{"id":{"repo_id":"venda","oai_identifier":"oai:univendspace.univen.ac.za:11602/2614"},"canonical_url":"https://search.dev.ndltd.org/etd/venda/oai:univendspace.univen.ac.za:11602/2614","repository":{"repo_id":"venda","name":"University of Venda","base_url":"https://univendspace.univen.ac.za/server/oai/request"},"display":{"title":"Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a","abstract":"Background Chlorophyll-a (Chl-a) is a vital parameter to assess vegetation quality in plants as an indicator of photosynthetic capacity to ensure proper flow of ecosystem services. Nowadays, with a rapid increase in human population and deforestation chl-a in higher plants remain at risk from degradation. Aim The study sought to compare the spatial and spectral properties Landsat-8 and Sentinel-2 in estimating and mapping chlorophyll-a (chl-a) concentrations in the Vhembe District Municipality (VDM), South Africa. Methods Landsat-8 and Sentinel-2 multispectral data were used in conjunction with field data collected in August 2017, Firstly, this study assessed the correlation between chl-a and satellite data. Secondly, explored the optimal spatial resolution for mapping chlorophyll-a with stepwise multiple linear regression, and lastly, this study mapped the concentration of plant chl-a across a heterogeneous landscape. Results When assessing the correlation between chl-a and satellite data there was an obvious correlation between chlorophyll-a and Band 5 entropy with the highest R² of 0.39 at 30 m spatial scale of Landsat-8. However, there was no statistical significant difference amongst the various spatial resolution. The ability of Gray Level Co-occurrence Matrix (GLCM) texture features with Landsat-8 at medium resolution 30 m with R² = 0.55, p = 0.000006, and RMSE = 0.17 μg/m² in estimating plant chl-a yielded higher performance accuracy than Sentinel-2 at 10 m resolution with R² = 0.24, p = 0, and RMSE = 0.46 μg/m², and 20 m resolution with R² = 0.52, p = 0.00001, and RMSE = 6.90 μg/m². In exploring the optimal spatial resolution, Landsat-8 at 30 m spatial resolution was optimal for mapping plant chlorophyll-a. Lastly, plant chl-a were successfully mapped with Landsat-8 multispectral data at 30 m spatial resolution using multiple linear regression. The distribution of plant chlorophyll-a varies across the study area and is unevenly distributed due to different species and height. Discussions Chlorophyll-a as a crucial parameter in plants and requires continuous monitoring to ensure and improve ecosystem services provided by plants. This study estimated plant chlorophyll-a across the Vhembe District Municipality. The correlation coefficients derived by GLCM’s features demonstrated the ability of GLCM’s features in predicting and mapping plant chlorophyll-a with Landsat-8 at 30 m spatial resolution. Several studies have successfully mapped chlorophyll-a from a homogeneous xii landscape, for instance in agricultural crops and limited studies mapped chlorophyll-a calibrated from a heterogeneous landscape which motivated this study. Recent advancement in optical remote sensing data opens new avenues for mapping plant chlorophyll-a at various spatial resolution. Conclusion The use of remote sensing data at 30 m spatial resolution with GLCM features effectively predicted plant chlorophyll-a and enable the data processing and performance accuracy assessment. Therefore, this study highlighted the importance of Landsat-8 imagery in vegetation monitoring across a heterogeneous landscape.","abstract_html":"Background Chlorophyll-a (Chl-a) is a vital parameter to assess vegetation quality in plants as an indicator of photosynthetic capacity to ensure proper flow of ecosystem services. Nowadays, with a rapid increase in human population and deforestation chl-a in higher plants remain at risk from degradation. Aim The study sought to compare the spatial and spectral properties Landsat-8 and Sentinel-2 in estimating and mapping chlorophyll-a (chl-a) concentrations in the Vhembe District Municipality (VDM), South Africa. Methods Landsat-8 and Sentinel-2 multispectral data were used in conjunction with field data collected in August 2017, Firstly, this study assessed the correlation between chl-a and satellite data. Secondly, explored the optimal spatial resolution for mapping chlorophyll-a with stepwise multiple linear regression, and lastly, this study mapped the concentration of plant chl-a across a heterogeneous landscape. Results When assessing the correlation between chl-a and satellite data there was an obvious correlation between chlorophyll-a and Band 5 entropy with the highest R² of 0.39 at 30 m spatial scale of Landsat-8. However, there was no statistical significant