In 2011 the Marshal Office of Małopolska Voivodeship decided to evaluate the vulnerability of soils to water erosion for the entire region. The quantitative and qualitative assessment of the erosion risk for the soils of the Małopolska region was done based on the USLE approach. The special work-flow of geoinformation technologies was used to fulfil this goal. A high-resolution soil map, together with rainfall data, a detailed digital elevation model and statistical information about areas sown with particular crops created the input information for erosion modelling in GIS environment. The satellite remote sensing technology and the object-based image analysis (OBIA) approach gave valuable support to this study. RapidEye satellite images were used to obtain the essential up-to-date data about land use and vegetation cover for the entire region (15,000 km²). The application of OBIA also led to defining the direction of field cultivation and the mapping of contour tillage areas. As a result, the spatially differentiated values of erosion control practice factor were used. Both, the potential and the actual soil erosion risk were assessed quantificatively and qualitatively. The results of the erosion assessment in the Małopolska Voivodeship reveal the fact that a majority of its agricultural lands is characterized by moderate or low erosion risk levels. However, high-resolution erosion risk maps show its substantial spatial diversity. According to our study, average or higher actual erosion intensity levels occur for 10.6 % of agricultural land, i.e. 3.6 % of the entire voivodeship area. In 20 % of the municipalities there is a very urgent demand for erosion control. In the next 23 % an urgent erosion control is needed. Our study showed that even a slight improvement of P-factor estimation may have an influence on modeling results. In our case, despite a marginal change of erosion assessment figures on a regional scale, the influence on the final prioritization of areas (municipalities) according to erosion control needs is visible. The study shows that, high-resolution satellite imagery and OBIA may be efficiently used for P-factor mapping and thus contribute to a refined soil erosion risk assessment.

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.

Over the last years, many features describing satellite images have been proposed. In this paper we analyse and compare fractal and multifractal features in the context of the discrimination of the four most common land cover classes on very high resolution optical satellite images. In particular, we consider agricultural, forest, urban and water classes from WorldView-2 scenes and for their global description we propose to use the “degree of multifractality”. We focus on the verification if the image filtration prior to the calculation of fractal and multifractal parameters may improve their efficiency as image content descriptors. In order to evaluate the effectiveness of the coupling of fractal and multifractal features with the image filtration we analyse the class separability measures and the classification accuracy. Our study results indicate that the degree of multifractality improves the classification accuracy in comparison to the fractal dimension. German In den letzten Jahren wurden viele Merkmale zur Beschreibung von Satellitenbildern vorgeschlagen. In diesem Beitrag untersuchen wir fraktale und multifraktale Merkmale zur Unterscheidung der vier am meisten vorkommenden Landnutzungsklassen auf hochaufgelösten optischen Satellitenbildern. Konkret betrachten wir die Erkennbarkeit von Landwirtschaft, Wald, Stadt und Wasser in WorldView-2 Bildern und schlagen als Maß für ihre globale Beschreibung den ,,Grad der Multifraktalität“ vor. Einen Schwerpunkt bildet die Frage, welche Art der Bildfilterung vor der Bestimmung der fraktalen Parameter zu einer besseren Trennbarkeit der Klassen führt. Dieses quantifizieren wir mit verschiedenen Qualitätsmaßen. Außerdem wird gezeigt, dass ein höherer Grad der Multifraktionalität zu besseren Ergebnissen führt.

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.

The paper presents the results of the study of usefulness of selected textural features for describing the content of image chips (1024 by 1024 pixels) cut from WorldView-2 panchromatic images. Several texture analysis techniques were applied to calculate 290 different global image descriptors. These features together with 4 histogram-based characteristics formed the set of classification features. Decision trees were used to classify the images into broad land cover categories. Performance of each group of textural characteristics was assessed and compared to the results of classification based on all calculated characteristics.