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.

Monitoring of land cover plays an important role in effective environmental management, assessment of natural resources, environmental protection, urban planning and sustainable development. Increasing demand for accurate and repeatable information on land cover and land cover changes causes rapid development of the advanced, machine learning algorithms dedicated to land cover mapping using satellite images. Free and open access to Sentinel-2 data, characterized with high spatial and temporal resolution, increased the potential to map and to monitor land surface with high accuracy and frequency. Despite a considerable number of approaches towards land cover classification based on satellite data, there is still a challenge to clearly separate complex land cover classes, for example grasslands, arable land and wetlands. The aim of this study is to examine, whether a hierarchal classification of Sentinel-2 data can improve the accuracy of land cover mapping and delineation of complex land cover classes. The study is conducted in the Lodz Province, in central Poland. The pixel-based land cover classification is carried out using the machine learning Random Forest (RF) algorithm, based on a time series of Sentinel-2 imagery acquired in 2020. The following nine land cover classes are mapped: sealed surfaces, woodland broadleaved, woodland coniferous, shrubs, permanent herbaceous (grassy cover), periodically herbaceous (i.e., arable land), mosses, non-vegetated (bare soil) and water bodies. The land cover classification is conducted following two approaches: (1) flat, where all land cover classes are classified together, and (2) hierarchical, where the stratification is applied to first separate the most stable land cover classes and then classifying the most problematic once. The national databases served as the source of the reference sampling plots for the classification process. The process of selection and verification of the reference sampling plots is performed automatically. To assess the stability of the classification models the classification processes are performed iteratively. The results of this study confirmed that the hierarchical approach gave more accurate results compared to the commonly used flat approach. The median of the overall accuracy (OA) of the hierarchical classification was higher by 3–9 percentage points compared to the flat one. Of interest, the OA of the hierarchical classification reached 0.93–0.99, whereas the flat approach reached 0.90. Individual classes are also better classified in the hierarchical approach.

Forests play a vital role in ecology, economy, urban planning, and social well-being, emphasising the importance of monitoring forest cover and its changes. The study evaluates the ecological impact of the military conflict on forest ecosystem in Ukraine using a time series of Sentinel-2 data and machine learning algorithms. Forest losses following the beginning of the war were derived using a change detection method across the study areas. Two types of forest loss were delineated: conversion of woody areas to non-woody cover, and burnt forest. Before the war, forest loss was predominantly due to the conversion of woody to non-woody cover, accounting for 74% of total changes, while forest fires represented the remaining 26%. Following the outbreak of the conflict, the total area of forest loss doubled. Notably, the proportion of forest converted to non-woody cover decreased to 66%, while the proportion of burnt forest increased to 34%, evidencing the severe impact of military operations on forest ecosystem. Of interest, the area of forest converted to non-wood cover doubled between, possibly reflecting an increased demand for wood due to the conflict, potentially driven by a rise in legal and illegal logging as a result of weakened governance and reduced enforcement of environmental regulations.

Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.