Abstract
Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention in ecosystem conservation, forest management and research. However, TreMs have until now only been assessed by experts during field surveys, which are time-consuming and difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different TreMs (bark, bark pockets, cavities, fungi, ivy and mosses) in dense TLS point clouds using machine learning algorithms, including deep learning. To classify the TreMs, we applied: (1) the Random Forest (RF) classifier, incorporating frequently used local geometric features and two additional self-developed orientation features, and (2) a deep Convolutional Neural Network (CNN) trained using rasterized multiview orthographic projections (MVOPs) containing top view, front view and side view of the point’s local 3D neighborhood. The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized MVOPs are even more suitable. Whereas the overall accuracy of the RF was 70%, that of the deep CNN was substantially higher (83%). This study reveals that close-range TLS is promising for the semi-automated identification of TreMs for forest monitoring purposes, in particular when applying deep learning techniques.
Highlights
Monitoring of forest biodiversity is a key issue in the context of sustainable forest management [1]
We evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different Tree-related microhabitats (TreMs) in dense TLS point clouds using machine learning algorithms, including deep learning
The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized multiview orthographic projections (MVOPs) are even more suitable
Summary
Monitoring of forest biodiversity is a key issue in the context of sustainable forest management [1]. Multi-purpose forests need to provide habitats for animals and plants to fulfill their ecological function, as well as performing economic and social functions. Stem structures, such as cavities, epiphytes and other tree-related microhabitats (TreMs), serve as habitats at the tree level and are existential for a wide range of insects, birds and mammal species during their life cycles [2,3]. TreM assessment in the field has so far been mostly conducted in small forest areas and rarely in national forest inventories [13]. In the Swiss National Forest Inventory (NFI), TreMs have been partially recorded in the field survey for 35 years [10]. Reproducible and efficient methods for assessing TreMs are needed for future NFI surveys
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