Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

Zaakirah Bassa; Urmilla Bob; Zoltan Szantoi; Riyad Ismail

doi:10.1117/1.JRS.10.015017

11 March 2016 Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

Zaakirah Bassa, Urmilla Bob, Zoltan Szantoi, Riyad Ismail

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 1, 015017 (March 2016). https://doi.org/10.1117/1.JRS.10.015017

Abstract

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the “one-against-one” and “one-against-all” combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (<80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) <80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

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Zaakirah Bassa, Urmilla Bob, Zoltan Szantoi, and Riyad Ismail "Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms," Journal of Applied Remote Sensing 10(1), 015017 (11 March 2016). https://doi.org/10.1117/1.JRS.10.015017

Published: 11 March 2016

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