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Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning

Holmgren, Johan and Lindberg, Eva (2019). Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning. Remote Sensing Letters. 10 , 1143-1152
[Research article]

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Abstract

This letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model's suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40-97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84-99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data.

Authors/Creators:Holmgren, Johan and Lindberg, Eva
Title:Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning
Series Name/Journal:Remote Sensing Letters
Year of publishing :2019
Volume:10
Page range:1143-1152
Number of Pages:10
Publisher:TAYLOR & FRANCIS LTD
ISSN:2150-704X
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
URN:NBN:urn:nbn:se:slu:epsilon-p-101610
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-101610
Additional ID:
Type of IDID
DOI10.1080/2150704X.2019.1658237
Web of Science (WoS)000483611500001
ID Code:23369
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:29 Apr 2021 11:07
Metadata Last Modified:03 May 2021 06:52

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