Mapping crop types in Levubu area of Limpopo using Landsat 8 and Sentinel-2 sensing data

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dc.contributor.authorTshigoli, Thivhafuni Portia
dc.date.accessioned2021-12-17T14:19:01Z
dc.date.available2021-12-17T14:19:01Z
dc.date.issued2021
dc.descriptionA research report submitted in fulfilment of the requirements for the degree of Master of Science in GIS and Remote Sensing to the Faculty of Science, School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2021en_ZA
dc.description.abstractOf late food security has become one of the serious global challenges; hence crop production and monitoring plays a crucial role. One of the solutions to food security challenges is through developing proper management systems of agricultural resources. Remote sensing offers unique opportunities for frequent monitoring and management of crop types. This is done through mapping the distribution of crops which provides farmers, policy makers, decision makers and researchers an opportunity to develop better strategies for crop management and production. The study used Landsat 8 OLI and Sentinel-2 MSI, which were acquired for a single date, in mapping the spatial distribution of different crop types in Levubu area in the Limpopo province, South Africa. Two main objectives were followed in this study: firstly, to assess the potential of Landsat 8 and Sentinel-2 data in understanding the distribution pattern of crop, secondly, to determine the potential of the random forest algorithm in classifying different crops using different remote sensing datasets. Eight classes (Avocado, Banana, Built up, Macadamia nut, Maize, Mango, Vegetation and Water body) were classified in this study using random forest (RF) machine learning classifier. Other classes such as Built up, Vegetation and Water body were classified to assess the ability of RF to distinguish between crop classes and non-crop classes. The results show overall accuracies of 77.08% and 87.04 % with kappa coefficients of 70.72% and 83.56% for Landsat 8 and Sentinel-2 using RF, respectively. The study found that the overall accuracy, user’s and producer’s accuracy results of Sentinel-2 MSI were better than Landsat 8 OLI. Therefore, Sentinel-2 MSI is suitable for more accuracy and increased details in mapping crops in Levubu area.en_ZA
dc.description.librarianTL (2021)en_ZA
dc.facultyFaculty of Scienceen_ZA
dc.identifier.urihttps://hdl.handle.net/10539/32370
dc.language.isoenen_ZA
dc.schoolSchool of Geography, Archaeology and Environmental Studiesen_ZA
dc.titleMapping crop types in Levubu area of Limpopo using Landsat 8 and Sentinel-2 sensing dataen_ZA
dc.typeThesisen_ZA
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