Schneider Thomas1, Patrick Wolf1, Natascha Oppelt2, Katja Dörnhöfer2,

Peter Gege3

Investigations toward S2 and S3 Time Series for Monitoring Freshwater

Lake Macrophytes

1 Technische Universität München, Chair for Aquatic System Biology, Limnological Station 2 Christian Albert University Kiel, Chair for Remote Sensing and Environmental Modelling

3 German Aerospace Centre, Institute for Remote Sensing Methods

• increase in mean water temperature

• shift in vegetation period

• expansion of the vegetation period

• later/earlier begin in spring

• heavy rain events more frequent

• drought periods more frequent

• changes in landuse on catchment level (e.g. “green energy plants)

• anorganic material intake by increased erosion (maize-effect)

• fertilizer and pesticide intake

:

Ecologic and economic effects like “invasive” species, Cyanobacteria blooms, etc.

Observations on freshwater lake status in 2013 :

Vegetation period: later/earlier begin in spring

2010 2011

19.05.

20.06.

Temperature across the 2010 and 2011 vegetation period

Day of the year

Mass development of submersed macrophytes

Blue algae (cyanobactera) are all over. More than 100 species in Bavaria, some of them with „toxic“ potential

Cyanobacteria blooms

Blue algae bloom in lake Grambker, Northern Germany.

Cyanobacteria in lakes of the federal state Bremen

Tables warning from bathing in situations with algal blooms

http://www.umwelt.bremen.de/sixcms/media.php/13/Blaualgen_Flyer_%DCberarbeitung_08_06_05.pdf

For an effective monitoring of algal blooms the assessment in the Pre-blooming stage is essential!

Mid July until Mid of October 2013, 4 m depth, 1m distance, fixed camera settings, close to noon data take

Water transmissivity changes over the vegetation period

(F. Meyer, 2014)

RGB histogram day 6, noon, 0,5m distance to target l

Original, raw format adjusted image

EU-WFD standard monitoring every third year Sufficient for inventory of species

Not sufficient for invasive macrophyte monitoring!! Not sufficient for an algal bloom warning system!! Not sufficient for water content monitoring!!

Monitoring seems a necessity!

Does a RS based inventory and monitoring system is appropriated for such a task??

Basic concept

Probenahmerahmen

R L

Reflexionsfaktor = Reflexion/Einstrahlung = R/E

E

measurement, sampling sorting, enhancing

T1

Tn

Additional data:

Bathymetriy, location,

water contents, sediment, seasonal wethering, etc.

Lab: biometry, pigments, photogrammetrie, etc.

model development

Reflection-model Integration Growth-model

prognosis

external source: Phenology model output: • biomass • pigments • coverage etc. proposed : • species, • abundances • etc.

time Tx, location y :

Data take (time Tx, location y) RS-data processing

Integration of external data

preprocessing: atmosphere, water-column, -surface, etc.

Prozessierung: Klassifikation processing: classification

model-inversion

Bio-optical model: Prognosis-check Parameterextraction: • Biomass • Pigments • coverage, etc. •Identifikation: • species, abundances

Diagnosis need for action? yes/no!

distribution maps

Chara contaria Chara aspera P.pectinatus fouled

P.perfoliatus P.pectinatus

Part 1: in situ measurements

What do the sensors measure?

atm

osph

ere

wat

er

surface

Ed

Macrophytes

Sensor

Boat

Setup for macrophyte in situ measurements

Typical Bavarian Beerbench-construction for stable jetty measurements

Elodea nuttallii Western waterweed neophyte Najas marina Spiny najad indigenous

Chara aspera Rough chara indigenous Potamogeton perfoliatus Perfoliate pondweed indigenous

• pure stands • defined spots • phenology / biometry • spectral measurements

Methods – study area

0,60

100

height [m] density [%] biomass [g]

300

Methods of macrophyte in situ measurements

results

height [m] density [%] biomass [g]

300 100 0,60

800 100 1,20

120 100 0,20

500 100 1,60

000 000 0,00

results

Sediments

results results

canopy cover density macrophyte type I

results

canopy cover density macrophyte type II

results

Structure

results

Phenology

results

Different lakes results

Starnberger See Tegernsee

Different years

results

2010 2011

Combined growth-/reflecion model (R, Version 2.15.3) Lake name abbr. STA (Lake Starnberger ), TEG (Lake Tegernsee) location jetty1, jetty2, jetty3, Bernried, Ringsee-bay date JJJJ_MM_TT time hh-mm-ss See bottom type Sediment, Chara_spp., Potamogeton_perfoliatus, Elodea_nuttallii, Najas_marina See bottom abbreviation P (plant), S (sediment), PS (plant-sediment-mix), W (Wasser), PW (plant-water mix) Sediment cover 0, 25,50,75,100 (in %-plant share) Depth of measurement X,xx (in meter) Canopy height X,xx (in meter) Growth depth X,xx (in meter) Wet-biomass X,xxx (in kilogram/0,25m²) Dry -biomass X,xxx (in kilogram/0,25m²) Phänologic Phase Phase_YY_X.X (with ‚YY‘ for the species ‚X.X‘ number) Nutrion availability sediment Tendency: Ptotal>= 0,05 or Tendency: Ptotal<= 0,05 (in %) Water temp (filter for Najas) growth above 15°C, seed development above 20°C comments Text, if adequate 400nm x,xxxxxxx (Reflexionsintensität bei 400nm, Einheit ‚1/Sr‘) … x,xxxxxxx (Reflexionsintensität bei … nm, Einheit ‚1/Sr‘) 700nm x,xxxxxxx (Reflexionsintensität bei 700nm, Einheit ‚1/Sr‘)

