AbstractSavannah biomes are home to a great diversity of flora and fauna species whose survival depend on the inherent heterogeneity within the savannah landscape. In southern African savannahs, grazing lawn patches play a central role in ecosystem dynamics via their influence on herbivory and fire regimes. The extent and distribution of grazing lawns therefore have important cascading impacts on habitat heterogeneity, biodiversity and important ecosystem processes such as nutrient cycling. Despite the keystone nature of grazing lawns in savannah ecosystems, there is limited knowledge on their extent and distribution. There is substantial empirical evidence of the factors that drive the formation and persistence of grazing lawns. However, no broad-scale approach exist to monitor grazing lawns and enable investigations into changes in their distribution and the impacts on broader ecosystem dynamics. Ground-based monitoring of grazing lawns is laborious and inefficient over large spatial and temporal scales.
This research uses high-resolution satellite remote sensing to characterize grazing lawns and investigate dynamics in their cover and structure in southern African savannahs.
This is achieved through a series of analysis that addresses three main objectives including (i) developing methods for accurate detection and mapping of savannah grazing lawn distribution using high-resolution satellite imagery and machine learning techniques; (ii) identifying changes in cover and structural distribution of grazing lawns over space and time; and (iii) Identifying the dominant drivers of change in grazing lawn cover and structure. For the analysis, a robust machine learning workflow is developed to identify grazing lawns in mesic and semi-arid savannah landscapes usingWorldView-3 imagery; a cost effective approach for high resolution grazing lawn monitoring is developed via fusion of open access Planet and Sentinel-2 imagery; multi-temporal high-resolution satellite images from 2002, 2014 and 2019 are used to identify changes in grazing lawn cover and structure under different savannah landscape conditions, including the effect of drought stress; and lastly, spatio-temporal analysis of grazing lawn occurrence and change trajectory is used to identify the dominant drivers of grazing lawn dynamics. High grazing lawn detection accuracies were achieved with all machine learning algorithms. Random Forest, Multilayer Perceptron and Support Vector Machine algorithms had similar accuracies and marginally outperformed Classification and Regression Trees algorithm. The results demonstrate the utility of high-resolution satellite images for overcoming savannah heterogeneity challenges to classification, leading to accurate grazing lawn detection. WorldView-3 with its high spatial resolution and broad array of vegetation sensitive spectral bands is particularly ideal for grazing lawn monitoring, and could be used for targeted investigations due to acquisition cost. Alternatively, fusion of open-access planet and Sentinel-2 images provide a cost effective option for operational management applications. In the absence of drought stress, grazing lawn extent increase uniformly, signaling to possible grazer population increase between 2002 and 2014. Most of the increase tend to occur as an expansion to existing patches or in close proximity to existing patches. The impact of drought stress on grazing lawns depends on local landscape characteristics, particularly related to water availability. Gains and losses in grazing lawn cover largely occur as transitions to and from tall grass swards, further highlighting tall grass vegetation as the main competitor for space. Woody encroachment was found not to be an immediate threat to grazing lawn cover, but could gain more significance with projected increase in drought frequency and intensity. In terms of the dominant drivers of grazing lawn dynamics, this study found the presence of water points as resource hot-spots to be an important determining factor of grazing lawn spatial distribution within the savannah landscape. Overall, grazing lawn dynamics was inferred to be primarily driven by grazers, the pattern and nature of which depends on factors that act to alter grazer population and behavior.
This research makes vital methodological contributions to grazing lawn and overall savannah vegetation monitoring. A pioneering remote sensing based methodology for monitoring grazing lawn dynamics is developed. Additionally this research contributes to literature on grazing lawn ecology. A greater knowledge of grazing dynamics has
been achieved, contributing to a better understanding of habitat heterogeneity in southern African savannahs. Overall, this research provides important tools and novel
ecological insights to guide conservation management in savannah ecosystems.
|Date of Award||2022|
|Sponsors||Royal Geographical Society|
|Supervisor||PAUL APLIN (Supervisor), MICHAEL POWELL (Supervisor), JOAQUIN ALBERTO CORTES CARRILLO (Director of Studies) & MARCIO PIE (Supervisor)|
- southern Africa
- grazing lawns
- remote sensing
- image fusion
- machine learning
- image classification
- change detection