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UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Kamýcká 129, 165 00 Praha, Czech Republic
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3829;
Received: 14 October 2020 / Revised: 19 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels. View Full-Text
Keywords: ULS; LiDAR; forestry; canopy changes; branch bending; change detection ULS; LiDAR; forestry; canopy changes; branch bending; change detection
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MDPI and ACS Style

Slavík, M.; Kuželka, K.; Modlinger, R.; Tomášková, I.; Surový, P. UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy. Remote Sens. 2020, 12, 3829.

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