Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfires. We evaluated historical patterns of Moderate Resolution Imaging Spectroradiometer active fires in African tropical forests from 2003 to 2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year‐to‐year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015–2016 El Niño. These results contrast with the drier African woodlands and savannas, where fire has been decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon dynamics and their local implications for biodiversity and human livelihoods.

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

Many developing nations are faced with severe food insecurity partly because of their overdependence on rainfed agriculture. In Kenya, climate system variations that impact staple food crops like maize ultimately threaten the nation’s food security. This study applied analytical hierarchy process, a multi-criteria decision-making technique, and remote sensing, performed within a Geographic Information Systems framework, in developing a land suitability model for maize farming in Kenya under the changing climatic conditions. Levels of suitability were delineated using soil, climate and topographic variables. Local farmers’ knowledge was also incorporated to propose context-specific climate change adaptation practices in agriculture. The study revealed that majority (55.6%) of the land is marginally suitable. Significant changes in weather and climate were also revealed, and these generally translated to lower maize crop yields. Finally, diversification, proper timing, soil fertility retention and restoration and better-quality seeds were found to be the most practical adaptation measures.

The Mount Elgon ecosystem (MEE), an important hydrological and socio-economic area in East Africa, has exhibited significant landscape changes. These are driven by both natural factors and human activities. Yet, the vulnerability of this ecosystem is poorly understood. This study characterizes ecological and environmental (eco-environmental) vulnerability for the MEE using freely available Earth observation, topographic, and socio-economic data. Spatial principal component analysis (SPCA) was used to compute a new eco-environmental vulnerability index (EEVI) by integrating natural, environmental, and socio-economic conditions. The final EEVI was then categorized into five classes (potential, slight, light, moderate, and severe). Temporal principal component analysis (TPCA) was also conducted to identify persistent changes in multi-year variables spanning the period 2001–2018. Further, the precipitation concentration index (PCI) was assessed to evaluate changes in the spatio-temporal distribution of precipitation in the MEE. The study found that EEVI indicates the most aggregate vulnerability on the Ugandan side, especially in savanna regions. Majority of the MEE was moderately vulnerable, and savannas and grasslands constituted the largest proportion of the severe vulnerability class. There was also a marked increase in vulnerability with decrease in elevation. Eco-environmental vulnerability was strongly associated with multi-year variables based on precipitation, temperature, and population density. The study also found that precipitation concentration is amplifying especially in the wet season, thus threatening agriculture and community livelihoods. Areas in the moderate and severe vulnerability classes were identified for prioritized conservation attention.

Michigan (MI) has a long history of diverse agricultural production. One of the most rapidly expanding and profitable agricultural crops, wine grapes (Vitis vinifera L.), has only been in cultivation across MI since the 1970s. As of 2014 more than 2100 acres of Vitis vinifera were growing statewide. With such success there is a push to rapidly develop more vinifera vineyards across the state. The industry is striving to have 10,000 acres in cultivation by 2024. This study presents a data-driven approach for guiding decision making to make this goal attainable. The study models land suitability across the state using environmental, climate, topographic and land use data to understand the most to least ideal portions of the landscape for vinifera establishment. The models are tested in 17 MI counties. The study found that land suitability for viticulture has expanded and therefore, viticulture can be extended beyond the traditional growing areas. This study suggests that warming temperatures have influenced land suitability and demonstrates the application and utility of GIS-based land suitability modeling in viticulture development. Maps produced in this study provide knowledge of the climate and environmental trends, which is critical when choosing where and what cultivar to grow. With such resources, growers can be better prepared to invest and expand this pivotal agricultural sector.

Ghana has retained a substantial area of tropical forests in an extensive network of protected reserves. These forests are impacted by land uses such as logging, mining, and agriculture as well as wildfires. We studied forest disturbance and recovery from 2013 to 2020 using annual maps of forest cover derived from Landsat imagery. Fire-associated disturbance was distinguished using VIIRS active fire data. We used boosted regression trees to model disturbances in closed and open forests as a function of climate variability, human accessibility, and landscape structure. A total of 3562 km² of forest reserves were disturbed, of which 17% (615 km²) were fire disturbances and 83% (2946 km²) were non-fire disturbances. Of the total disturbed area, 68% was degradation (change from closed to open forest), 28% was open forest loss, and only 4% was closed forest loss. Over the same period, 2702 km² of forest reserves recovered, with 1948 km² of these recovering to closed-canopy forests. Fire disturbances were strongly associated with precipitation anomalies and occurred mostly in drier years, whereas non-fire disturbances had weaker relationships with precipitation. Disturbances in closed forests occurred in landscapes where closed forest cover was already low. In contrast, disturbances in open forests were most common in locations with intermediate levels of population pressure from nearby cities and proximity to non-forest land cover. The results support the idea that forest disturbance in Ghana is a multi-stage process involving degradation of closed forests followed by loss of the resulting open forests. Although non-fire disturbance rates are consistent from year to year, sharp increases in fire disturbance occur in drought years. Locations with the highest disturbance risk are associated with measurable indicators of climate, human pressure, and fragmentation, which can be used to identify these areas for conservation and forest restoration activities.