Sirex noctilio is causing considerable mortality in commercial pine forests in KwaZulu- Natal, South Africa. The ability to remotely detect S. noctilio infestations remains crucial for monitoring the spread of the wasp and for the effective deployment of suppression activities. This thesis advocates the development of techniques based on remote sensing technology to accurately detect and map S. noctilio infestations. To date, no research has examined the potential of remote sensing technologies for the detection and mapping of Pinus patula trees infested by S. noctilio. In the first part of this thesis, the focus was on whether high spatial resolution imagery could characterize S. noctilio induced stress in P. patula forests. Results showed that, the normalized difference vegetation index derived from high spatial resolution imagery has the potential to accurately detect and map the later stages of S. noctilio infestations. Additionally, operational guidelines for the optimal spatial resolutions that are suitable for detecting and mapping varying levels of sustained S. noctilio mortality were defined. Results showed that a pixel size of 2.3 m is recommended to detect high (11-15%) infestation levels, and a pixel size of 1.75 m is recommended for detecting low to medium infestation levels (1-10%). In the second part of this thesis, the focus was on the ability of high spectral resolution (hyperspectral) data to discriminate between healthy trees and the early stages of S. noctilio infestation. Results showed that specific wavelengths located in the visible and near infrared region have the greatest potential for discriminating between healthy trees and the early stages of S. noctilio infestation. The researcher also evaluated the robustness and accuracy of various machine learning algorithms in identifying spectral parameters that allowed for the successful detection of S. noctilio infestations. Results showed that the random forest algorithm simplified the process by identifying the minimum number of spectral parameters that provided the best overall accuracies. In the final part of this thesis spatial modelling techniques were used to proactively identify pine forests that are highly susceptible to S. noctilio infestations. For the first time the random forest algorithm was used in conjunction with geographic information systems for mapping pine forests that are susceptible to S. noctilio infestations. Overall, there is a high probability of S. noctilio infestation for the majority (63%) of pine forest plantations located in Mpumalanga, South Africa. Compared to previous studies, the random forest model identified highly susceptible pine forests at a more regional scale and provided an understanding of localized variations of environmental conditions in relation to the distribution of the wasps.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2008.