Browsing by Author "Maphanga, Thabang"

Now showing 1 - 4 of 4
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    Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands
    (Elsevier, 2022) Maphanga, Thabang; Dube, Timothy; Shoko, Cletah
    An increase in shrubs or woody species is likely, directly or indirectly, to significantly affect rural livelihoods, wildlife/livestock productivity and conservation efforts. Poor and inappropriate land use management practices have resulted in rangeland degradation, particularly in semi-arid regions, and this has amplified the bush encroachment rate in many African countries, particularly in key savannah rangelands. The rate of encroachment is also perceived to be connected to other environmental factors, such as climate change, fire and rainfall variability, which may influence the structure and density of the shrubs (woody plants), when compared to uncontrolled grazing. Remote sensing has provided robust data for global studies on both bush encroachment and climate variability over multiple decades, and these data have complemented the local and regional evidence and process studies. This paper thus provides a detailed review of the advancements in the use of remote sensing for the monitoring of bush encroachment on the African continent, which is fuelled by climate variability in the rangeland areas.
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    Impact of bush encroachment on African savannah rangelands in the face of climate change and variability
    (University of the Western Cape, 2024) Maphanga, Thabang; Dube, Timothy
    In the African continent, especially in the southern part of the continent, climate change has been associated with significant changes in temperature and precipitation patterns. Some of the signs of climate variability noted in Southern Africa are low and erratic precipitation that leads to drought, along with increases in temperature. Due to the climate variability, terrestrial ecosystems such as savannah rangelands have been adversely affected which has led to the disruption of ecosystem services and spatial configuration. For example, studies have shown that rainfall variability in the arid savannah regions is the primary determinant of the rate of bush encroachment. In rangelands, climate change has a significant impact on the transition from grass-dominated species to woody species, which is influenced by the specific weather patterns in the area. In this regard, it is pivotal to understand the rate of bush encroachment within the savannah rangelands of South Africa. The impact of bush encroachment on savannah rangelands amid climate variability in (un)protected areas remains under explored, despite extensive works in bush encroachment in general. In order to gain a thorough understanding of how environmental changes affect the growth of bushes, both in untouched landscapes and in human-manipulated environments. The main aim of this study was to assess the utility of remotely sensed data in detecting and mapping the extent of bush encroachment in the African savannah rangelands in the face of climate change and variability. To address the overarching aim of this study, four specific objectives were set. Firstly, a review of literature on advancements in satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands was conducted.
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    Understanding the spatio-temporal distribution of bush encroachment in savannah rangelands, South Africa
    (Taylor and Francis Ltd., 2024) Maphanga, Thabang; Shoko, Cletah; Sibanda, Mbulisi
    Bush encroachment threatens rangelands’ biodiversity and productivity, impacting savannah ecosystems based on location, management practices, and factors like erratic rainfall, climate change, and environmental variability. Considering these challenges, this study therefore seeks to evaluate bush encroachment changes over-time (1992–2022) in the Southern part of Kruger National Park and surrounding communities of South Africa. The study estimated the proportion and extent of encroacher plants in relation to native bush species. To achieve this objective, bioclimatic variables, and a digital elevation model in conjunction with the Random Forest model were utilized. Classified satellite imageries achieved an overall accuracy of 72 and 93%, respectively. A gradual increase in bush encroachment was observed from 41,947 hectares (ha) (3.4%) in 1992 to 61,225 ha (10%) in 2022. Additionally, this study observed a decline in the spatial extent of native plant species by 178,163.4 ha, while invasive species have expanded by 44,022.17 ha from 1992 to 2022 wet season.
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    Using multisource remotely sensed data and cloud computing approaches to map non-native species in the semi-arid savannah rangelands of Mpumalanga, South Africa
    (Routledge, 2024) Maphanga, Thabang; Dube, Timothy; Sibanda, Mbulisi
    Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study’s objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer’s accuracy (PA) and user’s accuracy (UA) ranging from 67% to 95%. It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.