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Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%-91.7%) across all the zones and 89.1% (50%-100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%-97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%-98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales. [ABSTRACT FROM AUTHOR]

Climate change will cause substantial vegetation shifts across the world. Africa may face varying dynamics such as tree decline, savannization, and woody encroachment due to rising temperatures and rainfall changes. This study examines the potential effects of climate change on Kenyan vegetation and vegetation shifts for 2050 and 2100, employing a statistical model to predict vegetation state as driven by environmental variables, including temperature, soil moisture, livestock density, and topography. We evaluate the model by hindcasting it from 2020 to 2000 and then project future vegetation states for 2050 and 2100 under SSP 2–4.5 and SSP 5–8.5. In response to moderate emissions, a notable increase in arid-associated shrubland vegetation (53–58%) is forecasted, leading to the expansion of drylands at the expense of savannas, grasslands, and forests. Under high-emission scenarios, savannas are forecasted to expand (52–65%) at the expense of forested areas. Overall, dense forest cover declines across scenarios, affecting protected areas by promoting increased savanna cover and reducing forest area (40% to 50%). These projected shifts in major vegetation types would likely alter ecosystem functioning and associated services, impacting pastoralists and wildlife and raising biodiversity concerns. Protected areas in Kenya could lose 50% of their forests, highlighting the urgency of climate change mitigation. These findings offer a crucial foundation for future research and action on Kenya's vegetation. [ABSTRACT FROM AUTHOR]

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localized and dispersed among data owners and databases. Here, we synthesized data from several research groups and conservation NGOs and present the first release of the Landscape Dynamics (landDX) spatial-temporal database, covering ~30,000 km2 of southern Kenya. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions, to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management, and for local and governmental spatial planning. Measurement(s) livestock enclosures • agriculture • fence Technology Type(s) digital curation Sample Characteristic - Environment savanna Sample Characteristic - Location East Africa Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16828204 [ABSTRACT FROM AUTHOR]

The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small-holder fencing. Here, we present the first comprehensive vegetation map of GMME at high (10-m) spatial resolution. The map consists of nine key vegetation cover types (VCTs), which were derived in a two-step process integrating data from high-resolution WorldView-3 images (1.2-m) and Sentinel-2 images using a deep-learning workflow. We evaluate the role of anthropogenic, topographic, and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS-derived browning/greening rates in the preceding 17 years at 250-m resolution. Results show that most VCTs showed a preceding greening trend in the protected land. In contrast, the semi- and unprotected land showed a general preceding greening trend in the woody-dominated cover types, while they exhibited browning trends in grass-dominated cover types. These results suggest that woody vegetation densification may be happening across much of the GMME, alongside vegetation declines within the non-woody covers in the semi- and unprotected lands. Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure. Increasing woody densification across the entire GMME in the future would replace high-quality grass cover and pose a risk to the maintenance of the region's rich savanna megafauna, thus pointing to a need for further investigation using alternative data sources. The increasing availability of high-resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change. • A deep-learning based high-resolution mapping workflow was proposed. • Vegetation cover types were mapped in 10-m in Greater Maasai Mara Ecosystem (GMME). • Woody vegetation densification is happening across much of the GMME. • A density decline in grass cover was observed in semi- and unprotected lands in GMME. • The woody vegetation densification is correlated with rainfall and human pressure. [ABSTRACT FROM AUTHOR]

Persistence of species in the Anthropocene depends on human willingness-to-coexist with them, but this is rarely incorporated into habitat suitability or conservation priority assessments. We propose a framework of sustainable coexistence potential that integrates human willingness-to-coexist with habitat suitability assessments. We demonstrate its applicability for elephants and rhinos in the socio-ecological system of Maasai Mara, Kenya, by integrating spatial distributions of peoples' willingness-to-coexist based on Bayesian hierarchical models using 556 household interviews, with socio-ecological habitat suitability mapping validated with long-term elephant observations from aerial surveys. Willingness-to-coexist was higher if people had little personal experience with a species, and strongly reduced by experiencing a species as a threat to humans. The sustainable coexistence potential framework highlights areas of low socio-ecological suitability, and areas that require more effort to increase positive stakeholder engagement to achieve long-term persistence of large herbivores in human-dominated landscapes. [ABSTRACT FROM AUTHOR]