Abstract
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.
Highlights
The area and structure of agricultural land is significantly influenced by the development of methods and technologies of agricultural management
The method we have developed based on the random forest classifier and its ability to predict probabilities of potential changes (Figure 5) can serve as a source for targeted in situ inspections during the vegetation season
It was demonstrated that the random forest classifier and its internal mean decrease accuracy (MDA) metric are able to successfully detect relevant predictors for changes from permanent grassland to arable land
Summary
The area and structure of agricultural land is significantly influenced by the development of methods and technologies of agricultural management. In the context of agricultural management and the associated development of land use, it has influenced the effects of the global market and subsidy policies, especially agricultural, energy, and landscaping policies. These are mainly specific subsidy programs within the European Union, where subsidies are provided for agriculturally oriented lands for specific use of the given land unit [1,2,3,4]. Land 2020, 9, 420 especially the structure of cultivated crops, changes in the share of individual agricultural areas, and the emphasis on changes between permanent grassland and arable land [5,6,7]. Constant reduction in the area of these biomes has a very negative effect on the landscape as a whole in Europe [10] and on the absorption of greenhouse gases due to the use of land for agricultural purposes [11]
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