Abstract
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.
Highlights
Forest resources fulfil many functions and constitute an important element of human life.Accurate information on the status of forest resources and their constant monitoring at local, regional and global scales is crucial for their sustainable management [1]
The main aim of this study is to examine the potential of Sentnel-2 data for forest cover and forest type classification in Gabon
The highest overall accuracy for the forest cover was obtained for the combination of Sentiel-2 bands at 10 m and 20 m spatial resolution and digital elevation model (DEM)
Summary
Forest resources fulfil many functions and constitute an important element of human life. Accurate information on the status of forest resources and their constant monitoring at local, regional and global scales is crucial for their sustainable management [1]. Information on forest dynamics is essential for deriving an extent and rate of deforestation [2]. Up-to-date forest maps are important for crisis management, rapid mapping of forest lost, or degradation due to natural hazards, e.g., fires, floods, and severe droughts [3]. In many European and North American countries, forest inventory is regularly carried out by ground-based measurement methods or sometimes using remote sensing techniques. The situation is more complex in developing countries, especially those located in the Forests 2020, 11, 941; doi:10.3390/f11090941 www.mdpi.com/journal/forests
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