Adelabu, SMutanga, OAdam, ECho, Moses A2014-02-132014-02-132013-11Adelabu, S., Mutanga, O., Adam, E. and Cho, M.A. 2013. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. Journal of Applied Remote Sensing, vol. 7(1), pp 1-141931-3195http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=1782792http://hdl.handle.net/10204/7206Copyright: 2013 SPIE (Society of Photo-optical Instrumentation Engineers). Published in Journal of Applied Remote Sensing, vol. 7(1).Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples. We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.enRandom forestsSupport vector machinesTree species classificationSemiarid environmentsRed-edgeArticleAdelabu, S., Mutanga, O., Adam, E., & Cho, M. A. (2013). Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. http://hdl.handle.net/10204/7206Adelabu, S, O Mutanga, E Adam, and Moses A Cho "Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image." (2013) http://hdl.handle.net/10204/7206Adelabu S, Mutanga O, Adam E, Cho MA. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. 2013; http://hdl.handle.net/10204/7206.TY - Article AU - Adelabu, S AU - Mutanga, O AU - Adam, E AU - Cho, Moses A AB - Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples. We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms. DA - 2013-11 DB - ResearchSpace DP - CSIR KW - Random forests KW - Support vector machines KW - Tree species classification KW - Semiarid environments KW - Red-edge LK - https://researchspace.csir.co.za PY - 2013 SM - 1931-3195 T1 - Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image TI - Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image UR - http://hdl.handle.net/10204/7206 ER -