Application of EO for Environmental Monitoring at the Finnish Environment Institute Data Processing (CalFin) and Examples of Products Markus Törmä Finnish Environment Institute SYKE markus.torma@ymparisto.fi
Earth observation services at SYKE www.syke.fi/earthobservation www.syke.fi/earthobservation 2
Water quality Chlorophyll-a WWW: years 2007-2016, March - October Daily composite images Represents the quantity of algae in water but not directly the amount of cyanobacterium Surface algal blooms WWW: years 2012-2016, late June - early September Daily composite images Generalized chlorophyll-a estimate is classified to 4 classes indicating probability for the surface algae blooms 3
Water quality Turbidity WWW: 2012-2016, March - October Daily composite images Sea Surface Temperature WWW: 2007-2016, April - October Night images 4
Future with Sentinels Sentinels will bring huge improvement for the water quality monitoring in comparison to the gap-filling years without optical ESA satellite instruments. S3 OLCI & SLSTR: Continuing operational production (MERIS, MODIS) for the Baltic Sea Starting operational production for large lakes Chl-a, algae blooms, turbidity, CDOM, transparency, SST (with SLSTR) S2 MSI: High resolution: smaller lakes, coastal areas Algae blooms, turbidity and transparency + Reed belts and other macrophytes? 5
S2 MSI 16.5.2016 Hanko harbour and beach areas Detection of algae blooms and coastal processes near popular beaches and visiting harbors
Snow monitoring Copernicus GlobalLand Pan-European Fractional Snow Cover Continuation of CryoLand (EU, 2011-2015, coordinated by ENVEO IT GmbH) Snow products in Pan-European and regional scales 0.005º grid size Data processing and data portal still maintained as Copernicus portal SCAmod-algorithm (Metsämäki et al. 2005; 2012) & MODIS reflectance data Sentinel-3 SLSTR used in future 7
25.11.2016 FSC time series Jan-Aug Melt-off day is defined as the first day of at least several days snow-free (FSC=0%) period, but detection may restart if enough new snow days appear Pixel is labelled non-classified if FSC-products do not identify a proper at least a few days continuous snow cover
Lake ice: Freeze-up Landsat-8 8.11.2016 Sääksjärvi, South-Western Finland SW-Finland Centre for Economic Development, Transport and Environment has been interested about lake ice information Freeze-up and melt dates Sentinel-1 8.11.2016 Sentinels have potential, we are looking project in order to make product Sentinel-2 12.11.2016 9
Lake ice: melting Lokka artificial lake, Northern Finland Sentinel-1 EW-mode 24.1.2016 IW-mode 20.3.2016 IW-mode 19.5.2016 EW-mode 23.5.2016 10
Corine Land Cover Finnish High Resolution Corine Land Cover 2012 Raster, 20 m pixel Pan-Europen land cover classification Next version CLC2018 Start early 2017, finished summer 2018 Previous versions 2000, 2006 and 2012 Combination of existing spatial data and image interpretation Sentinels: More images SAR: wetlands & sparsely vegetated mountain areas Pixel size of Finnish HR CLC 20 m 10 m? Automatic generalization European CLC2012 Vector, 25 ha MMU Download from http://www.syke.fi/en-us/open_information/spatial_datasets 11
Agricultural information for MAVI MAVI: Agency for rural affairs Control of EU agricultural subsidies Information needs Plant classification Ploughing SEN3APP (EU FP7) 12
Agricultural information for MAVI Plant classification Loimaa-Nakkila test area Sentinel-1 time series, 14 dates, 2015 6 plant groups Winter cereals Spring cereals Peas Potato Rapeseed Grasses, pasture, fallow Anomaly detection Search parcels with nontypical NDVI or backscatter June NDVI-mosaic Red: NDVI considerably smaller than plant class mean Yellow: NDVI slightly smaller Blue: NDVI slightly higher 13
Vegetation phenology - Harmonized time series of Fractional Snow Cover and vegetation indices from MODIS for the period 2001 to 2015 calculated for Finland and surrounding areas - Yearly maps of phenological events, e.g. the green-up of vegetation are provided for Finland - Continuation of MODIS time series with Sentinel-3 SLSTR is planned from 2017 onwards using the Calvalus processing system on CalFin Green-up (DoY) No data < 80 80-90 90-100 100-110 110-120 120-130 130-140 140-150 150-160 160-170 > 170 Böttcher et al. (2014). Remote Sensing of Environment, 140, 625-638 Böttcher et al. (2016). Remote Sensing, 8, 580.
Critical points In Finland, in order to get timely images due to weather, plenty of imaging capacity is needed Sentinel-1: 2 satellites plenty of images, processing capacity is bottleneck at the moment Sentinel-2: 1 satellite, -2B 2017 one satellite is not enough, extra capacity using Landsat-8 Radiometric correction as automated as possible Sentinel-1: process should be installed to Calvalus Sentinel-2: cloud masking is difficult SNAP, Idepix & Sen2Cor Envimon by VTT 15
Thank you! Examples from Vesa Keto, Hanna Alasalmi, Mikko Kervinen, Kari Kallio, Saku Anttila, Timo Pyhälahti, Sari Metsämäki, Kristin Böttcher, Pekka Härmä, Olli-Pekka Mattila, Eeva Bruun, Sofia Junttila (sorry to all I forgot to mention) Project partners include Finnish Meteorological Institute FMI VTT Technical Research Centre of Finland Ltd Natural Resources Institute Finalnd LUKE MAVI: Agency of Rural Affairs National Land Survey NLS Finnish Geospatial Research Institute