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 article aims to meet two needs: (i) the need for skilled workers and students in the area of remote sensing and (ii) the need to make school science interesting for students. This article addresses both needs by proposing a project for high school students entitled “the Colors of Earth”. The main aim for students was to distinguish between different types of land cover via the creation of various false color band compositions from the satellite Sentinel-2. Achieving this goal requires knowledge from various subjects and enables their practical application via work performed using real data. The project was presented to 39 high-school teachers and 184 high-school students (K-9 and K-10) in the summer semester of the 2019/2020 school year, and their opinions about the project were collected. Overall, both students and teachers judged the project to be interesting, worth introducing to the school, and capable of influencing student opinions of science. In addition, introducing remote sensing elements during pre-university education can help meet the demands for students and workers to study Earth observation.

In this letter, we apply the multifractal formalism to land cover change detection on very high spatial resolution data. Specifically, multifractal spectra are determined and, with modifications, are used as an initial general indicator of change on the subsets of IKONOS and Pleiades images. Next, we calculate Hölder exponents for each pixel in the images and use them to generate a change mask. Our analysis shows that Hölder exponents enable a detailed evaluation of changes in land cover. A comparison with change detection based solely on panchromatic images shows that the multifractal description method has significant advantages as it reduces the number of false positives. In addition, we show that our change detection results are comparable with other multiscale techniques.

In the article a non-uniformity correction method is presented which allows to compensate for the influence of detector’s temperature drift. For this purpose, dependency between output signal value and the temperature of the detector array was investigated. Additionally the influence of the temperature on the Offset and Gain coefficients was measured. Presented method utilizes estimated dependency between output signal of detectors and their temperature. In the presented method, the shutter is used for establishing signal reference. Thermoelectric cooler is used for changing the temperature of the detector array.

In our work we analyse fractal and multifractal characteristics for description and extraction of information from very high spatial resolution satellite images. In particular, we propose the degree of multifractality as a parameter for extraction of four land cover types and investigate its usefulness in comparison with the fractal dimension. Results show that degree of multifractality designated for individual fragments of images differs depending on the present land cover type. The highest multifractality level is observed for urban area, the lowest for water, which can be treated as a monofractal. In general multifractal parameter allows for automatic assignment of land cover types to specific classes. Some deviations take place in case of discrimination between agricultural areas and forests. This problem is also considered by using additional measures during the process of multifractal parameter computation. Conducted analysis shows that multifractal formalism creates additional possibilities for the description and automatic classification of satellite images.