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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20705 - Remote sensing

Project

<a href="/en/project/CK02000203" target="_blank" >CK02000203: Monitoring and evaluation of risk phenomena in the vicinity of transport infrastructure using remote sensing</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

Urban Forestry & Urban Greening

ISSN

1618-8667

e-ISSN

1618-8667

Volume of the periodical

93

Issue of the periodical within the volume

128229

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

1-13

UT code for WoS article

001180281300001

EID of the result in the Scopus database

2-s2.0-85184007058