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]
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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 | ||||||
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Title: | Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data | ||||||
Series Name/Journal: | International Journal of Remote Sensing | ||||||
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: |
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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: | 15 Jan 2021 19:22 |
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