A Tree Segmentation Algorithm for Airborne Light Detection and Ranging Data Based on Graph Theory and Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F24%3A10255102" target="_blank" >RIV/61989100:27350/24:10255102 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43410/24:43925507

Výsledek na webu

<a href="https://www.mdpi.com/1999-4907/15/7/1111" target="_blank" >https://www.mdpi.com/1999-4907/15/7/1111</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/f15071111" target="_blank" >10.3390/f15071111</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Tree Segmentation Algorithm for Airborne Light Detection and Ranging Data Based on Graph Theory and Clustering

Popis výsledku v původním jazyce

This paper presents a single tree segmentation method applied to 3D point cloud data acquired with a LiDAR scanner mounted on an unmanned aerial vehicle (UAV). The method itself is based on clustering methods and graph theory and uses only the spatial properties of points. Firstly, the point cloud is reduced to clusters with DBSCAN. Those clusters are connected to a 3D graph, and then graph partitioning and further refinements are applied to obtain the final segments. Multiple datasets were acquired for two test sites in the Czech Republic which are covered by commercial forest to evaluate the influence of laser scanning parameters and forest characteristics on segmentation results. The accuracy of segmentation was compared with manual labels collected on top of the orthophoto image and reached between 82 and 93% depending on the test site and laser scanning parameters. Additionally, an area-based approach was employed for validation using field-measured data, where the distribution of tree heights in plots was analyzed.

Název v anglickém jazyce

A Tree Segmentation Algorithm for Airborne Light Detection and Ranging Data Based on Graph Theory and Clustering

Popis výsledku anglicky

This paper presents a single tree segmentation method applied to 3D point cloud data acquired with a LiDAR scanner mounted on an unmanned aerial vehicle (UAV). The method itself is based on clustering methods and graph theory and uses only the spatial properties of points. Firstly, the point cloud is reduced to clusters with DBSCAN. Those clusters are connected to a 3D graph, and then graph partitioning and further refinements are applied to obtain the final segments. Multiple datasets were acquired for two test sites in the Czech Republic which are covered by commercial forest to evaluate the influence of laser scanning parameters and forest characteristics on segmentation results. The accuracy of segmentation was compared with manual labels collected on top of the orthophoto image and reached between 82 and 93% depending on the test site and laser scanning parameters. Additionally, an area-based approach was employed for validation using field-measured data, where the distribution of tree heights in plots was analyzed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10500 - Earth and related environmental sciences

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

Forests

ISSN

1999-4907

e-ISSN

1999-4907

Svazek periodika

15

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001277666400001

EID výsledku v databázi Scopus

2-s2.0-85199630881