Permanent grassland is one of the monitored categories of land use, land use change, and forestry (LULUCF) within the climate concept and greenhouse gas reduction policy (Regulation (EU) 2018/841). Mapping the conditions and changes of permanent grasslands is thus very important. The area of permanent grassland is strongly influenced by agricultural subsidy policies. Over the course of history, it is possible to trace different shares of permanent grassland within agricultural land and areas with significant changes from grassland to arable land. The need for monitoring permanent grassland and arable land has been growing in recent years. New subsidy policies determining farm management are beginning to affect land use, especially in countries that have joined the EU in recent waves. The large amount of freely available satellite data enables this monitoring to take place, mainly owing to data products of the Copernicus program. There are a large number of parameters (predictors) that can be calculated from satellite data, but finding the right combination is very difficult. This study presents a methodical, systematic procedure using the random forest classifier and its internal metric of mean decrease accuracy (MDA) to select the most suitable predictors to detect changes from permanent grassland to arable land. The relevance of suitable predictors takes into account the date of the satellite image, the overall accuracy of change detection, and the time required for calculations. Biological predictors, such as leaf area index (LAI), fraction absorbed photosynthetically active radiation (FAPAR), normalized difference vegetation index (NDVI), etc. were tested in the form of a time series from the Sentinel-2 satellite, and the most suitable ones were selected. FAPAR, canopy water content (CWC), and LAI seemed to be the most suitable. The proposed change detection procedure achieved a very high accuracy of more than 95% within the study site with an area of 8766 km2.

Change detection workflow for mapping changes from arable lands to permanent grasslands with advanced boosting methods. Geodetski vestnik, 63 (3), 379-394.

This article focuses on utilization of vegetation indices for vegetation phenology analysis based on multitemporal MERIS data. The model data set contained imagery acquired during the vegetation season of the year 2009 and it covered most of the area of the Czech Republic. Databases LPIS and GlobCover were used for spatial delimitation of the observed vegetation types. Firstly, a methodology of processing multitemporal MERIS data for atmospheric and geometric corrections is presented. The main part deals with the evaluation of spectral characteristic of the forest species and agricultural crops by means of vegetation indices. Results showed that the MTCI index is well related to the changes of chlorophyll concentration and it is a suitable measure for chlorophyll estimation from MERIS data. Indices fCover and LAI are very sensitive to the quantity of vegetation cover (biomass). Perspectives of the research regarding the planned missions of the satellites Sentinel 2 and Sentinel 3 are given in conclusion.