dc.contributor.author |
Naidoo, Laven
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|
dc.contributor.author |
Mathieu, Renaud SA
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|
dc.contributor.author |
Kleynhans, W
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|
dc.contributor.author |
Wessels, Konrad J
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dc.contributor.author |
Asner, GP
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dc.contributor.author |
Leblon, B
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|
dc.date.accessioned |
2015-03-12T10:26:39Z |
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dc.date.available |
2015-03-12T10:26:39Z |
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dc.date.issued |
2014-07 |
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dc.identifier.citation |
Naidoo, L., Mathieu, R.S.A., Main, R., Kleynhans, W., Wessels, K.J., Asner, G.P. and Leblon, B. 2014. The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets. In: Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, Canada, 13-18 July 2014 |
en_US |
dc.identifier.issn |
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6946608 |
|
dc.identifier.uri |
http://hdl.handle.net/10204/7957
|
|
dc.description |
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, Canada, 13-18 July 2014. Post print attached. |
en_US |
dc.description.abstract |
The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow;14123 |
|
dc.subject |
Woody canopy cover |
en_US |
dc.subject |
Savannahs |
en_US |
dc.subject |
Synthetic aperture radar |
en_US |
dc.subject |
Multi-frequency |
en_US |
dc.subject |
Non-parametric |
en_US |
dc.title |
The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Naidoo, L., Mathieu, R. S., Kleynhans, W., Wessels, K. J., Asner, G., & Leblon, B. (2014). The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets. IEEE. http://hdl.handle.net/10204/7957 |
en_ZA |
dc.identifier.chicagocitation |
Naidoo, Laven, Renaud SA Mathieu, W Kleynhans, Konrad J Wessels, GP Asner, and B Leblon. "The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets." (2014): http://hdl.handle.net/10204/7957 |
en_ZA |
dc.identifier.vancouvercitation |
Naidoo L, Mathieu RS, Kleynhans W, Wessels KJ, Asner G, Leblon B, The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets; IEEE; 2014. http://hdl.handle.net/10204/7957 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Naidoo, Laven
AU - Mathieu, Renaud SA
AU - Kleynhans, W
AU - Wessels, Konrad J
AU - Asner, GP
AU - Leblon, B
AB - The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images.
DA - 2014-07
DB - ResearchSpace
DP - CSIR
KW - Woody canopy cover
KW - Savannahs
KW - Synthetic aperture radar
KW - Multi-frequency
KW - Non-parametric
LK - https://researchspace.csir.co.za
PY - 2014
SM - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6946608
T1 - The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets
TI - The assessment of data mining algorithms for modelling Savannah woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets
UR - http://hdl.handle.net/10204/7957
ER -
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en_ZA |