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Article

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

1
Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, University of Warsaw, Faculty of Geography and Regional Studies, Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
2
Centre of Applied Geomatics, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland
3
Department of Geographical, Environmental and Marine Sciences, Faculty of Letters and Human Sciences, University Omar Bongo of Libreville, Libreville PO Box 17.043, Gabon
*
Author to whom correspondence should be addressed.
Forests 2020, 11(9), 941; https://doi.org/10.3390/f11090941
Received: 23 July 2020 / Revised: 25 August 2020 / Accepted: 26 August 2020 / Published: 28 August 2020
(This article belongs to the Special Issue Random Forests for Forest Ecology)
This 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. View Full-Text
Keywords: Sentinel-2; random forest; Gabon; forest type; tropical forest; forest cover Sentinel-2; random forest; Gabon; forest type; tropical forest; forest cover
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MDPI and ACS Style

Waśniewski, A.; Hościło, A.; Zagajewski, B.; Moukétou-Tarazewicz, D. Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon. Forests 2020, 11, 941. https://doi.org/10.3390/f11090941

AMA Style

Waśniewski A, Hościło A, Zagajewski B, Moukétou-Tarazewicz D. Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon. Forests. 2020; 11(9):941. https://doi.org/10.3390/f11090941

Chicago/Turabian Style

Waśniewski, Adam, Agata Hościło, Bogdan Zagajewski, and Dieudonné Moukétou-Tarazewicz. 2020. "Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon" Forests 11, no. 9: 941. https://doi.org/10.3390/f11090941

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