LabISA - Instrumentation Laboratory for Aquatic Systems ...
Transcript of LabISA - Instrumentation Laboratory for Aquatic Systems ...
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Brazilian inland water-UMASS- September 2015
Cláudio [email protected]
LabISA - Instrumentation
Laboratory for Aquatic
Systems and Brazilian inland
water bio-optical dataset
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2Brazilian Inland WaterCláudio Barbosa
Remote Sensing of inland waters research group
The history timeline activities (two periods)
Above water measurements & empirical algorithms (2001-2010)
Focused in the seasonal dynamics of volume and water composition
in the Amazon floodplain lakes.
Water column profiles measurements & semi analytical algorithms
Focused in bio-optics characterization and the seasonal dynamics
of bio-optics properties
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3Brazilian Inland WaterCláudio Barbosa
First period: Above water measurements (2001-2010)
In 2001, we began studies focused on the development of methodologies
integrating remote sensing data, spectroradiometric data and empirical
algorithms to understand the dynamics of water circulation in the Amazon
floodplain lakes.
Up to 2007 nearly 10 field campaigns measuring above water reflectance,
limnological variables as well as bathymetry of some lakes
After 2007 we also started to make measurements in hydroelectric reservoirs.
Built a database integrating all available radiometric and limnological data.
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4Brazilian Inland WaterCláudio Barbosa
Above water measurements in the amazon
floodplain Lakes
2001 to 2010
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5Brazilian Inland WaterCláudio Barbosa
Tefé
ManausSantarém
Belém12
3
Study sites in the Amazon floodplain (three sites)
Site 1 900 km
upstream from
the Amazon River
mouth.
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6Brazilian Inland WaterCláudio Barbosa
Diversity of Amazonian water types
Black water(Negro River)
White water (Amazon River)Manaus
Santarém
Clear Water (Tapajós River)
Organic dissolved matter
High inorganic matter
Chlorophyll concentration500-800g/liter
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7Meeting Tim Malthus
high water stage Image
Annual flooding amplitude : 7 meters
Low water stage Image
Inter-annual fluctuations : 2 meters
Dynamics of flooding in terms of water level fluctuationDaily water stage records
100 Km
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8Brazilian Inland WaterCláudio Barbosa
The flood pulse leads to a complex mixture of water
composition seasonally
19992002
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9Brazilian Inland WaterCláudio Barbosa
low
decline
rising
high
low
decline
rising
high
low
decline
rising
high
risinglow high decline
Gaussiano
risinglow high decline
Mean concentration values
Effect of food pulse on the water composition
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10Meeting Tim Malthus
Effect of food pulse on the water spectral response
Low water
•Estado 1
•Estado 4•Estado 3
•Estado 2
Receding water level
Rising water level
Highwater level
How changes on
water composition
affect both:
Amplitude and
Shape of the
spectra
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11Meeting Tim Malthus
Spectra shaped by water composition
High inorganic suspended solid concentrationHigh chlorophyll
concentration
Dissolved organic matter
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12Blue Planet Symposium:
Advances of inland water Remote sensing in BrazilCláudio Barbosa
Intensive sampling in different water level stage (2003-2010)
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13Brazilian Inland WaterCláudio Barbosa
Field infrastructure
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14Brazilian Inland WaterCláudio Barbosa
Field infrastructure
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15Meeting Tim Malthus
MERIS Image
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16Brazilian Inland WaterCláudio Barbosa
Visual intercomparison of spectral shape
Chlorophyll = 61 ug/lChlorophyll = 26 ug/l
Reflectance at each in situ sample station
was extracted from MERIS image for
all MERIS spectral bands
surface reflectance image
In situ spectra were resampled
to MERIS spectral resolution bands
(SimMERIS)
Spectron SE590 & Hand Held ASD
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17Brazilian Inland WaterCláudio Barbosa
Visual intercomparison of spectral shape
satellite overpass
one day time delay
six day time delay.
