ALS data based forest stand delineation with a coarse-to-fine segmentation approach

Forest stands are important forest management units for many forestry applications. Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR), provides abundant information on vertical structures of forests, and it is more stable than the conventional aerial or satellite spectral imaging in various conditions. In order to obtain accurate Forest Stand Delineations (FSD), a method using a coarse-to-fine segmentation approach solely based on ALS data is proposed in this work. Firstly, a three-band feature image is extracted from ALS point clouds. Secondly, from the feature image, coarse forest stands are generated with the mean-shift algorithm. Then each coarse stand is refined by Simple Linear Iterative Clustering superpixels based Seeded Region Growing (SLIC-SRG) to generate final forest stands. Also we process separately the forest and non-forest areas to increase the delineation accuracy. Moreover, some parameters of our method have physical meanings in FSD, easing the parameter setting process. Our method was tested on real ALS data and compared with other methods. Experimental results show that our method outperforms previous methods in terms of accuracies for ALS data based FSD.