Support Vector Machines Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning

Airborne laser scanning is nowadays widely used for the estimation of forest stand parameters. Prediction models have to deal with high dimensional laser data sets as well as limited field calibration data. This problem is enhanced in mountainous areas where forest is highly heterogeneous and field data collection costly. Artificial neural network models and support vector regression (SVR) have already demonstrated their ability to address such issues for species specific plot volume prediction. In this paper we compare the stand parameters prediction accuracies of support vector machines and ordinary least squares multiple regression models for dominant height, basal area, mean diameter and stem density. Sensitivity of these techniques to the input variables is investigated by testing data sets including different number and types of laser metrics, and by reducing their dimension with principal component and independent component analyzes. Whereas usual variables only reflect the vertical distribution, we also integrate the entropy of the horizontal distribution of the point cloud in the laser metrics. Results show that SVR prediction models are of similar accuracy than multiple regression models, but are more robust regarding the metrics included in the data sets. Preliminary dimension reduction of the data set by principal component analysis generally benefits more to SVR than to multiple regression. The optimal combination of laser metrics to be included in the data sets mainly depends on the forest parameter to be estimated.