Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

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Yrttimaa , T , Luoma , V , Saarinen , N , Kankare , V , Junttila , S , Holopainen , M , Hyyppä , J & Vastaranta , M 2020 , ' Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning ' , Remote Sensing , vol. 12 , no. 17 , 2672 , pp. 1-20 .

Title: Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning
Author: Yrttimaa, Tuomas; Luoma, Ville; Saarinen, Ninni; Kankare, Ville; Junttila, Samuli; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko
Contributor organization: Laboratory of Forest Resources Management and Geo-information Science
Department of Forest Sciences
Forest Health Group
Forest Ecology and Management
Date: 2020-09
Language: eng
Number of pages: 20
Belongs to series: Remote Sensing
ISSN: 2072-4292
Abstract: Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS inquantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindricalcharacteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and foreststands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (∆dbh), 44.14–55.49 cm2 for basal area (∆g), and 1.91–4.85 m for tree height (∆h). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted meandiameter (∆Dg), 0.81–2.26 m for changes in basal area-weighted mean height (∆Hg), 1.40–2.34 m2 /ha for changes in mean basal area (∆G), and 74–193 n/ha for changes in the number of trees per hectare (∆TPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.
Subject: 4112 Forestry
time series
ground-based LiDAR
tree growth
time series
ground-based LiDAR
tree growth
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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