Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

The regional controls of biodiversity patterns have been traditionally evaluated using structural and compositional components at the species level, but evaluation of the functional component at the ecosystem level is still scarce. During the last decades, the role of ecosystem functioning in management and conservation has increased. Our aim was to use satellite-derived Ecosystem Functional Types (EFTs, patches of the land-surface with similar carbon gain dynamics) to characterize the regional patterns of ecosystem functional diversity and to evaluate the environmental and human controls that determine EFT richness across natural and human-modified systems in temperate South America. The EFT identification was based on three descriptors of carbon gain dynamics derived from seasonal curves of the MODIS Enhanced Vegetation Index (EVI): annual mean (surrogate of primary production), seasonal coefficient of variation (indicator of seasonality) and date of maximum EVI (descriptor of phenology). As observed for species richness in the southern hemisphere, water availability, not energy, emerged as the main climatic driver of EFT richness in natural areas of temperate South America. In anthropogenic areas, the role of both water and energy decreased and increasing human intervention increased richness at low levels of human influence, but decreased richness at high levels of human influence

Satellite Remote Sensing of Woody and Herbaceous Leaf Area for Improved Understanding of Forage Resources and Fire in Africa

In sub-Saharan Africa (SSA) tree-grass systems commonly referred to as savannas dominating drylands, play a critical role in social, cultural, economic and environmental systems. These coupled natural-human systems support millions of people through pastoralism, are important global biodiversity hotspots and play a critical role in global biogeochemical cycles. Despite the importance of SSA savannas, they have been marginalized for years as most governments neglect dryland resources in favor of agricultural research and development assistance. Hence, lack of spatially and temporally accurate information on the status and trends in savanna resources has led to poor planning and management. This scenario calls for research to derive information that can be used to guide development, management and conservation of savannas for enhanced human wellbeing, livestock productivity and wildlife management. The above considerations motivated a more detailed study of the composition, temporal and spatial variability of savannas, comprising of three components. Remote sensing data was combined with field and literature data to: partition Moderate Resolution Imaging Spectroradiometer (MODIS) total leaf area index (LAIA) time series into its woody (LAIW) and herbaceous (LAIH) constituents for SSA; and application of the partitioned LAI to determine how changes in herbaceous and woody LAI, affect fire regimes and livestock herbivory in SSA. The results of this analysis include presentation of algorithm for partitioning of MODIS LAIA from 2003-2015. Biome phenologies, seasonality and distribution of woody and herbaceous LAI are presented and the long-term average 8-day phenologies availed for evaluation and research application. In determining how changes in herbaceous and woody LAI affect fire regimes in SSA, we found that herbaceous fuelload (indexed as LAIH) correlated more closely with fire, than with LAIW, providing more explanatory power than overall biomass in fire activity. We observed an asymptotic relationship between herbaceous fuel-load and fire with trees promoting fires in dry ecosystems but suppressing fires in wetter regions. In the livestock herbivory analysis we found that the more refined forage indices (LAIH and LAIW) explained more of the variability in livestock distribution than the aggregate biomass, with livestock favoring moderate to nutrient rich forage resources dependent on animal body size

