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

• Published in: Forests 2020-09, Vol.11 (9), p.941
• Main Authors: Waśniewski, Adam, Hościło, Agata, Zagajewski, Bogdan, Moukétou-Tarazewicz, Dieudonné
• Format: Article
• Language: English
• Subjects: Accuracy; Atmosphere; Band spectra; Classification; Classifiers; Coniferous forests; Digital Elevation Models; Environmental conditions; forest cover; forest type; Gabon; Heterogeneity; Image classification; Image contrast; Mangroves; Mapping; Normalized difference vegetative index; Plantations; Rainforests; random forest; Remote sensing; Satellite imagery; Satellites; Sentinel-2; Spectral bands; Support vector machines; Time series; tropical forest; Vegetation
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f11090941

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.