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Grassland use intensity is a topic of growing interest worldwide, as grasslands are integral in supporting biodiversity, food production, and regulating of the global carbon cycle. Data available for characterizing grasslands management are largely descriptive and collected from laborious field campaigns or questionnaires. The recent launch of the Sentinel-2 earth monitoring constellation provides new possibilities for high temporal and spatial resolution remote sensing data covering large areas. This study aims to evaluate the potential of a time series of Sentinel-2 data for mapping of mowing frequency in the region of Canton Aargau, Switzerland. We tested two cloud masking processes and three spatial mapping units (pixels, parcel polygons and shrunken parcel polygons), and investigated how missing data influence the ability to accurately detect and map grassland management activity. We found that more than 40% of the study area was mown before 15 June, while the remaining part was either mown later, or was not mown at all. The highest accuracy for detection of mowing events was achieved using additional clouds masking and size reduction of parcels, which allowed correct detection of 77% of mowing events. Additionally, we found that using only standard cloud masking leads to significant overestimation of mowing events, and that the detection based on sparse time series does not fully correspond to key events in the grass growth season.

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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.

Central and Eastern Europe has experienced fundamental land use changes since the collapse of socialism around 1990. We analyzeanalyzed the patterns and determinants of agricultural land abandonment and recultivation in Slovakia during the transition from a state-controlled economy to an open-market economy (1986 to 2000) and the subsequent accession to the European Union (2000 to 2010). We quantified agricultural land-use change based on available maps derived from 30-m multi-seasonal Landsat imagery and analyzeanalyzed the socioeconomic and biophysical determinants of the observed agricultural land-use changes using boosted regression trees. We used a scenario-based approach to assess future agricultural land abandonment and recultivation until 2060. The maps of agricultural land use analysis reveal that cropland abandonment was the dominant land use process on 11% of agricultural land from 1986 to 2000, and on 6% of the agricultural land from 2000 to 2010. Recultivation occurred on approximately 2% of agricultural land in both periods. Although most abandoned land was located in the plains, the rate of abandonment was twice as high in the mountainous landscapes. The likelihood of abandonment increased with increased distance from the national capital (Bratislava), decreased with an increase of annual mean temperatures and was higher in proximity to forest edges and on steeper slopes. Recultivation was largely determined by the opposite effects. The scenario for 2060 suggests that future agricultural land abandonment and recultivation may largely be determined by climate and terrain conditions and, to a lesser extent, by proximity to economic centers. Our study underscores the value of synergetic use of satellite data and land-use modeling to provide the input for land planning, and to anticipate the potential effects of changing environmental and policy conditions.

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a b s t r a c tThe aquatic vegetation ofČíčov Lake in the Danube floodplain, which is listed in the Ramsar Convention, was investigated to address three main questions: (1) how have landscape composition and the structures of the lake and its buffer zone changed from the mid-20th century;(2) how have species richness and the abundance of the aquatic macrophyte assemblage in this lake ecosystem changed over the last 34 years; and (3) which landscape metrics can best explain these temporal changes for floating-leaved macrophytes? Two methodological approaches, remote sensing and botanical field surveys, were applied. Historical (1949, 1970, 1990) and contemporary (2006) aerial photographs were analysed to determine land cover. Landscape configuration and structure were analysed using eight landscape metrics selected in advance to measure spatio-temporal changes and the fragmentation of the lake ecosystem and its corresponding buffer zone. The species diversity, abundance and distribution of true aquatic macrophytes were surveyed eleven times in five survey stretches between 1973 and 2007.At the landscape level, a decrease in the area covered by floating-leaved macrophytes, as well as an increase in open water surface and fragmentation of the land cover classes in the lake ecosystem, were recorded from 1949 to 2006. Overall, 30 true aquatic macrophytes were found from 1973 to 2007. Species richness did not change considerably, but the abundance of aquatic species fluctuated over the years. Three groups of true aquatic vegetation, based on common structural characteristics, were found in 1973-1983, 1989-2002, and 2004-2007 over the last 34 years. The landscape metrics NP, PD, LPI, and SHDI, which all express patterns of landscape fragmentation mostly indicate temporal changes in floatingleaved macrophytes.