Thaumastocoris peregrinus (T. peregrinus) is a sap-sucking insect that feeds on Eucalyptus leaves. It poses a major threat to the forest sector by reducing the photosynthetic ability of the tree, resulting in stunted growth and even death of severely infested trees. The foliage of the tree infested with T. peregrinus turns into a deep red-brown colour starting at the northern side of the canopy but progressively spreads to the entire canopy. The monitoring of T. peregrinus and the effect it has on plantation health is essential to ensure productivity and future sustainability of forest yields. Insitu hyperspectral remote sensing combined with greater availability and lower cost of new generation multispectral satellite data, provides opportunities to detect and map T. peregrinus damage in plantation forests. This research advocates the development of remote sensing techniques to accurately detect and map T. peregrinus damage, an assessment that is critically needed to monitor plantation health in South Africa. The study first provides an overview of how improvements in multispectral and hyperspectral technology can be used to detect and map T. peregrinus damage, based on the previous work done on the remote sensing of forest pests. Secondly, the utility of field hyperspectral remote sensing in predicting T. peregrinus damage was tested. High resolution field spectral data that was resampled to the Hyperion sensor successfully predicted T. peregrinus damage with high accuracies using narrowband normalized indices and vegetation indices. Field spectroscopy was further tested in predicting water stress induced by T. peregrinus infestation, in order to identify early physiological stages of damage. A neural network algorithm successfully predicted plant water content and equivalent water thickness in T. peregrinus infested plantations. The result is promising for forest health monitoring programmes in detecting previsual physiological stages of damage. The analysis was then upscaled from field hyperspectral sensing to spaceborne sensing using the new generation WorldView-2 multispectral sensor, which contains key vegetation wavelengths. Partial least squares regression models were developed from the WorldView-2 bands and indices and significant predictors were identified by variable importance scores. The red edge and near-infrared bands of the WorldView-2 sensor, together with pigment specific indices predicted and mapped T. peregrinus damage with high accuracies. The study further combined environmental variables and vegetation indices calculated from the WorldView-2 imagery to improve the prediction and mapping of T. peregrinus damage using a multiple stepwise regression approach. The regression model selected the near infrared band 8 of the WorldView-2 sensor and the temperature dataset to predict and map T. peregrinus damage with high accuracies on an independent test dataset. This research contributes to the field of knowledge by developing innovative remote sensing techniques that can accurately detect and map T. peregrinus damage using the new generation WorldView-2 sensor. The result is significant for forest health monitoring and highlights the importance of improved sensors which contain key vegetation wavelengths for plantation health assessments. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

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.