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Soil erosion by water is a main form of land degradation worldwide. The problem has been addressed, among others, in the United Nations Sustainability Goals. However, for mitigation of erosion consequences and adequate management of affected areas, reliable information on the magnitude and spatial patterns of erosion is needed. Although such need is often addressed by erosion modelling, precise erosion monitoring is necessary for the calibration and validation of erosion models and to study erosion patterns in landscapes. Conventional methods for quantification of rill erosion are based on labour-intensive field measurements. In contrast, remote sensing techniques promise fast, non-invasive, systematic and larger-scale surveying. Thus, the main objective of this study was to develop and evaluate automated and transferable methodologies for mapping the spatial extent of erosion rills from a single acquisition of remote sensing data. Data collected by an uncrewed aerial vehicle was used to deliver a highly detailed digital elevation model (DEM) of the analysed area. Rills were classified by two methods with different settings. One approach was based on a series of decision rules applied on DEM-derived geomorphological terrain attributes. The second approach utilized the random forest machine learning algorithm. The methods were tested on three agricultural fields representing different erosion patterns and vegetation covers. Our study showed that the proposed methods can ensure recognition of rills with accuracies between 80 and 90% depending on rill characteristics. In some cases, however, the methods were sensitive to very small rill incisions and to similar geometry of rills to other features. Additionally, their performance was influenced by the vegetation structure and cover. Besides these challenges, the introduced approach was capable of mapping rills fully automatically at the field scale and can, therefore, support a fast and flexible assessment of erosion magnitudes.

Local-scale flooding (LSF) is usually characterized by much less severe damage compared to extreme flood events; however, it does have marked local environmental influence, especially when it is characterized by regular and frequent occurrence and long duration. Knowledge about the spatial extent of flood-prone areas is essential for flood risk and land management purposes, spatial planning, or emergency response. Flood mapping procedures have been supported by remote sensing for several decades, and progress in remote sensing technology and image processing over the last two decades has made flood extent analysis possible at an unprecedented level of detail. Here we provide an overview of applications of remote sensing technologies for analyzing the extent of flood events and discuss their applicability for LSF. We report on applications of data from the optical visible and reflective infrared spectrum, active microwave spectrum, and airborne laser scanning technology. Additionally, applications of elevation data supporting flood extent mapping are reviewed. The review reveals that in general remote sensing techniques and data types are likely to have similar capabilities and limitations for analyzing LSF as they have for extreme floods. However, data from many current remote sensing sensors are inadequate for LSF analysis, since very high spatial resolution data are required for mapping localized flooding. Finally, airborne laser scanning is found to be an emerging and promising technology in flood-related water surface analysis.

The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.