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A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging) data is to combine lidar transects and optical satellite imagery to characterize forest vertical structure. Although multiple regression is typically used for such modeling, it seldom fully captures the complex relationships between forest variables. In an effort to improve these relationships, this study investigated the potential of Support Vector Regression (SVR), a machine learning technique, to generalize (lidar-measured) forest canopy height from four lidar transects (representing 8.8 percent, 17.6 percent, 26.4 percent and 35.2 percent area of the site) to the entire study area using QuickBird imagery. The best estimated canopy height was then linked with field measurements to predict actual canopy height, above-ground biomass (AGB) and volume. GEOgraphic Object-Based Image Analysis (GEOBIA) was used to generate all estimates at a small tree/cluster level with a mean object size (MOS) of 0.04 ha for conifer and deciduous trees. Results show that for all lidar transect samples, SVR models achieved better performance for

A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data