Application of EO for Environmental Monitoring at the...

Application of EO for Environmental

Monitoring at the Finnish Environment

InstituteData Processing (CalFin) and Examples of Products

Markus Törmä

Finnish Environment Institute SYKE

markus.torma@ymparisto.fi

2www.syke.fi/earthobservation

Earth observation services at SYKE

www.syke.fi/earthobservation

Water quality

3

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

4

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?

5

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

16

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