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Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

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cris.lastimport.scopus2024-02-12T19:31:18Z
dc.abstract.enThis study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorWaśniewski, Adam
dc.contributor.authorHościło, Agata
dc.contributor.authorZagajewski, Bogdan
dc.contributor.authorMoukétou-Tarazewicz, Dieudonné
dc.date.accessioned2024-01-24T16:57:12Z
dc.date.available2024-01-24T16:57:12Z
dc.date.copyright2020-08-28
dc.date.issued2020
dc.description.accesstimeAT_PUBLICATION
dc.description.financeŚrodki finansowe, o których mowa w art. 365 pkt. 2 ustawy
dc.description.number9
dc.description.versionFINAL_PUBLISHED
dc.description.volume11
dc.identifier.doi10.3390/F11090941
dc.identifier.issn1999-4907
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/101221
dc.identifier.weblinkhttps://www.mdpi.com/1999-4907/11/9/941/pdf
dc.languageeng
dc.pbn.affiliationearth and related environmental sciences
dc.relation.ispartofForests
dc.relation.pages941
dc.rightsCC-BY-SA
dc.sciencecloudnosend
dc.subject.enSentinel-2
dc.subject.enforest cover
dc.subject.entropical forest
dc.subject.enforest type
dc.subject.enGabon
dc.subject.enrandom forest
dc.titleAssessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon
dc.typeJournalArticle
dspace.entity.typePublication