difference amongst the various spatial resolution. The ability of Gray Level Co-occurrence Matrix (GLCM) texture features with Landsat-8 at medium resolution 30 m with R² = 0.55, p = 0.000006, and RMSE = 0.17 μg/m² in estimating plant chl-a yielded higher performance accuracy than Sentinel-2 at 10 m resolution with R² = 0.24, p = 0, and RMSE = 0.46 μg/m², and 20 m resolution with R² = 0.52, p = 0.00001, and RMSE = 6.90 μg/m². In exploring the optimal spatial resolution, Landsat-8 at 30 m spatial resolution was optimal for mapping plant chlorophyll-a. Lastly, plant chl-a were successfully mapped with Landsat-8 multispectral data at 30 m spatial resolution using multiple linear regression. The distribution of plant chlorophyll-a varies across the study area and is unevenly distributed due to different species and height. Discussions Chlorophyll-a as a crucial parameter in plants and requires continuous monitoring to ensure and improve ecosystem services provided by plants. This study estimated plant chlorophyll-a across the Vhembe District Municipality. The correlation coefficients derived by GLCM’s features demonstrated the ability of GLCM’s features in predicting and mapping plant chlorophyll-a with Landsat-8 at 30 m spatial resolution. Several studies have successfully mapped chlorophyll-a from a homogeneous xii landscape, for instance in agricultural crops and limited studies mapped chlorophyll-a calibrated from a heterogeneous landscape which motivated this study. Recent advancement in optical remote sensing data opens new avenues for mapping plant chlorophyll-a at various spatial resolution. Conclusion The use of remote sensing data at 30 m spatial resolution with GLCM features effectively predicted plant chlorophyll-a and enable the data processing and performance accuracy assessment. Therefore, this study highlighted the importance of Landsat-8 imagery in vegetation monitoring across a heterogeneous landscape.","abstract_has_math":false,"creators":["Bongwe, Vhuhwaho"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Malahlela, O. E."],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-10-05","date_published":"2023-10-05","updated_at":"2026-07-27T21:57:28Z","subjects":["Chlorophyll","Stepwise multiple linear regression","Gray level-co-occurrence matrix","Spatial resolution","Landsat-8","Setinel-2"],"languages":["en"],"rights":["University of Venda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11602/2614","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Malahlela, O. 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Nowadays, with a rapid increase in human population and deforestation chl-a in higher plants remain at risk from degradation. Aim The study sought to compare the spatial and spectral properties Landsat-8 and Sentinel-2 in estimating and mapping chlorophyll-a (chl-a) concentrations in the Vhembe District Municipality (VDM), South Africa. Methods Landsat-8 and Sentinel-2 multispectral data were used in conjunction with field data collected in August 2017, Firstly, this study assessed the correlation between chl-a and satellite data. Secondly, explored the optimal spatial resolution for mapping chlorophyll-a with stepwise multiple linear regression, and lastly, this study mapped the concentration of plant chl-a across a heterogeneous landscape. Results When assessing the correlation between chl-a and satellite data there was an obvious correlation between chlorophyll-a and Band 5 entropy with the highest R² of 0.39 at 30 m spatial scale of Landsat-8. However, there was no statistical significant difference amongst the various spatial resolution. The ability of Gray Level Co-occurrence Matrix (GLCM) texture features with Landsat-8 at medium resolution 30 m with R² = 0.55, p = 0.000006, and RMSE = 0.17 μg/m² in estimating plant chl-a yielded higher performance accuracy than Sentinel-2 at 10 m resolution with R² = 0.24, p = 0, and RMSE = 0.46 μg/m², and 20 m resolution with R² = 0.52, p = 0.00001, and RMSE = 6.90 μg/m². In exploring the optimal spatial resolution, Landsat-8 at 30 m spatial resolution was optimal for mapping plant chlorophyll-a. Lastly, plant chl-a were successfully mapped with Landsat-8 multispectral data at 30 m spatial resolution using multiple linear regression. The distribution of plant chlorophyll-a varies across the study area and is unevenly distributed due to different species and height. Discussions Chlorophyll-a as a crucial parameter in plants and requires continuous monitoring to ensure and improve ecosystem services provided by plants. This study estimated plant chlorophyll-a across the Vhembe District Municipality. The correlation coefficients derived by GLCM’s features demonstrated the ability of GLCM’s features