application of eo for environmental monitoring at the...
TRANSCRIPT
Application of EO for Environmental
Monitoring at the Finnish Environment
InstituteData Processing (CalFin) and Examples of Products
Markus Törmä
Finnish Environment Institute SYKE
2www.syke.fi/earthobservation
Earth observation services at SYKE
www.syke.fi/earthobservation
Water quality
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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
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
Water quality
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Sea Surface Temperature
● WWW: 2007 - 2016, April -
October
● Night images
Turbidity
● WWW: 2012 - 2016, March -
October
● Daily composite images
● 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?
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Future with Sentinels
S2 MSI 16.5.2016 Hanko harbour and
beach areas
Detection of algae blooms and coastal processes near
popular beaches and visiting harbors
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
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Snow monitoring
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
25
.11
.20
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Pixel is labelled ‘non-classified’ if FSC-products do not identify
a proper at least a few days’ continuous snow cover
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
● 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
● Sentinels have potential, we
are looking project in order to
make product
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Lake ice: Freeze-upLandsat-8 8.11.2016
Sentinel-1 8.11.2016
Sentinel-2 12.11.2016
● Lokka artificial lake, Northern Finland
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Lake ice: melting
Sentinel-1 EW-mode 24.1.2016 IW-mode 20.3.2016
IW-mode 19.5.2016 EW-mode 23.5.2016
● 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?
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Corine Land Cover
Finnish High Resolution Corine
Land Cover 2012
Raster, 20 m pixel
European CLC2012
Vector, 25 ha MMU
Automatic generalization
Download from http://www.syke.fi/en-US/Open_information/Spatial_datasets
● MAVI: Agency for rural affairs
○ Control of EU agricultural subsidies
○ Information needs
• Plant classification
• Ploughing
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Agricultural information for MAVI
SEN3APP
(EU FP7)
Agricultural information for MAVI
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Anomaly detection
● Search parcels with non-
typical NDVI or backscatter
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
June NDVI-mosaic
Red: NDVI considerably
smaller than plant class mean
Yellow: NDVI slightly smaller
Blue: NDVI slightly higher
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
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
Böttcher et al. (2014). Remote Sensing of Environment, 140, 625-638
Böttcher et al. (2016). Remote Sensing, 8, 580.
● 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
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Critical points
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