Disaggregating Tree And Grass Phenology In Tropical Savannas

Savannas are mixed tree-grass systems and as one of the world’s largest biomes represent an important component of the Earth system affecting water and energy balances, carbon sequestration and biodiversity as well as supporting large human populations. Savanna vegetation structure and its distribution, however, may change because of major anthropogenic disturbances from climate change, wildfire, agriculture, and livestock production. The overstory and understory may have different water use strategies, different nutrient requirements and have different responses to fire and climate variation. The accurate measurement of the spatial distribution and structure of the overstory and understory are essential for understanding the savanna ecosystem. This project developed a workflow for separating the dynamics of the overstory and understory fractional cover in savannas at the continental scale (Australia, South America, and Africa). Previous studies have successfully separated the phenology of Australian savanna vegetation into persistent and seasonal greenness using time series decomposition, and into fractions of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS) using linear unmixing. This study combined these methods to separate the understory and overstory signal in both the green and senescent phenological stages using remotely sensed imagery from the MODIS (MODerate resolution Imaging Spectroradiometer) sensor. The methods and parameters were adjusted based on the vegetation variation. The workflow was first tested at the Australian site. Here the PV estimates for overstory and understory showed best performance, however NPV estimates exhibited spatial variation in validation relationships. At the South American site (Cerrado), an additional method based on frequency unmixing was developed to separate green vegetation components with similar phenology. When the decomposition and frequency methods were compared, the frequency method was better for extracting the green tree phenology, but the original decomposition method was better for retrieval of understory grass phenology. Both methods, however, were less accurate than in the Cerrado than in Australia due to intermingling and intergrading of grass and small woody components. Since African savanna trees are predominantly deciduous, the frequency method was combined with the linear unmixing of fractional cover to attempt to separate the relatively similar phenology of deciduous trees and seasonal grasses. The results for Africa revealed limitations associated with both methods. There was spatial and seasonal variation in the spectral indices used to unmix fractional cover resulting in poor validation for NPV in particular. The frequency analysis revealed significant phase variation indicative of different phenology, but these could not be clearly ascribed to separate grass and tree components. Overall findings indicate that site-specific variation and vegetation structure and composition, along with MODIS pixel resolution, and the simple vegetation index approach used was not robust across the different savanna biomes. The approach showed generally better performance for estimating PV fraction, and separating green phenology, but there were major inconsistencies, errors and biases in estimation of NPV and BS outside of the Australian savanna environment

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

Abstract: Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41−0.46; Root Mean Square Error (RMSE): 0.60−0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three–five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three–five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery

Vulnerability of Protected Areas to Human Encroachment, Climate Change and Fire in the Fragmented Tropical Forests of West Africa

The Upper Guinean region of West Africa is home to some of the most globally significant tropical biodiversity hotspots, providing ecosystem services that are crucial for the region’s socio-economic and environmental wellbeing. Nonetheless, following decades of human-caused destruction of natural habitats, protected areas currently remain the only significant refugia of original vegetation relics in landscapes that are highly fragmented. Aside from having strong geographic variation in land use, climate, vegetation, and human population, the region has also experienced remarkable biophysical and socio-economic changes in recent decades. All these factors influence the fire regime and the vulnerability of forests within protected areas to fire-mediated changes and forest loss, yet little is known about fire regimes and fire-vegetation interactions within the region. Therefore, the overarching goal of this dissertation was to improve our understanding of the interactions of climate, land use, and fire regimes, as well as effects of fire on forest resilience in the Upper Guinean region of West Africa. I conducted the first comprehensive regional analysis of the fire regime across the gradient from humid tropical forests to drier woodlands and woody savanna. This analysis revealed that different components of the fire regime were influenced by different environmental drivers. As a result, the various combinations of these environmental factors create distinctive fire regimes throughout the region. The results further showed increasing active fire trends in parts of the forested areas, and decreasing trend in fire activity across much of the savannas that were likely linked with land cover changes. An analysis of fire-vegetation interactions in the forest zone of Ghana provided evidence of alternative stable states involving tropical forest and a novel non-forest vegetation community maintained by fire-vegetation feedbacks. Furthermore, an analysis exploring recent drought-associated wildfires in the forest zone of Ghana revealed widespread fire encroachment into hitherto fire-resistant moist tropical forests, which were associated with forest degradation. These findings suggest that ongoing regional landscape and socio-economic changes along with climate change will lead to further changes in the fire regimes and forest vegetation of West Africa. Hence, efforts to project future fire regimes and develop regional strategies for adaptation will require an integrated approach, which encompasses multiple components of the fire regime and consider multiple drivers, including land use and climate. Furthermore, projections of future vegetation dynamics in the region will need to consider land use, vegetation, fires, and their dynamic landscape-scale interactions in the context of broader responses to climate change and human population growth. Overall, this dissertation produced novel results about the pathways and drivers of disturbance land cover change that are necessary for improving our understanding of ongoing changes in a lesser-known part of the tropics. These findings are also relevant for predicting and mitigating similar fire impacts in tropical forests worldwide