Browsing by Author "Oumar, Zakariyyaa."

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Field spectroscopy of plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa
(2008) Oumar, Zakariyyaa.
The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. The aim of this research was to assess the relationship between plant water content and reflectance data in Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa. Field reflectance and first derivative reflectance data were correlated with plant water content. The first derivative reflectance performed better than the field reflectance data in estimating plant water content with high correlations in the visible and mid-infrared portions of the electromagnetic spectrum. Several reflectance indices were also tested to evaluate their effectiveness in estimating plant water content and were compared to the red edge position. The red edge position calculated from the first derivative reflectance and from the linear four-point interpolation method performed better than all the water indices tested. It was therefore concluded that the red edge position can be used in association with other water indices as a stable spectral parameter to estimate plant water content on hyperspectral data. The South African satellite SumbandilaSat is due for launch in the near future and it is essential to test the utility of this satellite in estimating plant water content, a study which has not been done before. The field reflectance data from this study was resampled to the SumbandilaSat band settings and was put into a neural network to test its potential in estimating plant water content. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41 on an independent test dataset outperforming the traditional multiple regression method of estimation. The potential of the SumbandilaSat wavebands to estimate plant water content was tested using a sensitivity analysis. The results from the sensitivity analysis indicated that the xanthophyll, blue and near infrared wavebands are the three most important wavebands used by the neural network in estimating plant water content. It was therefore concluded that these three bands of the SumbandilaSat are essential for plant water estimation. In general this study showed the potential of up-scaling field spectral data to the SumbandilaSat, the second South African satellite scheduled for launch in the near future.
Remote sensing of forest health : the detection and mapping of Thaumastocoris peregrinus damage in plantation forests.
(2012) Oumar, Zakariyyaa.; Mutanga, Onisimo.
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.