Validation of sentinel 2 based machine learning models for Czech National Forest Inventory

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Validation of sentinel 2 based machine learning models for Czech National Forest Inventory

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Validation of sentinel 2 based machine learning models for Czech National Forest Inventory

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

1878-0512

Svazek periodika

87

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

103133

Kód UT WoS článku

001464950200001

EID výsledku v databázi Scopus

2-s2.0-105001734292