Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

Integrated Remote Sensing and Forecasting of Regional Terrestrial Precipitation with Global Nonlinear and Nonstationary Teleconnection Signals Using Wavelet Analysis

Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest - Pemigewasset Wilderness, (2) Green Mountain National Forest - Lye Brook Wilderness and (3) Adirondack State Park - Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks

Long-term patterns in remotely-sensed vegetation productivity for a transboundary conservation area in Southern Africa.

In the past century, researchers have observed changes in vegetation productivity and structure in savannas across the world. These changes, caused by shifts in precipitation patterns, fire patterns, soil nutrients, herbivory, and land management decisions, are important to understand because they affect availability of natural resources, which in turn affects the livelihoods of local populations. This study centers on the Kavango-Zambezi Transfrontier Conservation Area (KAZA), a transboundary conservation area that spans five countries in Southern Africa comprised of large areas of protected land. Using the Normalized-Difference Vegetation Index (NDVI), I tested a 35-year remotely-sensed time series for intra- and inter-annual vegetation patterns in KAZA between 1981 and 2015, including analyses for three communities in the region. A Mann- Kendall test for monotonic trends and a Sen’s Slope test were conducted to analyze inter-annual trends for significance and slope of change, respectively. Annual green-up time, the onset of the growing season, was also analyzed for spatial patterns. I found a positive overall trend of greening, as well as spatially clustered patterns of greening and browning across the study region, with sub-study area variation discussed at the community level. Annual growing season onset green-up patterns also varied, appearing to be spatially clustered across the region. The patterns found here have implications for stakeholders at the local and regional levels and will continue to develop as the region continues to face social and environmental changes, thus, continued monitoring is advised

Evaluation of the impact of climate and human induced changes on the Nigerian forest using remote sensing

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.Tertiary Education Trust Fund, Nigeri

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

Linkages between Atmospheric Circulation, Weather, Climate, Land Cover and Social Dynamics of the Tibetan Plateau

The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. In recent decades, the TP has undergone significant changes due to climate and human activities. Since the 1980s anthropogenic activities, such as the stocking of livestock, land cover change, permafrost degradation, urbanization, highway construction, deforestation and desertification, and unsustainable land management practices, have greatly increased over the TP. As a result, grasslands have undergone rapid degradation and have altered the land surface which in turn has altered the exchange of heat and moisture properties between land and the atmosphere. But gaps still exist in our knowledge of land-atmosphere interactions in the TP and their impacts on weather and climate around the TP, making it difficult to understand the complete energy and water cycles over the region. Moreover, human, and ecological systems are interlinked, and the drivers of change include biophysical, economic, political, social, and cultural elements that operate at different temporal and spatial scales. Current studies do not holistically reflect the complex social-ecological dynamics of the Tibetan Plateau. To increase our understanding of this coupled human-natural system, there is a need for an integrated approach to rendering visible the deep interconnections between the biophysical and social systems of the TP. There is a need for an integrative framework to study the impacts of sedentary and individualized production systems on the health and livelihoods of local communities in the context of land degradation and climate change. To do so, there is a need to understand better the spatial variability and landscape patterns in grassland degradation across the TP. Therefore, the main goal of this dissertation is to contribute to our understanding of the changes over the land surface and how these changes impact the plateau\u27s weather, climate, and social dynamics. This dissertation is structured as three interrelated manuscripts, which each explore specific research questions relating to this larger goal. These manuscripts constitute the three primary papers of this dissertation. The first paper documents the significant association of surface energy flux with vegetation cover, as measured by satellite based AVHRR GIMMS3g normalized difference vegetation index (NDVI) data, during the early growing season of May in the western region of the Tibetan Plateau. In addition, a 1°K increase in the temperature at the 500 hPa level was observed. Based on the identified positive effects of vegetation on the temperature associated with decreased NDVI in the western region of the Tibetan Plateau, I propose a positive energy process for land-atmosphere associations. In the second paper, an increase in Landsat-derived NDVI, i.e., a greening, is identified within the TP, especially during 1990 to 2018 and 2000 to 2018 time periods. Larger median growing season NDVI change values were observed for the Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grassland regions, in comparison to the other three regions studied. Land degradation is prominent in the lower and intermediate hillslope positions in comparison to the higher relative topographic positions, and change is more pronounced in the eastern Southeast Tibet shrublands and meadows and Tibetan Plateau Alpine Shrublands and Meadows grasslands. Geomorphons were found to be an effective spatial unit for analysis of hillslope change patterns. Through the extensive literature review presented in third paper, this dissertation recommends using critical physical geography (CPG) to study environmental and social issues in the TP. The conceptual model proposed provides a framework for analysis of the dominant controls, feedback, and interactions between natural, human, socioeconomic, and governance activities, allowing researchers to untangle climate change, land degradation, and vulnerability in the Tibetan Plateau. CPG will further help improve our understanding of the exposure of local people to climate and socio-economic and political change and help policy makers devise appropriate strategies to combat future grassland degradation and to improve the lives and strengthen livelihoods of the inhabitants of the TP

Vegetation Drought Response Index An Integration of Satellite, Climate, and Biophysical Data

Drought is a normal, recurring feature of climate in most parts of the world (Wilhite, 2000) that adversely affects vegetation conditions and can have significant impacts on agriculture, ecosystems, food security, human health, water resources, and the economy. For example, in the United States, 14 billion-dollar drought events occurred between 1980 and 2009 (NCDC, 2010), with a large proportion of the losses coming from the agricultural sector in the form of crop yield reductions and degraded hay/pasture conditions. During the 2002 drought, Hayes et al. (2004) found that many individual states across the United States experienced more than $1 billion in agriculture losses associated with both crops and livestock. The impact of drought on vegetation can have serious water resource implications as the use of finite surface and groundwater supplies to support agricultural crop production competes against other sectoral water interests (e.g., environmental, commercial, municipal, and recreation). Drought-related vegetation stress can also have various ecological impacts. Prime examples include widespread piñon pine tree die-off in the southwest United States due to protracted severe drought stress and associated bark beetle infestations (Breshears et al., 2005) and the geographic shift of a forest-woodland ecotone in this region in response to severe drought in the mid-1950s (Allen and Breshears, 1998). Tree mortality in response to extended drought periods has also been observed in other parts of the western United States (Guarin and Taylor, 2005), as well as in boreal (Kasischke and Turetsky, 2006), temperate (Fensham and Holman, 1999), and tropical (Williamson et al., 2000) forests. Droughts have also served as a catalyst for changes in wildfire activity (Swetnam and Betancourt, 1998; Westerling et al., 2006) and invasive plant species establishment (Everard et al., 2010)

Variation by Geographic Scale in the Migration-Environment Association: Evidence from Rural South Africa

"Scholarly understanding of human migration's environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around 'climate migrants'. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such 'natural capital' is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the 'environmental surplus' hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this 'operational scale sensitivity'. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability." (author's abstract

A Low-cost Normalized Difference Vegetation Index (NDVI) Payload for Cubesats and Unmanned Aerial Vehicles (UAVs)

The focus of this research has been the design and fabrication of a Normalized Difference Vegetation Index (NDVI) payload configuration. This unique payload employs low-cost commercial off-the-shelf (COTS) hardware and equipment to assess photosynthetic activity and vegetation health through remote sensing on Cubesat or Unmanned Aerial Vehicle (UAV) platforms. The proposed NDVI imaging payload is comprised of three main subsystems: an electrical system, a software system, and a hardware system. The electrical system includes a custom designed printed circuit board (PCB), a single cell 3.7 V lithium-polymer battery, voltage regulator circuitry components, and wiring harnesses and connectors. The software system employs a master and slave system that communicates through general purpose input/output (GPIO) pin responses. Raspberry Pi Zero computer boards serve as the central processing units (CPUs) of the hardware subsystem, which also consists of the Pi-Cam standard red/green/blue (RGB) and Pi No-IR near-infrared (NIR) camera modules. A PCB was designed to be compatible with the Cubesat standard and lightweight component selections make it a desirable option for UAV flights. Open-source GIMP image processing software was used to analyze results from ground-based testing and flight testing on a UAV and general aviation (GA) aircraft at various altitudes to validate proof of concept. Raw NDVI and NDVI color map images were created from GIMP post-processing. Analysis of the results suggests that the angle of incidence of the sun with respect to the view angle of the imaging payload is a significant factor in the resulting NDVI values. Terrain also appeared to have an effect on the results where shadows were cast from the sun at low angles of incidence. Therefore, in the northern hemisphere it is recommended that image collection is performed roughly within the hours of 10 AM and 2 PM between the vernal and autumnal equinoxes to ensure a solar altitude of at least 35°. For best results, it has been verified that image data should be collected at the local time of maximum solar altitude for a particular date and location of interest (typically around noon). The information gather by this research can be used by scientists and technologist to potentially provide a means of enhancing their research and further developing technologies of UAV applications and space-based systems

