Abstract
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.
Highlights
Vegetation plays very important roles in ecosystem processes, including the mitigation of climate change effects [1] and the regulation of land surface temperatures, carbon and energy cycles [2,3,4]
The Mount Elgon ecosystem (MEE) of eastern Uganda and western Kenya was found to exhibit significant variability in vegetation dynamics and precipitation regimes. This variability was attributed to the existing land-use and land-cover (LULC) orientation especially in eastern MEE and climate change and variability
The MEE had experienced significant and persistent greening and browning at different time scales and this change was attributed to both natural factors and anthropogenic factors
Summary
Vegetation plays very important roles in ecosystem processes, including the mitigation of climate change effects [1] and the regulation of land surface temperatures, carbon and energy cycles [2,3,4]. Previous studies have concluded that these changes are driven by (1) slowly-changing natural processes, such as regional climate change (e.g., changes in temperature, precipitation, etc.), nitrogen disposition and increasing atmospheric CO2 concentrations [7], and (2) more rapid anthropogenic activities, including land-use and land-cover (LULC) change (e.g., deforestation [8,9], overcultivation and overgrazing [8], afforestation, expanding green areas in cities [1] among others) These processes do not operate in isolation [7] but rather interact at multiple scales, with global-scale drivers interacting with processes at the regional and local scales [10], making vegetation change dynamics a complex phenomenon to examine. With recent developments in Earth observation (EO) technologies, spatio-temporally contiguous remote sensing (RS) data have been collected, making it possible to investigate vegetation dynamics accurately and comprehensively in terms of other environmental processes, at multiple scales
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