Land cover research, applications and development needs in Slovakia

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1 Land cover research, applications and development needs in Slovakia Andrej Halabuk Institute of Landscape Ecology Slovak Academy of Sciences (ILE SAS) Štefánikova 3, Bratislava, Slovakia

2 Institute of Landscape Ecology Slovak Academy of Sciences Interdisciplinary scientific institution for basic and applied research in landscape ecology (established in 1965) Total staff: 59 (scientific staff: 39; PhD students: 7) ILE SAS covers fields of abiotic, biotic and socio-economic sciences

3 Research related to Remote Sensing Landscape ecology Land cover mapping and classification Habitat suitability modelling and predictive mapping Landscape structure analysis Land use and land cover change (detection and modelling) Biodiversity and ecosystem research Long-term ecological research (time series analysis of NDVI for LSP and stress detection) Biodiversity assessment at landscape scale Impact of climate variability on ecosystems

4 Linking Pan-European Land cover Change to Pressures on Biodiversity

5 DPSIR concept Land cover transitions during Main changes from extensive grassland to forest from mosaics to intensive grassland from mosaics to extensive grassland from mosaics to forest from shrub to forest Main changes Study area boundary Not changed Change Change Changes in both periods from ext. grassland to grassland with trees from extensive grassland to forest from grassland with trees to forest from shrub to forest

6 Predictive land cover change modelling 2003 Liberalization 2030 BAU 2030 Biodiversity 2030

7 Future development: From land cover change to land cover dynamics Land cover dynamics characterized by seasonal and inter-seasonal variability of vegetation greenness reflected in dynamic change of SI signal Annual mean of MAX NDVI from Response to increasing availability of SI Increasing temporal resolution (LDCM, Sentinel2,...) Result: multitemporal and time series based land cover classification and land cover dynamics analysis time series analysis of vegetation greenness (NDVI) Inter-seasonal variability (CV of annual MAX NDVI from ) Mean seasonality within season SD of MAX NDVI

8 Grassland focus Using of NDVI time series for grassland monitoring Grasslands important component of landscape and its functioning (production, biodiversity, water retention) Lack of spatial statistics, difficult detection by RS Detection of management practice in grasslands (cutting, grazing, overgrowing, drying, flooding, burning)

9 Grasslands: 94 % accuracy Grasslands: 92 % accuracy Errors decreased dramatically after the crop harvesting in late June Accuracy decreased to 85% and 78% when using only 2 resp. 1 time period for the analysis

10 Grassland mapping in Slovakia Validat. set CLC 8d NDVI 16d NDVI Heterogeneous Prod. accuracy 38% 30% 30% User's accuracy 58% 58% 51% Homogenous Prod. accuracy 39% 68% 53% Intensive grasslands unmanaged grasslands User's accuracy 77,5% 61% 60% - Broader scale higher variability worse results - More effort needs for training - Phenology based classes needs to be defined - Still promising results compare to CLC flooded grasslands

11 Classification of grasslands based on annual profile of NDVI 1. Slovak grassland sites (pure 3758 pixels) based on GE inspection and LPIS 2. Hungarian lowlands (N2000 grasslands - approx pure pixels GE inspection) 3. Slovak grasslands approx heterog. pixels without no check

12 Land cover dynamics interaction with climate variability 2003 Median

13

14 Land cover dynamics detection - Mainly remote and mountainous areas - Further analysis needed including socioeconomic data - Possible consequences predictive modelling Number of years managed from

15 Land cover dynamics detection - Mainly remote and mountainous areas - Further analysis including socioeconomic data is needed - Possible consequences predictive modelling Number of years unmanaged from

16 unmanaged managed 1 Further analysis including socioeconomic data is needed

17 Future needs Network of field validation sites (including land management data) at regional scale Developing techniques for up-scaling between sites, networks of sites for detecting and interpreting key indicators of land use and land cover change Designing a system for a cost effective monitoring that enable frequent, repeated, regionally coordinated assessment of distribution, status and trends of landscape and ecosystems

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