Home About Browse Search
Svenska


Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

Huo, Langning and Lindberg, Eva (2020). Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data. International Journal of Remote Sensing. 41 , 9525-9544
[Journal article]

[img] PDF
20MB

Abstract

Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees.

Authors/Creators:Huo, Langning and Lindberg, Eva
Title:Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data
Year of publishing :2020
Volume:41
Page range:9525-9544
Number of Pages:20
Publisher:TAYLOR & FRANCIS LTD
ISSN:0143-1161
Language:English
Publication Type:Journal 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-108705
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-108705
Additional ID:
Type of IDID
DOI10.1080/01431161.2020.1800127
Web of Science (WoS)000583362400001
ID Code:18709
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:19 Nov 2020 14:28
Metadata Last Modified:19 Nov 2020 14:31

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits