| Summary: | An understanding of the distribution of the Bracken fern (Pteridium aquilinum (L.) Kuhn) is critical for providing an appropriate management strategy. In this regard, remote sensing can play a critical role in mapping and modelling such distribution. In this study, an integrated approach using the random forest, maximum likelihood and vegetation indices was developed and tested to determine the capability of WorldView-2 multispectral eight band image in characterising the Bracken fern. Results based on the WorldView-2 were further compared to SPOT-5 multispectral (MS) image findings. The WorldView-2 (WV-2) image was spectrally resized to four traditional bands (blue, 450-510nm; green, 510-580 nm; red, 630-690 nm and NIR1, 770-895 nm) and four additional bands (coastal blue, 400-450 nm; yellow, 585-625 nm; red-edge, 705-745 nm and NIR2, 860-1040 nm) to evaluate the practicality of the spectral resolution in mapping the Bracken fern. The results from this analysis showed that the spectrally resized additional bands were more successful in general land cover mapping and characterising the Bracken fern. The result’s overall accuracy was 79.14% while the user’s and producer’s accuracies were 97.62% and 91.11% respectively. The second part of the study sought to improve the classification accuracy by applying a robust machine learning algorithm, the random forest. Since the random forest does not automatically choose the optimal bands, the backward variable elimination technique was employed to identify the optimum wavelengths in WV-2 for the identification of the Bracken fern. Respective out-of-bag (OOB) errors of 13.1% and 9.17% were achieved when the WV-2’s eight bands and optimally selected bands (n= 5) were used. These bands lie in the green (510-580nm), near-infrared1 (770-895nm), red-edge (705-745nm), near-infrared2 (860-1040nm) and the coastal blue (400-450nm) regions of the electromagnetic spectrum. These findings confirm the importance of the additional bands in vegetation analyses. The vegetation indices computed from these regions of the spectrum were superior to those in the visible region. The classification accuracy using WV-2 bands was superior to that from the commonly used SPOT 5 image. === Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2014.
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