Internal displacement of populations due to armed conflicts can substantially impact a region’s Land Use and Land Cover (LULC) and the efforts towards the achievement of Sustainable Development Goals (SDGs). The objective of this study was to determine the effects of conflict-driven Internally Displaced Persons (IDPs) on vegetation cover and environmental sustainability in the Kas locality of Darfur, Sudan. Supervised classification and change analysis were performed on Sentinel-2 satellite images for the years 2016 and 2022 using QGIS software. The Sentinel-2 Level 2A data were analysed using the Random Forest (RF) Machine Learning (ML) classifier. Five land cover types were successfully classified (agricultural land, vegetation cover, built-up area, sand, and bareland) with overall accuracies of more than 86% and Kappa coefficients greater than 0.74. The results revealed a 35.33% (-10.20 km²) decline in vegetation cover area over the six-year study period, equivalent to an average annual loss rate of -5.89% (-1.70 km²) of vegetation cover. In contrast, agricultural land and built-up areas increased by 17.53% (98.12 km²) and 60.53% (5.29 km²) respectively between the two study years. The trends of the changes among different LULC classes suggest potential influences of human activities especially the IDPs, natural processes, and a combination of both in the study area. This study highlights the impacts of IDPs on natural resources and land cover patterns in a conflict-affected region. It also offers pertinent data that can support decision-makers in restoring the affected areas and preventing further environmental degradation for sustainability.

This study explored the land use and land cover (LULC) changes (1995–2023) in the gold mining hotspots of Mang’onyi, Sambaru, and Londoni in the Singida region of Tanzania. The study integrated remote sensing (RS) to evaluate the LULC transitions with social survey assessments (83 respondents) to determine the resident’s perceptions of the environmental, social, and economic implications of mining bridging technical data with socio-economic realities. Supervised classification of Landsat images was conducted using the random forest (RF) classifier to generate LULC maps with five classes (bareland, agricultural land, forest, built-up, and shrubs and grasses), followed by an analysis to identify LULC change trends. The results showed an overall increase in agricultural land 168.51 km² (587.55%), bareland 7.70 km² (121.45%), and built-up areas 0.55 km² (134.15%), while forest and shrubs and grasses areas declined by 97.67 km² (− 72.59%) and 79.09 km² (− 43.49%), respectively. A social survey assessment revealed residents perceived environmental (deforestation, biodiversity loss, land degradation, water, air, soil pollution), social (occupational hazards, land use conflicts, negative effects on livelihoods and culture, discrimination, child labor, community displacement), and economic (improved housing, infrastructural development, job creation, economy boost, improved access to services) impacts resulting from mining activities. Our findings underscore the importance of balancing the economic benefits of gold mining with the imperative to protect the environment and support sustainable livelihoods in the mining regions.

The Darfur conflict, which emerged in the early 21st century, represents a multifaceted crisis driven by socio-political and environmental factors, with resource scarcity, exacerbated by climate change, playing a pivotal role in intensifying tensions between agricultural and pastoral communities. While climate change is typically associated with adverse environmental outcomes, an analysis of data spanning four decades (1980–2023) reveals a contrasting trend of increased precipitation, enhanced vegetation, and decreased drought frequency in recent years. This research explores the potential of these positive environmental changes to mitigate resource-based conflicts and foster political stability in Darfur as improved environmental conditions are posited to create a foundation for conflict resolution and sustainable peacebuilding. The present study integrates trends in the Enhanced Vegetation Index (EVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to examine these shifts. EVI data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250 m resolution, was used to assess large-scale vegetation patterns in arid and semi-arid landscapes. The Autoregressive Integrated Moving Average (ARIMA) model was employed to forecast future precipitation scenarios up to the year 2034, enhancing the understanding of long-term climatic trends. Data processing and analysis utilized advanced tools, including Google Earth Engine (GEE), ArcGIS Pro (version 3.4), and R software (version 4.3.2). The findings reveal a significant (33.19%) improvement in natural vegetation cover between 2000 and 2023, with degraded and unchanged areas accounting for 1.95% and 64.86%, respectively. This finding aligns with a marked increase in annual precipitation and a reduction in drought intensity over the study period. Historical SPEI analysis showed persistent drought events between 1980 and 2012, followed by a notable decline in drought frequency and severity from 2013 to 2024. Precipitation projections suggest a stable trend, potentially supporting further vegetation recovery in the region. These environmental improvements are preliminarily linked to climate-change-induced increases in precipitation and reductions in drought severity. This study’s findings contribute to a nuanced understanding of the interplay between environmental dynamics and socio-political stability in Darfur, offering actionable insights for policy interventions aimed at fostering sustainable peace and resilience in the region.

