Forest Stand Delineation Using a Hybrid Segmentation Approach Based on Airborne Laser Scanning Data

Forest stand delineation is an important task for forest management. Traditional manual stand delineation based on aerial color-infrared images is a labor intensive process and its results are partially subjective. These images are also highly affected by weather conditions and imaging parameters. In this work, we applied a hybrid segmentation approach on Airborne Laser Scanning (ALS) data to delineate forest stands. The ALS data was firstly pre-processed to extract a three band feature image, containing tree height, density, and species information, respectively. Then the image was segmented by the mean shift algorithm to generate raw stands, which were refined by the Spectral Clustering (SC) algorithm in the following stage. In the SC algorithm, we also estimated the number of stands based on eigengap heuristics. We tested our method on real ALS data acquired at Juuka in Finland, and compared the results with the manually delineated result visually and numerically, as well as results based on previous methods. The experimental results showed that our method worked well for the forest stand delineation based on ALS data, and return better results in most cases when compared to previous methods.