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Matej Mojses and František Petrovič

Abstract

The aim of this paper is to describe agricultural landscapes in the cadastral area of Hriňova and their development in the context of social and economic changes over the past 60 years. This area is characterized by the occurrence of historical structures of agricultural landscape (HSAL) which are important because they comprise various cultural, environmental and ecological aspects. The assessment of land use changes on the two scales of cadastral area and selected small localities highlights that the most important trend here is agricultural extensification. The results show that despite these changes in land use, the historical structures in the agricultural landscape represented by forms of anthropogenic relief remain a permanent part of this research area.

Open access

Andrej Halabuk, Katarina Gerhatova, Frantisek Kohut, Zuzana Ponecova and Matej Mojses

Abstract

Halabuk A., Gerhatova K., Kohut F., Ponecova Z., Mojses M.: Identification of season-dependent relationships between spectral vegetation indices and aboveground phytomass in alpine grassland by using field spectroscopy. Ekologia (Bratislava), Vol. 32, No. 2, p. 186-196, 2013.

Spectral characteristics of alpine grasslands across the vegetation season (from May to September) are presented. The results are based on three year field spectroscopy monitoring of acid, nutrient poor grasslands at Kraľova hoľa research site, Low Tatras, Slovakia. Relationships between commonly used spectral vegetation indices (VIs) and field-based estimation of aboveground green phytomass (AG B) were analysed. Finally, season-dependent regression models were created in order to allow spatially extensive non-destructive monitoring of AG B. Spatial-temporal dynamics of background and standing litter markedly affect seasonal variations of relationships between VIs and AG B and predictability of the regression models. Because of a high proportion of litter during the whole season, this was a plant water-sensitive normalized difference water index (NDWI), which dominates as the predictive variable in the regression models across the whole season; except June, where chlorophyll absorption sensitive in normalized difference vegetation index (NDVI) performed the best (R2 = 0.57; rel. RMSE = 34%). However, the accuracy of the models was quite low (May: R2 = 0.45; rel. RMSE = 49%; July: R2 = 0.47; rel. RMSE = 26%; August: R2 = 0.13; rel. RMSE = 31%; September: R2 = 0.53; rel. RMSE = 40%).