Object-based forest gaps classification using airborne LiDAR data

Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm. Airborne light detection and ranging (LiDAR; 3.7 points/m(2)) data were collected as the original data source and the canopy height model (CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of object-based forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely, forest gaps, tree canopies, and others. The common support vector machine (SVM) classifier with the radial basis function kernel (RBF) was first adopted to test the effect of classification features (vegetation height features and some typical topographic features) on forest gap classification. Then the different classifiers (KNN, Bayes, decision tree, and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Moller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales (10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was 91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF (90%), Decision Tree (90%), Bayes (90%), or KNN (87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale (s) of segmentation.