Cho, Moses ANaidoo, LavenMathieu, Renaud SAAsner, GPRamoelo, Abel2013-05-032013-05-032012-11Cho, M.A., Naidoo, L., Mathieu, R., Asner, G.P. and Ramoelo, A. 2012. Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a savanna landscape. In: 9th International Conference of the African Association of Remote Sensing and the Environment, El Jadida Morocco, October 29 to November 2, 2012http://hdl.handle.net/10204/67159th International Conference of the African Association of Remote Sensing and the Environment, El Jadida Morocco, October 29 to November 2, 2012Several classification techniques have been used to map vegetation communities or land cover types using remote sensing data including maximum likelihood (ML), discriminant analysis and spectral angle mapper classifiers. ML classifier is a commonly used supervised classification method with conventional multispectral data that considers both first order variations (e.g. mean values) and second order variations (e.g. covariance matrices). However, there is a limitation with the application of the ML classifier in situations of high within-species variability. The objective of this study is to ascertain which classification techniques are suitable for classification of savanna tree species across a land-use gradient. Eight savanna tree species were classified for two sites in the vicinity of the Kruger National Park, South Africa using two parametric (ML and Mahalanobis distance classifiers) and three non-parametric classifiers (spectral angle mapper (SAM), artificial neural networks (ANN) and Random Forest (RF)). The spectral data used consisted of 8 WorldView-2 multispectral bands simulated from 72 VNIR bands image acquired over the study areas using the Carnegie Airborne Observatory (CAO) system. With the exception of SAM, the nonparametric classifiers provided higher classification accuracies (RF = 78%, ANN = 75%, SAM = 36%) when compared to the parametric classifiers (ML = 65%, Mahalanobis distance = 68). This study moves remote sensing closer towards classification of savanna tree species over large areas.enTree speciesSavannaRemote sensingWorldView-2Conference PresentationCho, M. A., Naidoo, L., Mathieu, R. S., Asner, G., & Ramoelo, A. (2012). Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape. African Association of Remote Sensing and the Environment. http://hdl.handle.net/10204/6715Cho, Moses A, Laven Naidoo, Renaud SA Mathieu, GP Asner, and Abel Ramoelo. "Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape." (2012): http://hdl.handle.net/10204/6715Cho MA, Naidoo L, Mathieu RS, Asner G, Ramoelo A, Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape; African Association of Remote Sensing and the Environment; 2012. http://hdl.handle.net/10204/6715 .TY - Conference Presentation AU - Cho, Moses A AU - Naidoo, Laven AU - Mathieu, Renaud SA AU - Asner, GP AU - Ramoelo, Abel AB - Several classification techniques have been used to map vegetation communities or land cover types using remote sensing data including maximum likelihood (ML), discriminant analysis and spectral angle mapper classifiers. ML classifier is a commonly used supervised classification method with conventional multispectral data that considers both first order variations (e.g. mean values) and second order variations (e.g. covariance matrices). However, there is a limitation with the application of the ML classifier in situations of high within-species variability. The objective of this study is to ascertain which classification techniques are suitable for classification of savanna tree species across a land-use gradient. Eight savanna tree species were classified for two sites in the vicinity of the Kruger National Park, South Africa using two parametric (ML and Mahalanobis distance classifiers) and three non-parametric classifiers (spectral angle mapper (SAM), artificial neural networks (ANN) and Random Forest (RF)). The spectral data used consisted of 8 WorldView-2 multispectral bands simulated from 72 VNIR bands image acquired over the study areas using the Carnegie Airborne Observatory (CAO) system. With the exception of SAM, the nonparametric classifiers provided higher classification accuracies (RF = 78%, ANN = 75%, SAM = 36%) when compared to the parametric classifiers (ML = 65%, Mahalanobis distance = 68). This study moves remote sensing closer towards classification of savanna tree species over large areas. DA - 2012-11 DB - ResearchSpace DP - CSIR KW - Tree species KW - Savanna KW - Remote sensing KW - WorldView-2 LK - https://researchspace.csir.co.za PY - 2012 T1 - Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape TI - Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape UR - http://hdl.handle.net/10204/6715 ER -