Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/QK21010354" target="_blank" >QK21010354: Progresivní metody hospodářsko-úpravnického plánování pro podporu trvale udržitelného lesního hospodářství</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000769500300001

EID výsledku v databázi Scopus

2-s2.0-85125012751