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University of Johannesburg (2020)

Satellite imagery for land use change and ecosystem services assessment in the Greater Limpopo Trans-frontier Region

Nyathi, Nesisa Analisa

Titre : Satellite imagery for land use change and ecosystem services assessment in the Greater Limpopo Trans-frontier Region

Auteur : Nyathi, Nesisa Analisa

Université de soutenance : University of Johannesburg

Grade : MASTER OF SUSTAINABLE URBAN PLANNING AND DEVELOPMENT 2020

Résumé
Land use change can result in variations in ecosystem services (ESs) and their relationships. Understanding the spatiotemporal changes in land use and land cover change helps understand ESs management Studying the temporal dynamics of ESs and their relationships can support scenario analyses that provide the theoretical basis for policy decisions and regional ecosystem management in any context. This can be achieved by utilizing remote sensing techniques which are an efficient tool in conducting spatio-temporal analysis of phenomena on earth, in particular in data scarce regions of southern Africa. Consequently, this study is aimed at using Landsat imagery to assess land use land cover change dynamics from 2007 to 2018 in the Greater Limpopo Trans-frontier Region, as well as assess its impacts on ecosystem services during the stipulated time. Furthermore, to assess the drivers of land cover change in the study area over time. The specific objectives are as follows ; (i) to spatially map the change in land cover within the given period, (ii) to assess performance of random forest classification scheme, and (iii) to use InVEST Carbon Model to quantify the amount of carbon storage in the study area, (iv) to use Scenario-based model to model future projections of carbon sequestration and vegetation change and (v) to utilize the carbon sequestration and vegetation change data to inform planning for ecosystem services. Landsat imagery acquired in 2007 and 2018 was used to derive land cover classes. The derived maps (classified) were compared graphically and statistically. To achieve this, the study spatially mapped the change in land cover using the Random Forest Classification Scheme with an overall accuracy of 76%. Results of the quantified spatial changes showed that in 2007, Agricultural areas occupied 2% of the total area, Bareland 29%, built up area 25%, dense vegetation 6%, grassland 22%, water 3% and shrubland a total of 7%. While in 2018, Agricultural areas occupied 3% of the area, bareland 13%, built up area 24%, dense vegetation 5%, water at 1% and shrubland a total of 13%. Overall, results showed a slight decrease in built-up areas and an increase in agricultural land over time. Drivers of land cover change in the area were identified mainly as migration, climatic conditions, agricultural drivers and deforestation. Carbon storage also shows that there was a decrease in carbon storage from 2007 to 2018. With the scenario-based model, the results showed an increase in agricultural areas and corresponding carbon storage. The results were used to inform policy and recommend effective land use management practices.

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