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Tracking environmental change is important to ensure efficient and sustainable natural resources management. Eastern Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most people. Despite several mapping efforts, eastern Africa lacks accurate and reliable high-resolution maps of rangeland health necessary for many management, policy, and research purposes. Earth observation data offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches, which rely on in situ methods, while also complementing their accuracy. Using machine learning classification and linear unmixing, we produced rangeland health indicators – Landsat-based time series from 2000 to 2022 at 30 m spatial resolution for mapping land cover classes (LCCs) and vegetation fractional cover (VFC; including photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) – two important data assets for deriving metrics of rangeland health in eastern Africa. Due to the scarcity of in situ measurements in the large, remote, and highly heterogeneous landscape, an algorithm was developed to combine high-resolution WorldView-2 and WorldView-3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region using visual photo-interpretation. The LCC algorithm yielded an overall accuracy of 0.856 when comparing predictions to our validation dataset comprised of a mixture of in situ observations and visual photo-interpretation from high-resolution imagery, with a kappa of 0.832; the VFC returned a R2=0.795 , p < 2. 2×10-16 , and normalized root mean squared error (nRMSE) = 0.123 when comparing predicted bare-ground fractions to visual photo-interpreted high-resolution imagery. Our products represent the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangelands health in eastern Africa including Kenya, Ethiopia, and Somalia, covering a total area of 745 840 km2. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at 10.5281/zenodo.7106166 (Soto et al., 2023) and Google Earth Engine (GEE; details in the "Data availability" section). [ABSTRACT FROM AUTHOR]

Communities in dryland ecosystems face multiple stressors that threaten their livelihood resilience. While community resilience in socio‐ecological systems is largely determined by access to and sustainable management of natural resources, the underlying mechanisms are still poorly understood. This study, which uses grounded theory integrated with discourse analysis, aims to contribute addressing this knowledge gap. First, we explore global literature describing trends and patterns in the various stressors—including climate change, frequent drought, and famine, ecological, socio‐economic, and institutional stressors—that impact the livelihood resilience of these communities, with the goal of summarizing how multiple stressors impact the sustainability of dryland social‐ecological systems. Then, using the sustainable development goals (SDGs) as a reference target, we propose potential entry points to support livelihood resilience pathways. These include land, water, livestock and agroforestry, and institutional and social capital. Lastly, we suggest an integrated multi‐scalar pathway embedded with cross‐sectorial policies and differential cross‐social strategies towards combining resilience with SDGs targets. This research can help stakeholders to adapt to and mitigate the impacts of various stressors, ultimately strengthening livelihood resilience in dryland ecosystems. [ABSTRACT FROM AUTHOR]

The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.

Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R² = 0.801, p < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km², dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) and Google Earth Engine (GEE; details in data availability section). [ABSTRACT FROM AUTHOR]

Urbanization can have profound impacts on the distributional ecology of wildlife and livestock, with implications for biodiversity conservation, ecosystem services and human health. A wealth of studies have assessed biotic responses to urbanization in North America and Europe, but there is little empirical evidence that directly links human activities to urban biodiversity in the tropics. Results from a large‐scale field study conducted in Nairobi, Kenya, are used to explore the impact of human activities on the biodiversity of wildlife and livestock with which humans co‐exist across the city. The structure of sympatric wildlife, livestock and human populations are characterized using unsupervised machine learning, and statistical modelling is used to relate compositional variation in these communities to socio‐ecological drivers occurring across the city. By characterizing landscape‐scale drivers acting on these interfaces, we demonstrate that socioeconomics, elevation and subsequent changes in habitat have measurable impacts upon the diversity, density and species assemblage of wildlife, livestock and humans. Restructuring of wildlife and livestock assemblages (both in terms of species diversity and composition) has important implications for the emergence of novel diseases at urban interfaces, and we therefore use our results to generate a set of testable hypotheses that explore the influence of urban change on microbial communities. These results provide novel insight into the impact of urbanization on biodiversity in the tropics. An understanding of associations between urban processes and the structure of human and animal populations is required to link urban development to conservation efforts and risks posed by disease emergence to human health, ultimately informing sustainable urban development policy. [ABSTRACT FROM AUTHOR]