  Understanding forest health is of great importance for the conservation of
the integrity of forest ecosystems. 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 machine learning techniques have
proven to be more efficient and sustainable with unprecedented accuracy in
forest inventory. This study, for the first time, automatically categorizing
individual coniferous trees (Norway spruce) into five decay stages (live,
declining, dead, loose bark, and clean) from combined airborne laser scanning
(ALS) point clouds and color infrared (CIR) images using three different
Machine Learning methods - 3D point cloud-based deep learning (KPConv),
Convolutional Neural Network (CNN), and Random Forest (RF). First, CIR
colorized point clouds are created by fusing the ALS point clouds and color
infrared images. Then, individual tree segmentation is conducted, after which
the results are further projected onto four orthogonal planes. Finally, the
classification is conducted on the two datasets (3D multispectral point clouds
and 2D projected images) based on the three Machine Learning algorithms. All
models achieved promising results, reaching overall accuracy (OA) of up to
88.8%, 88.4% and 85.9% for KPConv, CNN and RF, respectively. The experimental
results reveal that color information, 3D coordinates, and intensity of point
clouds have significant impact on the promising classification performance. The
performance of our models, therefore, shows the significance of machine/deep
learning for individual tree decay stages classification and landscape-wide
assessment of the dead wood amount and quality by using modern airborne remote
sensing techniques. The proposed method can contribute as an important and
reliable tool for monitoring biodiversity in forest ecosystems.
