Understanding habitat quality and its dynamics is imperative for maintaining healthy wildlife populations and ecosystems. We mapped and evaluated changes in habitat quality (1975–2015) in the Greater Serengeti Ecosystem of northern Tanzania using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) model. This is the first habitat quality assessment of its kind for this ecosystem. We characterized changes in habitat quality in the ecosystem and in a 30 kilometer buffer area. Four habitat quality classes (poor, low, medium and high) were identified and their coverage quantified. Overall (1975–2015), habitat quality declined over time but at rates that were higher for habitats with lower protection level or lower initial quality. As a result, habitat quality deteriorated the most in the unprotected and human-dominated buffer area surrounding the ecosystem, at intermediate rates in the less heavily protected Wildlife Management Areas, Game Controlled Areas, Game Reserves and the Ngorongoro Conservation Area and the least in the most heavily protected Serengeti National Park. The deterioration in habitat quality over time was attributed primarily to anthropogenic activities and major land use policy changes. Effective implementation of land use plans, robust and far-sighted institutional arrangements, adaptive legal and policy instruments are essential to sustaining high habitat quality in contexts of rapid human population growth.

Habitat condition is a vital ecological attribute in wildlife conservation and management in protected areas, including the Burunge wildlife management areas in Tanzania. Traditional techniques, including satellite remote sensing and ground-based techniques used to assess habitat condition, have limitations in terms of costs and low resolution of satellite platforms. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) have potential for assessing habitat condition, e.g., forage quantity and quality, vegetation cover and degradation, soil erosion and salinization, fire, and pollution of vegetation cover. We, therefore, examined how the recently emerged Unmanned Aerial Vehicle (UAV) platform and the traditional Sentinel-2 differs in indications of habitat condition using NDVI and GNDVI. We assigned 13 survey plots to random locations in the major land cover types: three survey plots in grasslands, shrublands, and woodlands, and two in riverine and mosaics cover types. We used a UAV-mounted, multi-spectral sensor and obtained Sentinel-2 imagery between February and March 2020. We categorized NDVI and GNDVI values into habitat condition classes (very good, good, poor, and very poor). We analyzed data using descriptive statistics and linear regression model in R-software. The results revealed higher sensitivity and ability of UAV to provide the necessary preliminary diagnostic indications of habitat condition. The UAV-based NDVI and GNDVI maps showed more details of all classes of habitat conditions than the Sentinel-2 maps. The linear regressions results showed strong positive correlations between the two platforms (p < 0.001). The differences were attributed primarily to spatial resolution and minor atmospheric effects. We recommend further studies to test other vegetation indices.

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient (k) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study’s novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas.

In Tanzania, pure grasslands cover is estimated to be 60,381 km2, about 6.8% of the total land area, and is distributed in different parts. These grasslands are diverse in dominant grass species depending on rainfall, soil type, altitude, and management or grazing system. They support livestock and wildlife distributed in different eco-tomes and habitats of the country. The potential of grasslands for the livelihood of rural people is explicit from the fact that local people depend solely on natural production to satisfy their needs for animal products. Analysis of grazing lands indicated that livestock population, production of meat, and milk from grasslands increased. But the wildlife population, when considered in terms of livestock equivalent units (Large Herbivore Units) showed a declining trend. The contribution of grasslands to the total volume of meat produced in the country showed a declining state, while milk production showed a slight increase. This situation entails a need to evaluate the grasslands of Tanzania to ascertain their potential for supporting people’s livestock, wildlife, and livelihood. This study concluded that more research is needed to establish the possibility of grasslands to keep large numbers of grazing herbivores for sustainable livestock and wildlife production.

Understanding of relative distribution of avifauna provides insights for the conservation and management of wildlife in the community managed areas. This study examined relative diversity, abundance, and distribution of avifauna in selected habitat types across five Wildlife Management Areas of the Ruvuma landscape in miombo vegetation, southern Tanzania. Five habitat types were surveyed during the study: farmland, swamps, riverine forest, dense and open woodland. Transect lines, mist-netting, and point count methods were used to document 156 species of birds in the study sites. Descriptive statistics and Kruskal-Wallis tests were used to compare species richness and diversity across habitat types. We found differences in avifaunal species distribution in the study area whereby farmland had the highest abundance of avifauna species and lowest in the riverine forest. These results suggest that variations of avifauna species abundance, diversity, and distribution could be attributed by human activities across habitat types; due to the reason that habitats with less human encroachment had good species diversity and richness. Therefore, to improve avitourism and avoid local extinction of species, we urge for prompt action to mitigate species loss by creating awareness in the adjacent community through conservation education on the importance of protecting such biodiversity resources.