UAV leaf-on, leaf-off and ALS-aided tree height: A case study on the trees in the vicinity of roads

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F24%3A98314" target="_blank" >RIV/60460709:41330/24:98314 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.ufug.2024.128229" target="_blank" >https://doi.org/10.1016/j.ufug.2024.128229</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ufug.2024.128229" target="_blank" >10.1016/j.ufug.2024.128229</a>

Jazyk výsledku

angličtina

Název v původním jazyce

UAV leaf-on, leaf-off and ALS-aided tree height: A case study on the trees in the vicinity of roads

Popis výsledku v původním jazyce

The safety of critical traffic and energy infrastructure is often threatened by surrounding vegetation. We compare the accuracy of six canopy height models (CHMs) created by combining UAV-borne digital leaf-off and leaf-on surface/terrain models with nationwide sparse airborne laser scanning (ALS) data across six different study sites. We conducted the statistical evaluation at three levels for all involved samples, distinguishing, among others, between trees at the edge and inside the forest, as well as between conifers and deciduous trees. We hypothesised that combining UAV-borne leaf-on and leaf-off data or a combination of fine-scale UAV data with broader-scale ALS may benefit specific tasks associated with vegetation dynamics or precise inventory. However, the UAV-borne CHM using leaf-on imagery yielded the best overall accuracy (MAE 1.77 m), performing best both for trees at the forest edges (MAE 1.59 m) and inside the forest (MAE 2.12 m). This dataset also performed best for deciduous trees (MAE 1.84 m) while for conifers, UAV-borne CHM using leaf-off imagery performed best (MAE 1.58 m); the differences between these two models were, however, quite small and the model based on the combination of leaf-on and leaf-off imagery performed similarly well. We conclude that UAV-based CHMs are of sufficient accuracy and adding low-resolution ALS-based terrain data does not enhance their performance. Considering the simplicity, the leaf-on UAV is sufficient for everyday forestry practice where it could replace time-consuming and laborious field surveys.

Název v anglickém jazyce

UAV leaf-on, leaf-off and ALS-aided tree height: A case study on the trees in the vicinity of roads

Popis výsledku anglicky

The safety of critical traffic and energy infrastructure is often threatened by surrounding vegetation. We compare the accuracy of six canopy height models (CHMs) created by combining UAV-borne digital leaf-off and leaf-on surface/terrain models with nationwide sparse airborne laser scanning (ALS) data across six different study sites. We conducted the statistical evaluation at three levels for all involved samples, distinguishing, among others, between trees at the edge and inside the forest, as well as between conifers and deciduous trees. We hypothesised that combining UAV-borne leaf-on and leaf-off data or a combination of fine-scale UAV data with broader-scale ALS may benefit specific tasks associated with vegetation dynamics or precise inventory. However, the UAV-borne CHM using leaf-on imagery yielded the best overall accuracy (MAE 1.77 m), performing best both for trees at the forest edges (MAE 1.59 m) and inside the forest (MAE 2.12 m). This dataset also performed best for deciduous trees (MAE 1.84 m) while for conifers, UAV-borne CHM using leaf-off imagery performed best (MAE 1.58 m); the differences between these two models were, however, quite small and the model based on the combination of leaf-on and leaf-off imagery performed similarly well. We conclude that UAV-based CHMs are of sufficient accuracy and adding low-resolution ALS-based terrain data does not enhance their performance. Considering the simplicity, the leaf-on UAV is sufficient for everyday forestry practice where it could replace time-consuming and laborious field surveys.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/CK02000203" target="_blank" >CK02000203: Monitoring a vyhodnocení rizikových jevů v okolí dopravní infrastruktury s využitím DPZ</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Urban Forestry & Urban Greening

ISSN

1618-8667

e-ISSN

1618-8667

Svazek periodika

93

Číslo periodika v rámci svazku

128229

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

001180281300001

EID výsledku v databázi Scopus

2-s2.0-85184007058