Several alien plants are invading subtropical forest ecosystems through canopy gaps, resulting in the loss of native species biodiversity. The loss of native species in such habitats may result in reduced ecosystem functioning. The control and eradication of these invaders requires accurate mapping of the levels of invasion in canopy gaps. Our study tested (i) the utility of WorldView-2 imagery to map forest canopy gaps, and (ii) an integration of WorldView-2 data with environmental data to model the probability of occurrence of invasive Chromolaena odorata (triffid weed) in Dukuduku forest canopy gaps of KwaZulu- Natal, South Africa. Both pixel-based classification and object-based classification were explored for the delineation of forest canopy gaps. The overall classification accuracies increased by ± 12% from a spectrally resampled 4 band image similar to Landsat (74.64%) to an 8 band WorldView-2 imagery (86.90%). This indicates that the new bands of WorldView such as the red edge band can improve on the capability of common red, blue, green and near-infrared bands in delineating forest canopy gaps. The maximum likelihood classifier (MLC) in pixel-based classification yielded the overall classification accuracy of 86.90% on an 8 band WorldView-2 image, while the modified plant senescence reflectance index (mPSRI) in object-based classification yielded 93.69%. The McNemar’s test indicated that there was a statistical difference between the MLC and the mPSRI. The mPSRI is a vegetation index that incorporates the use of the red edge band, which solves a saturation problem common in sensors such as Landsat and SPOT. An integrated model (with both WorldView-2 data and environmental data) used to predict the occurrence of Chromolaena odorata in forest gaps yielded a deviance of about 42% (D2 = 0.42), compared to the model derived from environmental data only (D2 = 0.12) and WorldView-2 data only (D2 = 0.20). A D2 of 0.42 means that a model can explain about 42% of the variability of the presence/absence of Chromolaena odorata in forest gaps. The Distance to Stream and Aspect were the significant environmental variables (ρ < 0.05) which were positively correlated with presence/absence of Chromolaena in forest gaps. WorldView-2 bands such as the coastal band (λ425 nm) yellow band (λ605 nm) and the nearinfrared- 1 (λ833 nm) are positively and significantly related to the presence/absence of invasive species (ρ < 0.05). On the other hand, a significant negative correlation (ρ < 0.05) of near-infrared-2 band (λ950 nm) and the red edge normalized difference vegetation index (NDVI725) suggests that the probability of occurrence of invasive Chromolaena increases forest gaps with low vegetation density. This study highlights the importance of WorldView- 2 imagery and its application in subtropical indigenous coastal forest monitoring. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

This study has provided a landscape level estimate of leaf area index (LAI) and net primary productivity (NPP) for a temperate broadleaf forest ecosystem in south-central Indiana. The estimates were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products LAI and NPP from both spatial and temporal perspectives. The evidence suggests that field-based estimates were poorly correlated with global MODIS data due to the simplifying assumptions of the MODIS global applicability, saturation problems of the red reflectance in highly vegetated areas, homogeneous land cover types of the study area, and other design assumptions of the field-based estimates. To improve the localized applicability of MODIS product algorithms, an empirical and localized algorithm combining in-situ measurements, the buildup of a localized biophysical model, and remote sensing-derived data were suggested for each local-scaled ecosystem. / Department of Natural Resources and Environmental Management

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To produce high quality paper, lignin should be removed from the pulp. Quantification of lignin concentrations using standard wet chemistry is accurate but time consuming and costly, thus not appropriate for a large number of samples. The ability of hyperspectral remote sensing to predict foliar lignin concentrations could be utilized to estimate wood lignin concentrations if meaningful relationships between wood and foliar chemistry are established. LAI (leaf area index) is a useful parameter that is incorporated into physiological models in forest assessment. Measuring LAI over vast areas is labour intensive and expensive; therefore, LAI has been correlated to vegetation indices using remote sensing. Broadband indices use average spectral information over broad bandwidths; therefore details on the characteristics of the forest canopy are compromised and averaged. Moreover, the broadband indices are known to be highly affected by soil background at low vegetation cover. The aim of this study is to determine foliar and wood lignin concentrations of Eucalyptus clones using hyperspectral lignin indices, and to estimate LAI of Eucalyptus clones from narrowband vegetation indices in Zululand, South Africa Twelve Eucalyptus compartments of ages between 6 and 9 years were selected and 5 trees were randomly sampled from each compartment. A Hyperion image was acquired within ten days of field sampling, SI and LAI measurements. Leaf samples were analyzed in the laboratory using the Klason method as per Tappi standards (Tappi, 1996-1997). Wood samples were analyzed for lignin concentrations using a NIRS (Near Infrared Spectroscopy) instrument. The results showed that there is no general model for predicting wood lignin concentrations from foliar lignin concentrations in Eucalyptus clones of ages between 6 and 9 years. Regression analysis performed for individual compartments and on compartments grouped according to age and SI showed that the relationship between wood and foliar lignin concentration is site and age specific. A Hyperion image was georeferenced and atmospherically corrected using ENVI FLAASH 4.2. The equation to calculate lignin indices for this study was: L1R= ~n5il: A'''''y . 1750 AI680 The relationship between the lignin index and laboratory-measured foliar lignin was significant with R2 = 0.79. This relationship was used to calculate imagepredicted foliar lignin concentrations. Firstly, the compartment specific equations were used to calculate predicted wood lignin concentrations from predicted foliar lignin concentrations. The relationship between the laboratorymeasured wood lignin concentrations and predicted wood lignin concentrations was significant with R2 = 0.91. Secondly, the age and site-specific equations were used to convert foliar lignin concentration to wood lignin concentrations. The wood lignin concentrations predicted from these equations were then compared to the laboratory-measured wood lignin concentrations using linear regression and the R2 was 0.79 with a p-value lower than 0.001. Two bands were used to calculate nine vegetation indices; one band from the near infrared (NIR) region and the other from the short wave infrared (SWIR). Correlations between the Vis and the LAI measurements were generated and . then evaluated to determine the most effective VI for estimating LAI of Eucalyptus plantations. All the results obtained were significant but the NU and MNU showed possible problems of saturation. The MNDVI*SR and SAVI*SR produced the most significant relationships with LAI with R2 values of 0.899 and 0.897 respectively. The standard error for both correlations was very low, at 0.080, and the p-value of 0.001. It was concluded that the Eucalyptus wood lignin concentrations can be predicted using hyperspectral remote sensing, hence wood and foliar lignin concentrations can be fairly accurately mapped across compartments. LAI significantly correlated to eight of the nine selected vegetation indices. Seven Vis are more suitable for LAI estimations in the Eucalyptus plantations in Zululand. The NU and MNU can only be used for LAI estimations in arid or semi-arid areas. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.