In South Africa, invasive black wattle trees (Acacia mearnsii D. Wild) are a major threat to
ecosystem functionality causing widespread social, economic and environmental degradation.
It is important that environmental managers are provided with rapid, regular and accurate
information on the location of invasive black wattle trees to coordinate removal efforts. This
study investigated the potential of an automated image classification algorithm to accurately
identify black wattle (A. mearnsii De Wild.) trees using imaging spectroscopy. Hyperspectral
data acquired by the EO-1 Hyperion sensor was used to identify black wattle trees in two
study areas near Greytown, KwaZulu-Natal, South Africa. Image classifications were
performed by the classification algorithm to identify black wattle trees using general and age
specific spectral signatures (three to five years, seven to nine years, eleven to thirteen years).
Results showed that using the general spectral signature an overall accuracy of 86.25%
(user’s accuracy: 72.50%) and 84.50% (user’s accuracy: 69%) was achieved for study area
one and study area two respectively. Using age specific spectral signatures, black wattle trees
between three to five years of age were mapped with an overall accuracy of 62% (user’s
accuracy: 24%) and 74.50% (user’s accuracy: 49%) for study area one and study area two
respectively. The low user’s accuracies for the age specific classifications could be attributed
to the use of relatively low resolution satellite imagery and not the efficacy of the
classification algorithm. It was concluded that the classification algorithm could be used to
identify black wattle trees using imaging spectroscopy with a high degree of accuracy. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/9738 |
| Date | 17 October 2013 |
| Creators | Agjee, Na'eem Hoosen. |
| Contributors | Pillay, A., Ahmed, Fethi B. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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