Medium Resolution Image Fusion, Does It Enhance Forest Structure Assessment

This research explored the potential benefits of fusing optical and Synthetic Aperture Radar (SAR) medium resolution satellite-borne sensor data for forest structural assessment. Image fusion was applied as a means of retaining disparate data features relevant to modeling and mapping of forest structural attributes in even-aged (4-11 years) Eucalyptus plantations, located in the southern Kwazulu-Natal midlands of South Africa. Remote sensing data used in this research included the visible and near-infrared bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as a fine beam (6.25 m resolution) RadarSAT-1 image. Both data sets were collected during the spring of 2006 and fused using a modified discrete wavelet transformation. Spatially referenced forest inventory data were also collected during this time, with 122 plots enumerated in 38 plantation compartments. Empirical relationships (optimized multiple regression) were used to test whether fused data sources produced superior statistical models. Secondary objectives of the paper included exploring the role of scale in terms of forest modelling at the plot and extended plot levels (Voroni diagrams). Results indicated that even an optimized multiple regression approach did not return accuracies suitable for plantation forestry applications (adjusted R of 0.55 and 0.6 for basal area and merchantable volume respectively). No significant difference was found between fused and non-fused data sets, however optical and fused data sets produced superior models when compared to SAR results. No significant difference was found between field enumerated plot level modelling and Voroni level modelling with both data sets producing similar goodness of fit statistics. Findings indicate that the spatial resolutions of both sensors are inappropriate for plantation forest assessment. The frequency of the C-band Radarsat-1 image is for instance unable to penetrate the canopy and interact with the woody structures below canopy, leading to weak statistical models. The lack of variability in both the optical and SAR data lead to unconvincing results in the fused imagery, where in some cases the adjusted R results were worse than the single data set approach. It was concluded that future research should focus on high spatial resolution optical and LiDAR data and the development of automated and semi-automated forest inventory procedures.