2026-09-25T15:11:20Zhttps://univendspace.univen.ac.za/server/oai/requestoai:univendspace.univen.ac.za:11602/26142024-09-10T14:39:16Zcom_11602_1928com_11602_1914com_11602_1897com_11602_737col_11602_2140col_11602_738
Malahlela, O. E.
Bongwe, Vhuhwaho
2023
2023-11-08T13:14:25Z
2023-11-08T13:14:25Z
2023-10-05
Bongwe, V. (2023).Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a . University of Venda, Thohoyandou, South Africa.<http://hdl.handle.net/11602/2614>.
http://hdl.handle.net/11602/2614
Bongwe V. Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a. []. , 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2614
Bongwe, V. (2023). <i>Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a</i>. (). . Retrieved from http://hdl.handle.net/11602/2614
Bongwe, Vhuhwaho. <i>"Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a."</i> ., , 2023. http://hdl.handle.net/11602/2614
TY - Dissertation
AU - Bongwe, Vhuhwaho
AB - 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.
DA - 2023-10-05
DB - ResearchSpace
DP - Univen
KW - Chlorophyll
KW - Stepwise multiple linear regression
KW - Gray level-co-occurrence matrix
KW - Spatial resolution
KW - Landsat-8
KW - Setinel-2
LK - https://univendspace.univen.ac.za
PY - 2023
T1 - Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a
TI - Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a
UR - http://hdl.handle.net/11602/2614
ER -
MENVSC
Department of Geography and Environmental Sciences
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.
National Research Foundation (NRF)
1 online resource (xii, 67 leaves): color illustrations, color maps
en
PDF
University of Venda
Chlorophyll
UCTD
Stepwise multiple linear regression
Gray level-co-occurrence matrix
Spatial resolution
Landsat-8
Setinel-2
Comparison of spatial and spectral properties of Landsat-8 and Sentinel-2 data for mapping plant chlorophyll-a
Dissertation
open.access