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The Bale Mountains National Park (BMNP) in Ethiopia comprises the largest fraction of the Afro-Alpine ecosystem in Africa, which provides vital mountain ecosystem services at local, regional, and global levels. However, the BMNP has been severely threatened by natural and anthropogenic disturbances in recent decades. In particular, landscape alteration due to human activities such as red ash quarrying has become a common practice in the BMNP, which poses a major environmental challenge by severely degrading the Afro-Alpine ecosystem. This study aims to quantify the long-term volumetric changes of two red ash quarry sites in the BMNP using historical aerial photographs and in situ data, and to assess their impact on the Afro-Alpine ecosystem. The Structure-from-Motion multi-view stereo photogrammetry algorithm was used to reconstruct the three-dimensional landscape for the year 1967 and 1984 while spatial interpolation techniques were applied to generate the current digital elevation models for 2023. To quantify the volumetric changes and landscape alteration of the quarry sites, differences in digital elevation models were computed. The result showed that the volume of resources extracted from the BMNP quarry sites increased significantly over the study period from 1984 to 2023 compared with the period from 1967 to 1984. In general, between 1967 and 2023, the total net surface volume of the quarry sites decreased by 503,721 ± 27,970 m3 and 368,523 ± 30,003 m3, respectively. The extent of the excavated area increased by 53,147 m2 and 45,297 m2 for Site 1 and 2, respectively. In terms of habitat loss, major gravel road construction inside the BMNP resulted in the reduction of Afro-Alpine vegetation by 476,860 m2, ericaceous vegetation by 403,806 m2 and Afromontane forest by 493,222 m2 with associated decline in species diversity and density. The excavation and gravel road construction have contributed to the degradation of the Afro-Alpine ecosystem, especially the endemic Lobelia rhynchopetalum on the quarry sites and roads. If excavation continues at the same rate as in the last half century, it can threaten the whole mountain ecosystem of the National Park and beyond, highlighting the importance of preventing these anthropogenic changes and conserving the remaining Afro-Alpine ecosystem. [ABSTRACT FROM AUTHOR]

The natural resources of Ethiopian high-altitude ecosystems are commonly perceived as increasingly threatened by devastating land-use practices owing to decreasing lowland resources. Quantified time-series data of the course of land-use cover changes are still needed. Very-high-resolution digital data on the historical landscape over recent decades are needed to determine the impacts of changes in afro-alpine ecosystems. However, digital elevation models (DEMs) and orthomosaics do not exist for most afro-alpine ecosystems of Africa. We processed the only available and oldest historical aerial photographs for Ethiopia and any afro-alpine ecosystem. Here, we provide a DEM and an orthomosaic image for the years 1967 and 1984 for the Bale Mountains in Ethiopia, which comprise the largest afro-alpine ecosystem in Africa. We used 298 historical aerial photographs captured in 1967 and 1984 for generating DEMs and orthomosaics with a structure-from-motion multi-view stereo photogrammetry workflow along an elevation gradient from 977 to 4377 m above sea level (a.s.l.) at very high spatial resolutions of 0.84 m and 0.98 m for the years 1967 and 1984, respectively. The structure-from-motion multi-view stereo photogrammetry workflow, employed with Agisoft Metashape, represents a modern approach that combines computer vision and photogrammetry. This method proves useful for reconstructing DEMs and orthomosaics from historical aerial photographs, with a focus on high spatial resolution. To validate the accuracy of the reconstructed DEMs, ground control points gathered through GPS measurements were used, resulting in root mean square error (RMSE) values of 3.55 m for the year 1967 and 3.44 m for the year 1984. Our datasets can be used by researchers and policymakers for watershed management, as the area provides water for more than 30 million people, landscape management, detailed mapping, and analysis of geological and archaeological features as well as natural resources, analyses of geomorphological processes, and biodiversity research. All the datasets are available online at 10.5281/zenodo.7271617 (Muhammed et al., 2022a) for all the inputs used and at 10.5281/zenodo.7269999 (Muhammed et al., 2022b) for the results obtained (very-high-resolution DEMs and orthomosaics) for both the years 1967 and 1984. [ABSTRACT FROM AUTHOR]

Subterranean animals act as ecosystem engineers, for example, through soil perturbation and herbivory, shaping their environments worldwide. As the occurrence of animals is often linked to above‐ground features such as plant species composition or landscape textures, satellite‐based remote sensing approaches can be used to predict the distribution of subterranean species. Here, we combine in‐situ collected vegetation composition data with remotely sensed data to improve the prediction of a subterranean species across a large spatial scale. We compared three machine learning‐based modeling strategies, including field and satellite‐based remote sensing data to different extents, in order to predict the distribution of the subterranean giant root‐rat GRR, Tachyoryctes macrocephalus, an endangered rodent species endemic to the Bale Mountains in southeast Ethiopia. We included no, some and extensive fieldwork data in the modeling to test how these data improved prediction quality. We found prediction quality to be particularly dependent on the spatial coverage of the training data. Species distributions were best predicted by using texture metrics and eyeball‐selected data points of landscape marks created by the GRR. Vegetation composition as a predictor showed the lowest contribution to model performance and lacked spatial accuracy. Our results suggest that the time‐consuming collection of vegetation data in the field is not necessarily required for the prediction of subterranean species that leave traceable above‐ground landscape marks like the GRR. Instead, remotely sensed and spatially eyeball‐selected presence data of subterranean species could profoundly enhance predictions. The usage of remote sensing‐derived texture metrics has great potential for improving the distribution modeling of subterranean species, especially in arid ecosystems. [ABSTRACT FROM AUTHOR]

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research., (© 2024. The Author(s).)