Individuals’ perceived fairness or justice beliefs are related to health in numerous ways. However, environment justice research to date has given little attention to perceived fairness of environmental exposures as experienced by individuals. This study explored the feasibility of a bottom-up digital participatory (via mobile phones) approach using ecological momentary assessment (EMA) to capture individuals’ subjective experience of environmental exposures and the subjective evaluation of fairness by those affected in the context of Nepal. In total, 22 individuals participated in the study for 28 days. The results show high rates of study retention and adherence. Individuals’ justice perception was found to vary within and between individuals, but also substantially depending on the types of environmental exposures. Nevertheless, the study indicates that uncertainties are inevitable as study design and timing may conflict participants’ daily lives and priorities. The method allows us to consider multiple geographic contexts of individuals’ everyday lives beyond residential environment. This pilot study proved the possibility to assess perceptions of environmental justice issues and demonstrated the necessary steps to using digital participatory method for assessing subjective perception of fairness of individuals.

Understanding the governance pattern between civil society and market actors in allocation, access to, and use of land for low-income housing is important in managing the informal settlement. In this study, the concept of “action space” is conceptualized as a means to analyze the interaction between civil society and market actors. This novel approach is then applied to a resettlement project in Nepal as a case study. The analysis revealed the mechanism by which the “action space” was created and the push and pull of the “action space” among actors that led to various types of legitimacy. The results show that due to a regulatory vacuum a gap between market legitimacy and social legitimacy was created, resulting in a failure to cater land for low-income housing. Analysis of governance patterns using the lens of “action space” offers an important perspective on how to minimize the gap between market legitimacy and social legitimacy.

Graphical Abstract Sentinel-1 SAR-based flood analysis and machine learning workflow producing a flood susceptibility map for the Koshi River Basin. This study produces a multi-year flood susceptibility assessment for the Koshi River Basin, Nepal, by integrating Sentinel-1 SAR imagery with machine-learning and geographic information systems (GIS)-based approaches. Five-year (2019–2023) temporal flood analysis from Sentinel-1 GRD data was used for the preparation of flood inventory, consisting of 375 flood and 375 non-flood points by extracting multi-year composites alongside 11 environmental, topographic, climatic, and vegetation-based conditioning factors, which served as the foundation for training and validating the susceptibility models. The four machine learning (ML) classifiers: bagging, random forest (RF), support vector machine (SVM), and AdaBoost were evaluated against the weighted sum method. Bagging achieved the highest performance (accuracy 94.52%, precision 95.45%, recall 95.45%, F1-score 95.45%, and AUC 0.990) and identified 6.185% of the basin as highly susceptible to flooding. Strong agreement between bagging and RF (r = 0.76) indicated consistent model behaviour, while weaker correlations with the weighted sum method underscored limitations of rule-based approaches in capturing nonlinear flood processes. The combination of a multi-year SAR-based flood inventory and ensemble ML techniques demonstrates clear advantages in generating reliable susceptibility maps, providing actionable evidence to support land-use planning, disaster preparedness, and targeted flood mitigation.

Background: Agriculture remains the primary livelihood source in Nepal, particularly in the middle hill region where household food security is closely linked to landholding characteristics and agricultural productivity. Understanding the factors influencing food sufficiency is essential for designing effective food security interventions. Methods: This study examined the relationship between household food sufficiency and landholding size, land fragmentation, family size, farming intensity, and agricultural productivity in Mandandeupur Municipality, Kavrepalanchok District, Nepal. Primary data were collected from 391 households through a structured questionnaire survey, complemented by focus group discussions and key informant interviews. Secondary data were used for validation and contextual analysis. Results: The findings revealed that small and fragmented landholdings significantly contribute to lower agricultural productivity and household food deficits. The average household food sufficiency in the municipality was 9.69 months per year, indicating a moderately food-secure condition. Household food sufficiency was positively associated with landholding size, farming intensity, and agricultural productivity, whereas larger family size and land fragmentation negatively affected food availability. Conclusion: Improving household food security in Nepal's middle hills requires agricultural modernization, irrigation expansion, access to improved seeds, and land consolidation measures that enhance farm productivity and operational efficiency. Novelty: This study provides empirical evidence on the combined influence of landholding size, land fragmentation, family size, farming intensity, and agricultural productivity on household food sufficiency in Nepal's middle hill farming system using household-level primary data.

Forests play a critical role in global carbon cycles and biodiversity conservation. Quantifying forest aboveground biomass (FAGB) helps in assessing carbon emission and sequestration and can reduce uncertainty in monitoring global carbon cycles and climate change. Remote sensing techniques have proved to be a cost-effective way to estimate FAGB with timely and repeated observations and recommended by United Nations Framework on Climate Change (UNFCC) to assess FAGB and carbon stock for Reduction of Emissions from Degradation and Deforestation Program (REDD+). This research presents an assessment of freely available Landsat-8 and Sentinel-2 imagery for estimating FAGB in the Chure forests of Nepal using Multiple Regression Analysis. Sentinel-2 and Landsat-8 missions are similar, but Sentinel-2 has higher spatial resolution and collects data from the red-edge region of the electromagnetic spectrum while Landsat has one of the largest temporal ranges of imagery range from 1970s. Twelve variables derived from Landsat-8 and fifteen variables derived from Sentinel-2 imageries were used in the study. Forest inventory data of 225 plots collected by the Forest Research and Training Centre (FRTC) in 2017 and 2018 through field measurements, were used to train and validate the model. The correlation of FAGB measured in each plot and variables extracted from the Landsat-8 and Sentinel-2 optical imageries were assessed by the Pearson correlation coefficients. Multiple Linear Regression was applied based on chosen variables to develop the models for estimating FAGB for the whole study area. The R-squared values of 0.78 and 0,68 and standard error of estimate 45.98 and 40.29 were obtained respectively for the Sentinel-2 and Landsat-8 based estimation models. The estimated results were validated by considering R2 and RMSE values between observed and estimated FAGB. The values of R2 and RMSE between observed and estimated FAGB were 0.70 and 45.02 tons/ha and 0.77 and 34.33 tons/ha for Landsat-8 and Sentinel-2 images respectively. The results of the study showed that both Sentinel-2 and Landsat-8 imageries were viable for FAGB and carbon stocks estimation. However, with high R2 and low RMSE value Sentinel-2 based model outperforms the Landsat-8 based model and it seems to be better suited for FAGB estimation in Chure area for the studied year. Overall, the research highlights the potential of Sentinel-2 and Landsat-8 imagery for FAGB estimation and emphasizes the importance of utilizing multi-source data and advanced modelling techniques for accurate and reliable FAGB mapping. The study also revealed that Chure forests are of considerable significance as they store substantial amounts of carbon despite disturbance from different anthropogenic activities.

Digital Elevation Models are one of the important datasets of any Geographic Information System (GIS) and so are the parameters derived from them. One such parameter is slope, whose accuracy can have a significant effect on many engineering and construction works. This paper addresses the eight-slope calculation methods that are currently available to calculate slope value from a DEM and compares how these methods works on different slope range and values. These methods were applied to calculate slope from DEM of 30 m. To determine the method that calculates the most accurate slope value for a particular slope range by comparing them with actual slope value is the main objective of this paper. The methods 2FD, 3FD, 3FDWRD, Average Neighborhood, Constrained Quadratic Surface and FFD has given similar results across all slope range while the algorithms that appears to yield the most varying results are Maximum Max and Simple D. In addition, it is observed that the choice of algorithms is more important when grade slope is less than 10 percent. However, for terrains with above 10 percent slope, the choice of algorithms seems less important with only a difference of approximately 0.5 gradient.