Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes.

Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass-shrub-tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

Vitis vinifera Production in Michigan: Factors and Trends Driving Cultivation Patterns.

Vinifera cultivation is a thriving and growing industry across the state of Michigan (MI), United States. Extensive time, funds, and effort have been applied by the industry to promote growth and the onset of new producers. Specifically, Vitis vinifera wine grapes, which have been cultivated in MI since the 1970s, have seen a rapid expansion and investment from both first-time and legacy growers. However, historically, the climate of MI presented a challenge for cultivation because of low growing season temperatures (GSTs), short growing seasons, and excessive precipitation at the time of harvest. Over time, two key factors have led the MI wine industry to overcome the challenging climate. First, as seen in the literature, there are noted changes in climate, especially since the late 1980s, leading to more favorable conditions for cultivation. Second, MI growers traditionally focused on V. vinifera cultivation, which is susceptible to low winter temperatures, selected less vulnerable regions within the state while also focusing on vine protection techniques. Given the rapid growth of the wine industry across MI, there is a need to understand suitability and its drivers to help all growers make economically impactful decisions on production and expansion of wine grapes. This article looked to study the suitability of MI vinifera across the state in two ways. Initially, through an extensive literature review, the key drivers and commonly noted trends guiding vinifera production were chronicled. Second, through a trend analysis of the key drivers of suitability, the study investigated how such variables are changing significantly over space and time. The results of this study expand the knowledge of cool climate agriculture production and suitability for cultivation and highlight the complexity of relating suitability drivers for non-cool climate to cool climate vinifera cultivation.

Conflict and its relationship to climate variability in Sub-Saharan Africa.

Deviations in rainfall duration and timing are expected to have wide-ranging impacts for people in affected areas. One of these impacts is the potential for increased levels of conflict and accordingly, researchers are examining the relationship between climate variability and conflict. Thus far, there is a lack of consensus on the direction of this relationship. We contribute to the climate variability and conflict literature by incorporating Markov transitional probabilities into panel logit models to analyze how monthly deviations in rainfall affect the likelihood that a grid cell transitions to an above average level of conflict in Sub-Saharan Africa. To control for differences in seasons across the continent, we model this relationship for each of the regions of Sub-Saharan Africa separately - East, Central, West, and Southern. We find significant seasonal and regional effects between rainfall and the probability that a grid cell transitions from a state of peace to a state of conflict. In particular, above average rainfall is associated with a higher likelihood of transitioning into conflict during the dry season. Further, each region has specific months-primarily those associated with prime crop harvest periods-where variations in rainfall significantly influence conflict. We also find regional variations in the linkage between rainfall and conflict type related to the types of conflict that predominate in particular regions of Sub- Saharan Africa. These findings are important for policymakers because they suggest additional law enforcement and/or peacekeeping resources may be needed in times of above average rainfall. Policies that provide financial support for farmers or other sectors, such as mining, that are impacted by rainfall patterns may also be a useful strategy for conflict mitigation.

Using a coupled dynamic factor - random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa.

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001-2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall's Tau and Sen's Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001-2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike.