Automatic Classification of Single Tree Decay Stages from Combined ALS Data and Aerial CIR Imagery using Machine Learning

Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems. The monitoring of forest health is, therefore, indispensable for the long-term conservation of forests and their sustainable management. In this regard, evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity. Apparently, remote sensing-based techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory. However, the application of these techniques is still in its infancy with respect to dead wood mapping. This study investigates for the first time the automatic classification of individual coniferous trees into five decay stages (live, declining, dead, loose bark, and clean) from combined airborne laser scanning data and color infrared images using Machine Learning methods. First, CIR colorized point clouds are created by fusing the ALS point clouds and the color infrared images. Then, with the colorized point cloud, individual tree segmentation is conducted using a semi-automatic approach, which are further projected onto four orthogonal planes displaying the side views of the trees in 2D. Finally, the classification is conducted on the multispectral point clouds and projected images using the three Machine Learning algorithms. All models achieved promising results, reaching overall accuracy (OA) of up to 90.9%, 90.6%, and 80.6% for CNN, RF, and PointNet, respectively. The experimental results reveal that the image-based approach notably outperformed the point cloud-based one, while spectral image texture is of the highest relevance to the success of categorizing tree decay. Our models could therefore be used for automatic determination of single tree decay stages and landscape-wide assessment of dead wood amount and quality using modern airborne remote sensing.