Abstract:
The invasive plant known as bugweed (Solanum
mauritianum) is a notorious invader of forestry plantations in the
eastern parts of South Africa. Not only is bugweed considered to
be one of five most widespread invasive alien plant (IAP) species in
the summer rainfall regions of South Africa but it is also one of the
worst invasive alien plants in Africa. It forms dense infestations
that not only impacts upon commercial forestry activities but also
causes significant ecological and environment damage within natural
areas. Effective weed management efforts therefore require
robust approaches to accurately detect; map and monitor weed
distribution in order to mitigate the impact on forestry operations.
The main objective of this research was to determine the utility
of support vector machines (SVMs) with a 272-waveband AISA
Eagle image to detect and map the presence of co-occurring
bugweed within mature Pinus patula compartments in KwaZulu
Natal. The SVMwhen utilized with a recursive feature elimination
(SVM-RFE) approach required only 17 optimal wavebands from
the original image to produce a classification accuracy of 93%
and True Skills Statistic of 0.83. Results from this study indicate
that (1) there is definite potential for using SVMs for the accurate
detection and mapping of bugweed in commercial plantations
and (2) it is not necessary to use the entire 272-waveband dataset
because the SVM-RFE approach identified an optimal subset
of wavebands for weed detection thus enabling improved data
processing and analysis.