Tree species classification in heterogeneous forest with hyperspectral data from unmanned aerial vehicle

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F25%3A10258854" target="_blank" >RIV/61989100:27350/25:10258854 - isvavai.cz</a>

Result on the web

<a href="https://journals.pan.pl/dlibra/publication/154152/edition/137225/content" target="_blank" >https://journals.pan.pl/dlibra/publication/154152/edition/137225/content</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24425/agg.2025.154152" target="_blank" >10.24425/agg.2025.154152</a>

Result language

angličtina

Original language name

Tree species classification in heterogeneous forest with hyperspectral data from unmanned aerial vehicle

Original language description

Hyperspectral data obtained from unmanned aerial vehicles (UAVs) provide high spectral and spatial resolution, bringing potential for an accurate identification of individual tree species. The aim of this paper was to thoroughly investigate the possibilities of forest tree classification using hyperspectral data taken by the Resonon Pika L camera. Both hyperspectral and ground reference data were collected for a heterogenous forest in the Czech Republic. Standard processing methods (radiometric, atmospheric, and geometric corrections) were applied, followed by testing the methods for reduction (spectral resampling, MNF, PCA) and PPI. The classification phase consists of both unsupervised and supervised approaches, including Maximum Likelihood, Mahalanobis Distance, Spectral Angle Mapper, Minimum Distance, Random Forest, Extra Trees, Support Vector Machines, and Naive Bayes. Within the classical classifiers, the best results were achieved using the Maximum Likelihood classifier. In terms of machine learning algorithms, the best performing classifiers were Random Forest and Extra Trees. The use of Pika L camera in forestry classification is so far minimal, therefore the results can be helpful in potential utilization of this type of camera. The findings of this research not only contribute to a better understanding of UAV-based hyperspectral remote sensing for tree classifications but also provide practical insights andrecommendations for improvement

Czech name

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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

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OECD FORD branch

20705 - Remote sensing

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Advances in Geodesy and Geoinformation

ISSN

2720-7242

e-ISSN

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Volume of the periodical

74

Issue of the periodical within the volume

2

Country of publishing house

PL - POLAND

Number of pages

13

Pages from-to

nestránkováno

UT code for WoS article

001628126500001

EID of the result in the Scopus database

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