’s forest is at fire risk, with the highest risk in Kalika Rural Municipality (59.4 %). In the study area, approximately 30 % of fire incidents occurred at elevations above 3000 meters. This study emphasizes the potential of the RF algorithms and geospatial methods in similar mountainous regions, aiding the concerned stakeholders and authorities in developing effective forest management plans and improving understanding of forest fire risk.