Understanding vegetation dynamics and climate variability in the vicinity of Internally Displaced Person (IDP) camps is critical due to the high dependency of displaced populations on local natural resources. This study investigates vegetation cover changes and long-term climate variability around the Sortony IDP camp in Darfur, Sudan, using satellite and climate data spanning 1980 to 2024. High-resolution imagery from PlanetScope and Sentinel–2 Level 2A was used to assess vegetation cover changes from 2015 to 2024, while precipitation, temperature, and drought trends were analyzed over 44 years (1980–2024). Vegetation changes were quantified using the Normalized Difference Vegetation Index (NDVI), and drought conditions were assessed through the Standardized Precipitation Evapotranspiration Index (SPEI) at 6-, 9-, and 12-month timescales. Future precipitation predictions were modeled using the Autoregressive Integrated Moving Average (ARIMA) model. The results revealed a substantial increase in vegetative cover: the dense vegetation class increased by 3.50%, moderate vegetation by 17.33%, and low vegetation by 30.22%. In contrast, sparse and non-vegetated areas declined by 4.55% and 46.51%, respectively. The SPEI analysis indicated a marked reduction in drought frequency and severity after 2015, following a period of prolonged drought from 2000 to 2014. Forecasts suggest continued increases in rainfall through 2034, which may further support vegetation regrowth. These findings underscore the complex interplay between climatic factors and human activity in conflict-affected landscapes. The observed vegetation recovery highlights the region’s potential for ecological resilience, reinforcing the urgent need for sustainable land-use planning and climate-adaptive management strategies in humanitarian and post-conflict settings such as Darfur.

This study explores the spatiotemporal dynamics of vegetation cover in West Darfur, Sudan, a region severely affected by the compounded effects of climate change and socio‐environmental crises. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) data spanning 2000–2023 and the Standardised Precipitation‐Evapotranspiration Index (SPEI), we assessed vegetation dynamics in relation to drought conditions. Data processing and analysis were conducted using Google Earth Engine (GEE), QGIS, and R‐Software. An Autoregressive Integrated Moving Average (ARIMA) model was utilised to forecast future climate scenarios, thereby enhancing the predictive understanding of precipitation trends. The results revealed vegetation loss during the drought periods of 2005 and 2010, with decreases of 646.23 km ² (2.83%) and 275.21 km ² (1.21%), respectively. In contrast, substantial vegetation recovery was observed in 2015 (0.54%, 122.25 km ² ), 2020 (7.87%, 1794.70 km ² ), and 2023 (8.78%, 2001 km ² ), aligning with increased annual precipitation and reduced drought intensity. Historical SPEI analysis underscores prolonged drought events between 1980 and 2010, followed by a notable reduction in drought frequency and severity in subsequent years. Precipitation projections suggest a continuing trend of increasing precipitation, likely fostering further vegetation regeneration in the study area. This study underscores the intricate interplay between vegetation cover, precipitation variability, and drought conditions in West Darfur, highlighting both the region's vulnerability to climate‐induced stressors and its potential for ecological recovery under favourable climatic conditions. The findings contribute to informed policymaking aimed at mitigating climate impacts and promoting sustainable land‐use practices in vulnerable arid and semi‐arid regions.