Investigations toward S2 and S3 Time Series for Monitoring...
Transcript of Investigations toward S2 and S3 Time Series for Monitoring...
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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
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• 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 :
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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
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Mass development of submersed macrophytes
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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!
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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)
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RGB histogram day 6, noon, 0,5m distance to target l
Original, raw format adjusted image
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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??
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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
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Part 1: in situ measurements
What do the sensors measure?
atm
osph
ere
wat
er
surface
Ed
Macrophytes
Sensor
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Boat
Setup for macrophyte in situ measurements
Typical Bavarian Beerbench-construction for stable jetty measurements
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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
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0,60
100
height [m] density [%] biomass [g]
300
Methods of macrophyte in situ measurements
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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
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results
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Sediments
results results
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canopy cover density macrophyte type I
results
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canopy cover density macrophyte type II
results
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Structure
results
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Phenology
results
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Different lakes results
Starnberger See Tegernsee
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Different years
results
2010 2011
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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‘)
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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
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Results – Principal component analysis 1
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Results – Principal component analysis 2
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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+)
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Remote Sensing
600
km
1 km
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What do the sensors measure?
atm
osph
ere
wat
er
surface
Ed
Macrophytes
Sensor Part 2: from air / space
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Resolution = costs!
spectral
temporal radiometric
spatial
resolution
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hyperspectral vs. multispectral
Spectral resolution
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Spatial resolution Elodea nuttallii
Najas marina
Decreasing spatial resolution
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The water column
Reflexion = deep water reflexion + lake bottom reflexion
2 Meter depth subsurface
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Attenuation derived from in-situ
measurements
Bio-optical models
21
2
1
),(),(
ln
zzzEzE
d
d
dK −
=λλ
Attenuation-coefficient (Kd) after Maritorena, 1996
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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
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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
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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
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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
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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
]
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• 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!
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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
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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
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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)
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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 !!
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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
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Thanks for attention