A forestry investigation: Exploring factors behind improved tree species classification using bark images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F25%3A102279" target="_blank" >RIV/60460709:41320/25:102279 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ecoinf.2024.102932" target="_blank" >https://doi.org/10.1016/j.ecoinf.2024.102932</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ecoinf.2024.102932" target="_blank" >10.1016/j.ecoinf.2024.102932</a>
Alternative languages
Result language
angličtina
Original language name
A forestry investigation: Exploring factors behind improved tree species classification using bark images
Original language description
Novel ground-based remote sensing approaches have demonstrated high potential for accurate and detailed mapping and monitoring of forest ecosystems. These methods enable the measurement of various tree parameters important for forest inventory or ecological research, such as diameter at breast height, tree height and volume, and crown parameters. One crucial piece of information is tree species, which is essential for various reasons and challenging to implement within ground-based technology workflows. This study investigates why researchers often focus on segment-specific bark images for tree species classification via deep neural networks rather than large or entire tree images. Additionally, the aim is to determine the most effective algorithmic approaches for efficient tree species classification from bark images and to make these methods more accessible to interdisciplinary researchers. The findings reveal that segment-specific datasets with more overlaps provide better accuracy across various algorithms. Additionally, pre-processing techniques such as scaling can enhance accuracy to a certain extent. Convolutional Neural Networks (CNNs) consistently deliver the highest accuracy, even with diverse datasets, but fine-tuning these algorithms poses significant challenges for interdisciplinary researchers. To address this, we developed Windows-based research software, CNN Parameter Tuner 1.0, which allows the import of various data formats (jpg and png) and efficiently conducts parameter tuning by selecting parameters and values from the menu options.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10618 - Ecology
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Ecological Informatics
ISSN
1574-9541
e-ISSN
1574-9541
Volume of the periodical
85
Issue of the periodical within the volume
MAR 2025
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
22
Pages from-to
1-22
UT code for WoS article
001438292600001
EID of the result in the Scopus database
2-s2.0-85211221230