A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging)  Forests play a critical role in the global carbon budget, as they dominate the dynamics of the terrestrial carbon cycle (Dong et al., 2003) ; where, for example, 90 percent of above-ground carbon is stored in tree stems (Hese et al., 2005) . Airborne lidar (light detection and ranging), a recent remote sensing tool, has demonstrated the ability to characterize forest vertical structure (e.g., canopy height), leading to the accurate estimation of forest above-ground biomass (AGB) and timber volume (Means et al., 1999; Lefsky et al., 2002; Lim et al., 2003) . However, the current In an effort to overcome this cost limitation, recent studies report on the integration of lidar transects with optical remotely sensed data to estimate forest vertical structure (Hudak et al., 2002; Wulder and Seemann; Hilker et al., 2008; Chen and Hay, 2011) . The primary strategy is to generalize canopy height information from a relatively small area covered by lidar transects to the entire study area, covered by the optical scene. Multiple regression, as a standard statistical technique, is widely used in these studies. However, linear or other simple nonlinear (e.g., logarithmic or exponential) multiple regression models seldom fully characterize forest complexity, especially at fine scales, due to the high structural variability within small tree clusters when using high spatial resolution imagery (i.e., less than 5.0 m). Support vector machines (SVMs), originating from statistical learning theory, provide the capability to deal with highly nonlinear problems (Vapnik, 1995 and 1998) such as estimating complex forest structures. Additionally, SVMs are (a) robust in generalization, even when the training data are noisy, and (b) are guaranteed to have a unique global solution, that is not trapped in multiple local minima (Cristianini and Shawe-Taylor, 2000) . In forest remote sensing studies, SVMs have proven their use in the domain of classification (Huang et al., 2008; Kuemmerle et al., 2009) . However, few studies have investigated the application of support vector regression (also known as SVMs for regression, hereafter SVR) to estimate forest biophysical parameters, especially their vertical characteristics. To reduce high costs for lidar acquisition for forest parameterization while improving model accuracies, the primary objective of this study is to investigate the potential of SVR machine learning models to estimate forest biophysical parameters (i.e., canopy height, AGB andvolume) for a full study site by combining (smaller-area) lidar transects and QuickBird imagery. To do so, weproceed by: (a) applying a GEOBIA (geographic object-based image analysis) approach to extract forest characteristics at a small tree cluster level, (b) developing SVR models to estimate forest biophysical parameters, and (c) comparing the model performance between SVR and multiple regression.
Thus, Chen and Hay REF used Support Vector Regression (SVR) to estimate biophysical features of vegetation from LiDAR data and multispectral images outperforming classical stepwise regression.
A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data
Photogrammetric Engineering and Remote Sensing
Geography