Automatic forest cover classification using Sentinel-2 multispectral satellite data and machine learning algorithms in Google Earth Engine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10476948" target="_blank" >RIV/00216208:11310/23:10476948 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dcHh9e7F2Y" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dcHh9e7F2Y</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic forest cover classification using Sentinel-2 multispectral satellite data and machine learning algorithms in Google Earth Engine

Popis výsledku v původním jazyce

Forest cover plays an essential role in maintaining ecological equilibrium, mitigating climate change, and securing a sustainable future for both humanity and the planet. Most countries conduct forest inventory or remote sensing surveys every few years to monitor changes in forest cover. However, only a few initiatives offer more frequent updates, typically weekly or monthly, focusing exclusively on areas experiencing high rates of deforestation or those of significant ecological value. The present study focuses on the classification of forest cover throughout Slovakia, covering the period 2017-2022, using Sentinel-2 multispectral satellite imagery along with the machine learning (ML) algorithms Random Forest (RF) and Support Vector Machine (SVM). The computation was performed in the cloud-based Google Earth Engine (GEE) platform, which offers a versatile interface for a broad range of computational capabilities for geospatial analysis and landscape monitoring. Forest cover change processing and evaluation is based on the RF classification algorithm, which demonstrated higher accuracy than the SVM classifier. The results indicate that RF outperformed SVM by 4% and 21% in 2017 and 2020, respectively. The RF algorithm achieved an overall accuracy (OA) of 95% in both classification cases (2017 and 2020) and F1 score of up to 0.95. The selected RF algorithm revealed an increase in forest cover in Slovakia, particularly notable during the period 2017-2019, with a slight decrease detected between 2019 and 2020. Furthermore, it was determined that the current forest cover is lower than that reported in official state statistics and land cover databases. Additionally, a user-friendly automatic tool for forest cover classification was developed and made freely available in GEE. This tool can benefit foresters, urban planners, and everyday users by detecting subtle changes in forest cover, crucial for forest sustainability and human well-being.

Název v anglickém jazyce

Automatic forest cover classification using Sentinel-2 multispectral satellite data and machine learning algorithms in Google Earth Engine

Popis výsledku anglicky

Forest cover plays an essential role in maintaining ecological equilibrium, mitigating climate change, and securing a sustainable future for both humanity and the planet. Most countries conduct forest inventory or remote sensing surveys every few years to monitor changes in forest cover. However, only a few initiatives offer more frequent updates, typically weekly or monthly, focusing exclusively on areas experiencing high rates of deforestation or those of significant ecological value. The present study focuses on the classification of forest cover throughout Slovakia, covering the period 2017-2022, using Sentinel-2 multispectral satellite imagery along with the machine learning (ML) algorithms Random Forest (RF) and Support Vector Machine (SVM). The computation was performed in the cloud-based Google Earth Engine (GEE) platform, which offers a versatile interface for a broad range of computational capabilities for geospatial analysis and landscape monitoring. Forest cover change processing and evaluation is based on the RF classification algorithm, which demonstrated higher accuracy than the SVM classifier. The results indicate that RF outperformed SVM by 4% and 21% in 2017 and 2020, respectively. The RF algorithm achieved an overall accuracy (OA) of 95% in both classification cases (2017 and 2020) and F1 score of up to 0.95. The selected RF algorithm revealed an increase in forest cover in Slovakia, particularly notable during the period 2017-2019, with a slight decrease detected between 2019 and 2020. Furthermore, it was determined that the current forest cover is lower than that reported in official state statistics and land cover databases. Additionally, a user-friendly automatic tool for forest cover classification was developed and made freely available in GEE. This tool can benefit foresters, urban planners, and everyday users by detecting subtle changes in forest cover, crucial for forest sustainability and human well-being.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10508 - Physical geography

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Acta Geographica Universitatis Comenianae

ISSN

1338-6034

e-ISSN

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Svazek periodika

67

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

23

Strana od-do

163-185

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85180643497