Airborne laser scanning reveals large tree trunks on forest floor

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http://hdl.handle.net/10138/330351

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Heinaro , E , Tanhuanpaa , T , Yrttimaa , T , Holopainen , M & Vastaranta , M 2021 , ' Airborne laser scanning reveals large tree trunks on forest floor ' , Forest Ecology and Management , vol. 491 , 119225 . https://doi.org/10.1016/j.foreco.2021.119225

Title: Airborne laser scanning reveals large tree trunks on forest floor
Author: Heinaro, Einari; Tanhuanpaa, Topi; Yrttimaa, Tuomas; Holopainen, Markus; Vastaranta, Mikko
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
Date: 2021-07-01
Language: eng
Number of pages: 17
Belongs to series: Forest Ecology and Management
ISSN: 0378-1127
URI: http://hdl.handle.net/10138/330351
Abstract: Fallen trees decompose on the forest floor and create habitats for many species. Thus, mapping fallen trees allows identifying the most valuable areas regarding biodiversity, especially in boreal forests, enabling well-focused conservation and restoration actions. Airborne laser scanning (ALS) is capable of characterizing forests and the underlying topography. However, its potential for detecting and characterizing fallen trees under varying boreal forest conditions is not yet well understood. ALS-based fallen tree detection methods could improve our understanding regarding the spatiotemporal characteristics of dead wood over large landscapes. We developed and tested an automatic method for mapping individual fallen trees from an ALS point cloud with a point density of 15 points/m2. The presented method detects fallen trees using iterative Hough line detection and delineates the trees around the detected lines using region growing. Furthermore, we conducted a detailed evaluation of how the performance of ALS-based fallen tree detection is impacted by characteristics of fallen trees and the structure of vegetation around them. The results of this study showed that large fallen trees can be detected with a high accuracy in old-growth forests. In contrast, the detection of fallen trees in young managed stands proved challenging. The presented method was able to detect 78% of the largest fallen trees (diameter at breast height, DBH > 300 mm), whereas 30% of all trees with a DBH over 100 mm were detected. The performance of the detection method was positively correlated with both the size of fallen trees and the size of living trees surrounding them. In contrast, the performance was negatively correlated with the amount of undergrowth, ground vegetation, and the state of decay of fallen trees. Especially undergrowth and ground vegetation impacted the performance negatively, as they covered some of the fallen trees and lead to false fallen tree detections. Based on the results of this study, ALS-based collection of fallen tree information should be focused on old-growth forests and mature managed forests, at least with the current operative point densities.
Subject: Airborne laser scanning
Light detection and ranging
Dead wood
Fallen trees
Biodiversity
Hough transform
COARSE WOODY DEBRIS
NORWAY SPRUCE
DEAD WOOD
VEGETATION
ECOLOGY
MODELS
BIRCH
STAND
4112 Forestry
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