Image fusion for enhanced forest structural assessment

Abstract

This research explores the potential benefits of fusing active and passive medium-resolution satellite-borne sensor data for forest structural assessment. Image fusion was applied as a means of retaining disparate data features relevant to modelling 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 datasets 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 (ordinary and multiple regression) were used to test whether fused data sources produced superior statistical models. Secondary objectives of the article included exploring the roles of texture, derived from grey-level co-occurrence matrices, and scale in terms of forest modelling at the plot and extended plot levels (Voroni diagrams). Results indicate that single bands from both the optical and Synthetic Aperture Radar (SAR) datasets were not adept at modelling basal area and merchantable timber volume with adjusted R 2 (coefficient of determination) values < 0.3. An optimized multiple-regression approach (adjusted R 2) improved results based on mean, range and standard deviation statistics when compared to single bands, but were still not suitable for operational forest applications (basal area: R 2 = 0.55, volume: R 2 = 0.59). No significant difference was found between fused and non-fused datasets; however, optical and fused datasets produced superior models when compared to SAR results. Investigations into potential benefits of using textural indices and varied scales also returned inconclusive results. Findings indicate that the spatial resolutions of both sensors are inappropriate for plantation forest assessment. The frequency of the C-band Radarsat-1 data 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 2 results were worse than the single-dataset approach. It was concluded that future research should focus on high-spatial-resolution optical and Light Detection and Ranging (LiDAR) data and the development of automated and semi-automated forest-inventory procedures.