Earth observation and mosquito-borne diseases: assessing environmental risk factors for disease transmission via remote sensing data

Despite global intervention efforts, mosquito-borne diseases remain a major public health concern in many parts of the world. New strategies to target interventions rely on spatially explicit information about disease transmission risk. Because the transmission of mosquito borne diseases is influenced by environmental conditions, environmental data are often used to predict disease risk. However, the relationships between environmental conditions and such diseases are not homogeneous across different landscapes and requires a context-dependent understanding. The research presented in this dissertation consists of three case studies that used remote sensing data to identify environmental risk factors for mosquito-borne diseases in different geographic settings. In the first project, the distribution of malaria cases in two study areas in the Amhara region of Ethiopia was analyzed with the help of remote sensing data on land surface temperature, precipitation, spectral indices, as well as land cover and water availability. Environmental variables were derived from remote sensing data and their relationships with spatial and temporal patterns of malaria occurrence were investigated. Settlement structure played an important role in malaria occurrence in both study areas. Climatic factors were also important, with relative risk following a precipitation gradient in the area by lake Tana and a temperature gradient along the Blue Nile River escarpment. This research suggests that studies aiming to understand malaria-environmental relationships should be geographically context specific so they can account for such differences. Second, the spatial distribution of West Nile virus (WNV) risk in South Dakota was studied via different geospatial environmental datasets. We compared the effectiveness of 1) land cover and physiography data, 2) climate data, and 3) spectral data for mapping the risk of WNV transmission. The combination of all data sources resulted in the most accurate predictions. Elevation, late season (July/August) humidity, and early-season (May/June) surface moisture were the most important predictors of disease distribution. Indices that quantified inter-annual variability of climatic conditions and land surface moisture were better predictors than inter-annual means. These results suggest that combining measures of inter-annual environmental variability with static land cover and physiography variables can help to improve spatial predictions of arbovirus transmission risk. Third, mosquito populations in Norman, Oklahoma, were analyzed to investigate the influences of land cover and microclimate on the abundance of vector mosquitoes in a heterogeneous urban environment. Remotely-sensed variables, microclimate measurements, and weather station data were used to study patterns of mosquito abundances. Spatial distributions of the two vector species Ae. albopictus and Cx. quinquefasciatus were strongly associated with land cover variables. Impervious surface area positively affected the abundance of both species. Canopy cover was positively associated with the abundance of Cx. quinquefasciatus but negatively with Ae. albopictus abundance. Among all models based on time-varying environmental data, those based on remotely-sensed variables performed best in predicting species abundances. Abundances of both species were positively associated with high temperature and high relative humidity on the trap day, but negatively associated with precipitation two weeks prior to trapping. These results emphasize the great potential for including satellite imagery in habitat analyses of different vector mosquitoes. The results presented in this dissertation contribute to the understanding of how land cover and geographic context influence the transmission of mosquito-borne diseases. Particularly remote sensing variables capturing static land cover conditions and dynamic measures of vegetation greenness and moisture can explain spatial variation in disease transmission. as well as vector mosquito distribution. Whereas remotely sensed climatic variables like temperature and precipitation influenced gradients in malaria cases at a regional scale, they explained mostly seasonal variation in mosquito distribution at a city scale. Over-all, freely available remote sensing data can help us understand the environmental determinants of disease distribution and can be a valuable tool for predicting disease dynamics on a landscape scale