Riparian zones are dynamic ecosystems that form at the interface between the aquatic and terrestrial components of a landscape. They are shaped by complex interactions between the biophysical components of river systems, including hydrology, geomorphology, and vegetation. Remote sensing technology is a powerful tool useful for understanding riparian form, function, and change over time, as it allows for the continuous collection of geospatial data over large areas. This paper provides an overview of studies published from 1991 to 2021 that have used remote sensing techniques to map and understand the processes that shape riparian habitats and their ecological functions. In total, 257 articles were reviewed and organised into six main categories (physical channel properties; morphology and vegetation or field survey; canopy detection; application of vegetation and water indices; riparian vegetation; and fauna habitat assessment). The majority of studies used aerial RGB imagery for river reaches up to 100 km in length and Landsat satellite imagery for river reaches from 100 to 1000 km in length. During the recent decade, UAVs (unmanned aerial vehicles) have been widely used for low-cost monitoring and mapping of riverine and riparian environments. However, the transfer of RS data to managers and stakeholders for systematic monitoring as a source of decision making for and successful management of riparian zones remains one of the main challenges.

This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL.

Agricultural land abandonment is a dynamic process characterized by both significant spectral variability and spectral similarity to areas of agricultural land. The identification of abandoned agricultural land (AAL) based on remote sensing data must be preceded by field surveys that are focused on the acquisition of the physiognomic characteristics (the composition of species, height of vegetation, textures, and clustering into patterns) of these areas. This study aims to document the physiognomic and spectral differences between AAL and other land cover/land use classes in Slovakia. The Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2 time series for vegetation from April to September 2018, was applied. NDVI values were calculated for each Sentinel-2 scene, and NDVI profiles for selected samples were used to create phenological profiles for each AAL and land cover/land use class. The dispersion of the NDVI values for these classes, their median, and the root mean square error between NDVI data show that overgrowth by herbaceous plants is characterized by more significant dynamics (0.40–0.75), resulting in better spectral discriminability than classes overgrown by shrubs and trees (0.70–0.80). Field survey data are a fundamental prerequisite for the correct explanation of the discriminability of these AAL classes.

Slovenská akadémia vied vykonáva špičkový základný výskum, dlhodobý strategický a aplikovaný výskum a vývoj a poskytuje prístup k vedeckej infraštruktúre.