Identifying Tree-Related Microhabitats in TLS Point Clouds Using Machine Learning

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.

Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements

“…The great number of plots and their distribution in the landscape required a very efficient sampling design, which is time-effective as well. Further advances in sensor technologies with very dense aerial or UAV LiDAR might overcome these shortcomings of incomplete representation of the geometry of the stand and make new structural indices or the full detection of TreMs possible [38,74]. The sampling effort of the different methods (manual inventories, UAV, mobile or terrestrial RS) differs strongly.…”

“…While different RS-and TreM-based studies have shown promising results for the quantification of diversity of different taxa [1,3,13,38], their potential as combined descriptors of biodiversity had not yet been researched. As technical progress advances, new options for the detection of particular TreMs at very fine scales will become available [38], and information on the most applicable set of predictors can help to identify the most objective, cost and time efficient inventory methods for the selection of key retention elements as habitat trees for forest biodiversity.…”

“…TreM field inventories are commonly used to assess the quality and quantity of habitat trees [34,36] as remote sensing is not yet able to detect the full set of these relatively small structural attributes directly at the tree-level [37,38]. Rehush et al [38] detected TreMs only on the stem section and just for beech (Fagus sylvatica L.) trees with advanced machine learning techniques and were able to detect those TreMs with an accuracy of up to 83%. However, close-range remote sensing techniques are able to describe small-scale structural complexity of forests in increasing detail [4,[39][40][41][42].…”

Biodiversity conservation and wood production in a Natura 2000 Mediterranean forest. A trade-off evaluation focused on the occurrence of microhabitats

“…A recent study from the Swiss Federal Research Institute WSL shows how data from Terrestrial Laser Scanning could be useful for identifying TreMs, through their inclusion in the National forest inventory (Rehush et al 2018). Additionally, recent improvements in the use of Airborne Laser Scanning data for monitoring forest resources, especially single tree detection, could be useful for mapping tallest and largest trees that often correspond to habitat trees.…”

Same Viewpoint Different Perspectives—A Comparison of Expert Ratings with a TLS Derived Forest Stand Structural Complexity Index

“…To our knowledge, there is no common agreement on the setup of the scanning design [30], but at least some specifications (minimum distance to the next tree, height above ground, vegetation phase) may be beneficial to gain comparable results. Further research needs to be done to directly detect and quantify additional structural features like canopy gaps [43], or TreMs [44], that are not ideally represented by the SSCI.…”