Test-runs with 1000 repetitions gained the following results: (P (plant), S (sediment), PS (plant-sediment-mix), W (Wasser), PW (plant-water mix)) Classification of S, P, PS, PW oder W (level a): 70% correct Classification of species type (level b): 82% correct Classification to a phenologic phase (level c): Chara spp.: 91% correct Potamogeton perfoliatus: 89%correct Elodea nuttallii: 79% correct Najas marina: 76% correct

Model Inversion results, combined growth-/reflecion model

results

Results – Principal component analysis 1

Results – Principal component analysis 2

Results – growing season 2011, Lake Starnberg

Lake and species specific spectral libraries across the day across the vegetation period

Phenologic change “model” for Najas marina, 2011

deep water reflectance (0+)

Remote Sensing

600

km

1 km

What do the sensors measure?

atm

osph

ere

wat

er

surface

Ed

Macrophytes

Sensor Part 2: from air / space

Resolution = costs!

spectral

temporal radiometric

spatial

resolution

hyperspectral vs. multispectral

Spectral resolution

Spatial resolution Elodea nuttallii

Najas marina

Decreasing spatial resolution

The water column

Reflexion = deep water reflexion + lake bottom reflexion

2 Meter depth subsurface

Attenuation derived from in-situ

measurements

Bio-optical models

21

2

1

),(),(

ln

zzzEzE

d

d

dK −

=λλ

Attenuation-coefficient (Kd) after Maritorena, 1996

Water content experiments for APEX data analysis with white and black plastic foils: • on land (16 * 25 m) • in water (8 * 50 m)

Simulation results using Bomber (Giardino et al., 2012) inversion

RAMSES in situ measurements derived Ed (A), Lu (B) and Rrs (C) over optical deep water and statistics of atmospherically corrected RapidEye data (D) from 03/09/11 of 1000 Pixels over deep water (grey area represents standard deviation) (WASI)

Data base derived info transferred to RapidEye data

seasonal development of IOPs according to Ed-inversion results of RAMSES in-situ measurements with WASI (grey area shows standard deviation)

Water content

growing season 2011, Lake Starnberg

seasonal variability of the inherent optical properties for phytoplankton (CHL), suspended particulate matter (SPM), and coloured dissolved organic matter (cDOM)

BOMBER derived IOPs from RapidEye data for the test site Bernried

growing season 2011, Lake Starnberg

Water content

Unmixing results for RapidEye data using the bio-optical model BOMBER (Giardino et al., 2012):

derived “Secchi depth” equivalent of the water body for three “doy”

Distance to shore line [m]

Dep

th [m

]

• Macrophyte, sediment, epiphyte spectral libraries for lakes

• Reflection-/growth-model improvement • Attenuation coefficient time series by S-3 • Local lake bottom characterization by S-2

Outlook: Sentinel 2 + 3 concept:

Monitoring systems for freshwater lake water quality!

Monitoring concept with operational RS systems, Germany

N-S transect options

Expandable to a N-S /E-W “cross” (covering e.g. “thematic” lakes of the lignite mining areas)

Challenge by: • differing inclinations • differing swath width • differing resolutions

• spatial • spectral • temporal • radiometric

• etc.

need for standardised methods!

S3

Monitoring concept – “The European perspective”with operational RS systems Europe

The Sentinels and supporting systems seems appropriated for such a monitoring systems for the European freshwater lakes!

S3

subsurface remote sensing reflectance (rrs) implemented in the shallow water model of BOMBER (Giardino et al., 2012):

= deep water contribution (Lee et al., 1999) A0 and A1 = weighting factors for deep water and bottom contribution (Albert&Mobley, 2003) Kd, = parameterization of down welling irradiance Ku

C and KuB = parameterization of upwelling radiance from water column and bottom

H = water depth

Bio-optical models

Rrs and rrs transformation (Lee et al., 1998)

Basic concept

Probenahmerahmen

R L

Reflexionsfaktor = Reflexion/Einstrahlung = R/E

E

measurement, sampling sorting, enhancing

T1

Tn

Additional data:

Bathymetriy, location,

water contents, sediment, seasonal wethering, etc.

Lab: biometry, pigments, photogrammetrie, etc.

model development

Reflection-model Integration Growth-model

prognosis

external source: Phenology model output: • biomass • pigments • coverage etc. proposed : • species, • abundances • etc.

time Tx, location y :

Data take (time Tx, location y) RS-data processing

Integration of external data

preprocessing: atmosphere, water-column, -surface, etc.

Prozessierung: Klassifikation processing: classification

model-inversion

Bio-optical model: Prognosis-check Parameterextraction: • Biomass • Pigments • coverage, etc. •Identifikation: • species, abundances

Diagnosis need for action? yes/no!

distribution maps

Chara contaria Chara aspera P.pectinatus fouled

P.perfoliatus P.pectinatus

Improving growth controlling light conditions

Diagnosis need for action? Yes / no !!

23/05/2014 49

acknowledgements

We wish to thanks the Bavarian State Ministry of Health and Environment for funding this research in the frame of the

climate change mitigation program !

The German Space Directorate for future fundings to expand that research to a trans-Germany transect

RESA for RapidEye and ESA for APEX data delivery

Thanks for attention