The use of remotely sensed data to estimate forest attributes involves the acquisition of ground forest data. Recently the acquisition of ground data (field based) to estimate leaf area index (LAI) and biomass are becoming expensive and time consuming. Thus there is a need for an easy but yet effective means of predicting the LAI, which serves as an input to the forest growth prediction models and the quantification of water use by forests. The ability to predict LAI, biomass and eventually water use over a large area remotely using remotely sensed data is sought after by the forestry companies. Remotely sensed LAI values provide the opportunity to gain spatial information on plant biophysical attributes that can be used in spatial growth indices and process based growth models. In this study remotely sensed images were transformed into LAI value estimates, through the use of four vegetation indices (Normalized Difference Vegetation Index (NDVI), Corrected Normalized Difference Vegetation Index (NDVlc), Ratio Vegetation Index (RVI) and Normalized Ratio Vegetation Index (NRVI). Ground based measurements (Destructive Sampling and Leaf Canopy Analyzer) relating to LAI were obtained in order to evaluate the vegetation indices value estimates. All four vegetation indices values correlated significantly with the ground-based measurements, with the NDVI correlating the highest. These results suggested that NDVI is the best in estimating the LAI in Eucalyptus grandis x camaldulensis in the Zululand region with correlation coefficients of 0.78 for destructive sampling and 0.75 for leaf canopy analyzer. Visual inspection of scatter plots suggested that the relations between NDVI and ground based measurements were variable, with R2 values of 0.61 for destructive sampling and 0.55 for Leaf Canopy analyzer. These LAI estimates obtained through remotely sense data showed a great promise in South African estimation of LAI values of Eucalyptus grandis x camaldulensis. Thus water use and biomass can be quantified at a less expensive and time-consuming rate but yet efficiently and effectively. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2001.

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. / Thesis (M.Sc.) - University of KwaZulu-Natal, Pietermaritzburg, 2008.