in predicting and mapping plant chlorophyll-a with Landsat-8 at 30 m spatial resolution. Several studies have successfully mapped chlorophyll-a from a homogeneous xii landscape, for instance in agricultural crops and limited studies mapped chlorophyll-a calibrated from a heterogeneous landscape which motivated this study. Recent advancement in optical remote sensing data opens new avenues for mapping plant chlorophyll-a at various spatial resolution. Conclusion The use of remote sensing data at 30 m spatial resolution with GLCM features effectively predicted plant chlorophyll-a and enable the data processing and performance accuracy assessment. Therefore, this study highlighted the importance of Landsat-8 imagery in vegetation monitoring across a heterogeneous landscape."]},{"key":"dc:title","label":"Title","values":["Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a"]}]}],"canonical_facts":{"dc:contributor.advisor":["Malahlela, O. E."],"dc:creator":["Bongwe, Vhuhwaho"],"dc:date":["2023"],"dc:date.accessioned":["2023-11-08T13:14:25Z"],"dc:date.available":["2023-11-08T13:14:25Z"],"dc:date.issued":["2023-10-05"],"dc:description":["MENVSC","Department of Geography and Environmental Sciences"],"dc:description.abstract":["Background Chlorophyll-a (Chl-a) is a vital parameter to assess vegetation quality in plants as an indicator of photosynthetic capacity to ensure proper flow of ecosystem services. Nowadays, with a rapid increase in human population and deforestation chl-a in higher plants remain at risk from degradation. Aim The study sought to compare the spatial and spectral properties Landsat-8 and Sentinel-2 in estimating and mapping chlorophyll-a (chl-a) concentrations in the Vhembe District Municipality (VDM), South Africa. Methods Landsat-8 and Sentinel-2 multispectral data were used in conjunction with field data collected in August 2017, Firstly, this study assessed the correlation between chl-a and satellite data. Secondly, explored the optimal spatial resolution for mapping chlorophyll-a with stepwise multiple linear regression, and lastly, this study mapped the concentration of plant chl-a across a heterogeneous landscape. Results When assessing the correlation between chl-a and satellite data there was an obvious correlation between chlorophyll-a and Band 5 entropy with the highest R² of 0.39 at 30 m spatial scale of Landsat-8. However, there was no statistical significant difference amongst the various spatial resolution. The ability of Gray Level Co-occurrence Matrix (GLCM) texture features with Landsat-8 at medium resolution 30 m with R² = 0.55, p = 0.000006, and RMSE = 0.17 μg/m² in estimating plant chl-a yielded higher performance accuracy than Sentinel-2 at 10 m resolution with R² = 0.24, p = 0, and RMSE = 0.46 μg/m², and 20 m resolution with R² = 0.52, p = 0.00001, and RMSE = 6.90 μg/m². In exploring the optimal spatial resolution, Landsat-8 at 30 m spatial resolution was optimal for mapping plant chlorophyll-a. Lastly, plant chl-a were successfully mapped with Landsat-8 multispectral data at 30 m spatial resolution using multiple linear regression. The distribution of plant chlorophyll-a varies across the study area and is unevenly distributed due to different species and height. Discussions Chlorophyll-a as a crucial parameter in plants and requires continuous monitoring to ensure and improve ecosystem services provided by plants. This study estimated plant chlorophyll-a across the Vhembe District Municipality. The correlation coefficients derived by GLCM’s features demonstrated the ability of GLCM’s features in predicting and mapping plant chlorophyll-a with Landsat-8 at 30 m spatial resolution. Several studies have successfully mapped chlorophyll-a from a homogeneous xii landscape, for instance in agricultural crops and limited studies mapped chlorophyll-a calibrated from a heterogeneous landscape which motivated this study. Recent advancement in optical remote sensing data opens new avenues for mapping plant chlorophyll-a at various spatial resolution. Conclusion The use of remote sensing data at 30 m spatial resolution with GLCM features effectively predicted plant chlorophyll-a and enable the data processing and performance accuracy assessment. Therefore, this study highlighted the importance of Landsat-8 imagery in vegetation monitoring across a heterogeneous landscape."],"dc:identifier.uri":["http://hdl.handle.net/11602/2614"],"dc:language.iso":["en"],"dc:relation":["PDF"],"dc:rights":["University of Venda"],"dc:subject":["Chlorophyll","Stepwise multiple linear regression","Gray level-co-occurrence matrix","Spatial resolution","Landsat-8","Setinel-2"],"dc:title":["Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T21:57:28Z"}