Validation of sentinel 2 based machine learning models for Czech National Forest Inventory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F25%3A43926916" target="_blank" >RIV/62156489:43110/25:43926916 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ecoinf.2025.103133" target="_blank" >https://doi.org/10.1016/j.ecoinf.2025.103133</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ecoinf.2025.103133" target="_blank" >10.1016/j.ecoinf.2025.103133</a>
Alternative languages
Result language
angličtina
Original language name
Validation of sentinel 2 based machine learning models for Czech National Forest Inventory
Original language description
The National Forest Inventory (NFI) of the Czech Republic provides essential data for forest management but requires significant time and resources. This study highlights the critical role of validating Sentinel-2-based machine learning models against real NFI data to ensure their reliability for forest monitoring. While satellite-based models offer a cost-effective alternative, their practical applicability depends on rigorous validation. We applied four commonly used machine learning models-Classification and Regression Trees, Random Forest, Support Vector Machine, and Naive Bayes-to Sentinel-2 imagery to estimate forest cover conditions. The Random Forest model achieved the highest overall accuracy (98.3 %). By systematically comparing model predictions with official NFI data, we address a key gap in remote sensing applications: the need for real-world validation beyond training datasets. Our findings demonstrate that properly validated Sentinel-2-based models can enhance large-scale forest monitoring, reducing the financial and labor burdens of traditional field surveys while ensuring data accuracy for sustainable forest management.
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
10103 - Statistics and probability
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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
1878-0512
Volume of the periodical
87
Issue of the periodical within the volume
July
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
8
Pages from-to
103133
UT code for WoS article
001464950200001
EID of the result in the Scopus database
2-s2.0-105001734292