Research Papers

Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

[+] Author Affiliations
Samuel Adelabu

University of KwaZulu-Natal, School of Agricultural, Earth & Environmental Sciences, Geography Department, P/Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa

Onisimo Mutanga

University of KwaZulu-Natal, School of Agricultural, Earth & Environmental Sciences, Geography Department, P/Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa

Elhadi Adam

University of KwaZulu-Natal, School of Agricultural, Earth & Environmental Sciences, Geography Department, P/Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa

Moses Azong Cho

Natural Resources and Environment Council for Scientific and Industrial Research, P.O. Box 395, Pretoria 0001, South Africa

J. Appl. Remote Sens. 7(1), 073480 (Nov 19, 2013). doi:10.1117/1.JRS.7.073480
History: Received July 4, 2013; Revised October 17, 2013; Accepted October 21, 2013
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Abstract.  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.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Samuel Adelabu ; Onisimo Mutanga ; Elhadi Adam and Moses Azong Cho
"Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image", J. Appl. Remote Sens. 7(1), 073480 (Nov 19, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073480


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