Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited