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Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery

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Title: Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery
Authors: Gegana, Mpho
Publication Year: 2016
Subject Terms: Geospatial data--Data processing, Geographic information systems--Data processing, Remote sensing--Data processing, Image processing--Digital techniques, geo
Description: Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016. ; The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted. ; LG2017
Document Type: thesis
Language: English
Relation: http://hdl.handle.net/10539/21645
Availability: http://hdl.handle.net/10539/21645
Rights: undefined
Accession Number: edsbas.8CF7D8E
Database: BASE
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  Label: Title
  Group: Ti
  Data: Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery
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  Data: <searchLink fieldCode="AR" term="%22Gegana%2C+Mpho%22">Gegana, Mpho</searchLink>
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  Data: 2016
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  Data: <searchLink fieldCode="DE" term="%22Geospatial+data--Data+processing%22">Geospatial data--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+information+systems--Data+processing%22">Geographic information systems--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing--Data+processing%22">Remote sensing--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing--Digital+techniques%22">Image processing--Digital techniques</searchLink><br /><searchLink fieldCode="DE" term="%22geo%22">geo</searchLink>
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  Data: Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016. ; The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted. ; LG2017
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      – Text: English
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      – SubjectFull: Geospatial data--Data processing
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      – SubjectFull: Geographic information systems--Data processing
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      – TitleFull: Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery
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