Roughly 65% of the African continent is classified as savanna. Such regions are of critical importance given their high levels of biological productivity, role in the carbon cycle, structural differences, and support of large human populations. Across southern Africa there are 79 national parks within savanna landscapes. Understanding trends and factors of vegetation health in these parks is critical for proper management and sustainability. This research strives to understand factors and trends in vegetation health from 2000 to 2016 in and around the 79 national parks across southern Africa. A backward stepwise regression was used to understand the factors (e.g., precipitation, population density, and presence of transfrontier conservation areas) affecting the normalized difference vegetation index (NDVI) during the 21st century. There was a statistically significant positive (p < 0.05) relationship between mean annual precipitation and NDVI, and a significant negative relationship between population density and NDVI. To monitor vegetation trends in and around the parks, directional persistence, a seasonal NDVI time series-based trend analysis, was used. Directional persistence is the net accumulation of directional change in NDVI over time in a given period relative to a fixed benchmarked period. Parks and buffer zones across size classes were compared to examine differences in vegetation health. There was an overwhelmingly positive trend throughout. Additionally, national parks, overall, had higher amounts of positive persistence and lower amounts of negative persistence than the surrounding buffer zones. Having higher positive persistence inside of parks indicates that they are functioning favorably relative to the buffer zones in terms of vegetation resilience. This is an important finding for park managers and conservation overall in Southern Africa.

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Global change, particularly climate change, poses a risk of altering vegetation composition and health. The consequences manifest throughout Earth’s system as a change in ecosystem services and socioecological stability. It is therefore critical that vegetation dynamics are monitored to establish baseline conditions and detect shifts. Africa is at high risk of environmental change, yet evaluation of the link between climate and vegetation is still needed for some regions. This work expands on more frequent local and multinational scale studies of vegetation trends by quantifying directional persistence (DP) at a national scale for Ethiopia, based on the normalized difference vegetation index (NDVI) between 2000 and 2016. The DP metric determines cumulative change in vegetation greenness and has been applied to studies of ecological stability and health. Secondary analysis utilizing panel regression methodologies is carried out to measure the effect of climate on NDVI. Models are developed to consider spatial dependence by including fixed effects and spatial weights. Results indicate widespread cumulative declines in NDVI, with the greatest change during the dry season and concentrated in northern Ethiopia. Regression analyses suggest significant control from climatic variables. However, temperature has a larger effect on NDVI, which contrasts with findings of some previous studies.

The globe is currently undergoing a range of alarming changes related to social and environmental systems, and the links between the two. Our ability as researchers to study the dynamics of these ongoing processes is essential for real-world understanding and application of management strategies that can mitigate potentially negative outcomes. The scale of change and its associated impact generated by natural and anthropogenic drivers varies across the landscape, such as local degradation of ecosystem services, regional deforestation, large scale urbanization, and widespread yet geographically specific changes yielded by vagaries in climate. Understanding such critical changes is of paramount importance for the future wellbeing of the coupled human-natural systems that we are all a part of and on which we all depend.Historically, one of the greatest limitations in our ability to study these systems with remote sensing technology has been inadequate availability of time series datasets that provide fine enough spatial and temporal resolution capable of identifying processes of global environmental change (GEC). However, with the advances in sensors used for environmental remote sensing, as well as the improvements in data storage and distribution, we now have the capacity to employ time series techniques for detecting GEC and addressing the multitude of questions surrounding its impacts. Currently, many of the time series methodologies being applied to examine this suite of issues are still in development, and as such, there is significant space for growth, innovation, and exploration in the field of time series remote sensing analysis (TSRSA).Only a few decades ago, what was considered a detailed TSRSA may have involved three or more Landsat images and corresponding land cover classifications with a set time interval, such as a decadal study (

Land is the central resource for agriculture. In many parts of Sub-Saharan Africa (SSA), where a large portion of the population relies on agriculture for subsistence and household incomes, future declines in the productive capacity of the land owing to environmental change pose a major threat both to farming and the well-being of smallholders. Smallholders' access to land is concurrently at risk due to large-scale land acquisitions (LSLA), promoted by governments across SSA as a means to secure capital investments for agricultural growth and economic development. These issues are especially widespread in Ethiopia, which has faced both extensive land degradation and been a primary target country for LSLA investments. This study analyzes the relative quality of land under the control of smallholders vs. large investors in western Ethiopia, with particular attention to how future suitability of land is likely to change for growing three major smallholder crops: Maize (Zea mays), sorghum (Sorghum bicolor), and beans (Phaseolus vulgaris L.). Spatial analyses are applied to compare the suitability in areas allocated to LSLAs and the remaining land available to smallholders in the country's western farming systems. Crop-specific suitability datasets are used to approximate the change in land quality between baseline conditions and scenarios of future climate change to assess the effects of climate-induced land degradation. Results indicate large areas of decreasing suitability by the late 21 st century for all crops across Ethiopia. Furthermore, this study shows that LSLA occupy land with more stable suitability, suggesting more secure agricultural land is being offered to investors.