NDLI: Rapid detection of stand density, tree positions, and tree diameter with a 2D terrestrial laser scanner

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Rapid detection of stand density, tree positions, and tree diameter with a 2D terrestrial laser scanner

Content Provider	Springer Nature Link
Author	Brunner, Andreas Gizachew, Belachew
Copyright Year	2014
Abstract	Harvester operators that decide about tree removal during thinnings have currently no instruments to measure stand density continuously before and after the operation. We tested whether basal area can be measured rapidly for this purpose with a 2D terrestrial laser scanner. An algorithm was developed, which automatically detects trees from laser scanner point clouds, measures their position and diameter, and calculates basal area. A field test included 18 laser scans in two Norway spruce stands with a wide range of stand densities, representing situations before and after thinning. Occlusion is a problem of single laser scans, and about one-third of the trees within the scanning range were not detected. Occlusion varies with stem density and branchiness. We therefore applied a flexible scanning range, which is detected automatically based on the laser hit density distribution for each scan. Scanning ranges were between 5.5 and 8.4 m (mean = 7.3 m) in the test scans, which is below the reach of the harvester crane, but still large enough to estimate local stand density. Basal area measured with the laser scanner was unbiased only in one of the two stands. Trees not detected or trees falsely detected caused only small bias of the basal area measurement in one of the two stands. Measurement errors for individual scans were, however, often around 10 m2 ha−1.
Starting Page	819
Ending Page	831
Page Count	13
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	133
Issue Number	5
e-ISSN	16124677
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-03-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Tree detection Algorithm Thinning operation Occlusion Variable scanning range Forestry Plant Sciences Plant Ecology
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

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