Tree parameter estimation with iPhone point cloud data using multiple algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F24%3A100362" target="_blank" >RIV/60460709:41320/24:100362 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/01431161.2024.2409996" target="_blank" >http://dx.doi.org/10.1080/01431161.2024.2409996</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/01431161.2024.2409996" target="_blank" >10.1080/01431161.2024.2409996</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Tree parameter estimation with iPhone point cloud data using multiple algorithms

Popis výsledku v původním jazyce

LiDAR technology introduced the possibility of indirectly estimating various tree parameters. In 2020, Apple incorporated LiDAR sensors into their iPhone 12 Pro. This brings an opportunity to make data collection and plot scanning incredibly convenient. The main aim is to search for a tool and software to estimate the most accurate Diameter at Breast Height (DBH) for individual trees. Therefore, this study focuses on using an iPhone 12 Pro for data collection of individual trees. The performance of three software tools (ForestScanner, rTLS, RANSAC) for the estimation of DBH with field-estimated DBH was compared. The investigation of the significance of the estimation of DBH for each tree species was performed using the three algorithms. In this context, the scanning of 123 trees comprising six species: pine, oak, beech, hornbeam, spruce, and fir was done. DBH was estimated of the scanned tree point cloud using a built-in algorithm of ForestScanner application in the iPhone 12 Pro, RANSAC (CloudCompare plugin), and rTLS (an R-based package). In this study, ForestScanner showed the best results. Therefore, ForestScanner can be used as a reliable tool for measuring DBH. Inferences confirmed by ANOVA and Tukey post-hoc tests show that species significantly influence DBH. Accurate DBH estimation is crucial, and using lightweight devices like the iPhone can revolutionize the forest inventory sector in terms of the estimation of DBH.

Název v anglickém jazyce

Tree parameter estimation with iPhone point cloud data using multiple algorithms

Popis výsledku anglicky

LiDAR technology introduced the possibility of indirectly estimating various tree parameters. In 2020, Apple incorporated LiDAR sensors into their iPhone 12 Pro. This brings an opportunity to make data collection and plot scanning incredibly convenient. The main aim is to search for a tool and software to estimate the most accurate Diameter at Breast Height (DBH) for individual trees. Therefore, this study focuses on using an iPhone 12 Pro for data collection of individual trees. The performance of three software tools (ForestScanner, rTLS, RANSAC) for the estimation of DBH with field-estimated DBH was compared. The investigation of the significance of the estimation of DBH for each tree species was performed using the three algorithms. In this context, the scanning of 123 trees comprising six species: pine, oak, beech, hornbeam, spruce, and fir was done. DBH was estimated of the scanned tree point cloud using a built-in algorithm of ForestScanner application in the iPhone 12 Pro, RANSAC (CloudCompare plugin), and rTLS (an R-based package). In this study, ForestScanner showed the best results. Therefore, ForestScanner can be used as a reliable tool for measuring DBH. Inferences confirmed by ANOVA and Tukey post-hoc tests show that species significantly influence DBH. Accurate DBH estimation is crucial, and using lightweight devices like the iPhone can revolutionize the forest inventory sector in terms of the estimation of DBH.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20700 - Environmental engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

INTERNATIONAL JOURNAL OF REMOTE SENSING

ISSN

0143-1161

e-ISSN

0143-1161

Svazek periodika

2024

Číslo periodika v rámci svazku

18.0

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

001331842400001

EID výsledku v databázi Scopus

2-s2.0-85206572899