Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F22%3A94082" target="_blank" >RIV/60460709:41320/22:94082 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2072-4292/14/4/926" target="_blank" >https://www.mdpi.com/2072-4292/14/4/926</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs14040926" target="_blank" >10.3390/rs14040926</a>
Alternative languages
Result language
angličtina
Original language name
Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data
Original language description
A linear mixed-effects model was used to relate crown width to height using an inventory plot as a random effect for trees in Czechia based on data from the National Forest Inventory (NFI). This model was used to estimate window size for a local maximum filter procedure (LMF) to detect individual tree tops in unmanned aerial laser scanning (ULS) point clouds of mixed species forest stands with diverse structures. Random model parameters were estimated for the study site based on several sample trees. Models calibrated with five or more samples achieved significantly better results (mean percentage error, MPE -0,17 for 5 samples) compared to when a fixed-effects model (MPE -0,62) was used. Lower performance was observed in dense stands with trees that were between 5 and 10 m in height. It was concluded that locally calibrated models predicting crown widths from tree heights might serve as a universal point of departure when searching for an optimal window size setting in LMF procedures.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20705 - Remote sensing
Result continuities
Project
<a href="/en/project/QK21010354" target="_blank" >QK21010354: Progressive methods of forest management planning for supporting sustainable forest management</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
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Volume of the periodical
14
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
15
Pages from-to
1-15
UT code for WoS article
000769500300001
EID of the result in the Scopus database
2-s2.0-85125012751