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18Meeting Tim Malthus
Water masses characterization (decline water)Clustering based on (SAM)
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19Meeting Tim Malthus
Chlorophyll concentration (empirical models)
•(D)
•(A) •(B)
•(C)
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20Brazilian Inland WaterCláudio Barbosa
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 4MCC
ACC
ACPI
MCPI
ACMO
MCMO
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 4
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 2 MCC
ACC
ACPI
MCPI
ACMO
MCMO
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 2
MACPI
MCC
ACPI
MCPI
ACMO
MCMO
Estado 3MACPI
MCC
ACPI
MCPI
ACMO
MCMO
MACPI
MCC
ACPI
MCPI
ACMO
MCMO
Estado 3
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 1MCC
ACC
ACPI
MCPI
ACMO
MCMO
MCC
ACC
ACPI
MCPI
ACMO
MCMO
MCC
ACC
ACPI
MCPI
ACMO
MCMO
Estado 1
mapping water masses
State 2 = low state 3 rising
State 1 = high state 4 decline
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21Brazilian Inland WaterCláudio Barbosa
Tefé
ManausSantarém
Belém12
3
Site 2 An extension of site 1 - nearly 1000 km
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22Brazilian Inland WaterCláudio Barbosa
Seasonal changes in chlorophyll distributions in Amazon floodplain lakes
derived from MODIS images (Novo et all, 2006 Limnology (2006) 7:153–161)
Site 2 An extension of site 1 - nearly 1000 km
TabatingaTefé
Manaus
Santarém
Belém2
3 1
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JUN/02
31%
41%
6%
2%
6%
14%Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
JUL/02
17%
14%
3%1%
5%
60%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
AGO/02
30%
29%5%
2%
7%
27%Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
SET/02
25%
33%4%1%3%
34%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
OUT0/2
19%
16%
3%1%6%
55%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
NOV/02
11%
14%
4%
1%
5%
65%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
DEZ/02
9%
22%
9%
2%5%
53%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
JAN/03
25%
19%
7%2%
8%
39%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
FEB/03
NO DATA
CLOUD COVER
MAR/03
16%
15%
6%2%
4%
57%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
APR/03
28%
33%
11%
3%
8%
17% Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
MAI/03
21%
23%
8%2%
7%
39%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
JUN/03
25%
40%7%
2%
7%
19% Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
JUL/03
24%
35%
6%
2%
6%
27%Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
AGO/03
27%
30%
7%
2%
6%
28%Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
SET/03
16%
34%
7%1%4%
38%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
OUT/03
23%
13%
2%1%4%
57%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
NOV/03
16%
28%
6%
2%
6%
42%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
DEZ/03
14%
20%
5%1%5%
55%
Chl < 20
20<Chl<56
56<Chl<92
92<Chl<110
Chl>110
Other
Chl (Images MODIS, empirical model)
Novo et al 2006 -Limnology (2006) 7:153–161
Seasonal changes :2002 & 2003
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24Brazilian Inland WaterCláudio Barbosa
Fluorometric and Spectral data applied to estimate chlorophyll
concentration in freshwater environments
Empirical models for suspended sediments concentration estimative
in white water amazon rivers from LANDSAT 5 images
Spectral Library: References for water types classification in
Amazon wetlands
Some master dissertation and PhD Thesis
Remote Sensing of water circulation dynamics in the Curuai
floodplain/Amazon River
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25Brazilian Inland WaterCláudio Barbosa
Occurrence and removal of sunglint effects in hyperspectral and high
spatial resolution images from the Spectrir sensor
Integration between MODIS images and census data to assessment of
livestock impact in aquatic systems on the lower amazon
Mapping the Amazon river floodplain using object based classification with
SRTM-DEM and HAND-DEM Data
Some master dissertation and PhD Thesis
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26Blue Planet Symposium:
Advances of inland water Remote sensing in BrazilCláudio Barbosa
Reference spectra to classify Amazon water typesLobo at all 2012 - IJRS Vol. 33, No. 11
Result of SAM algorithms: overall accuracy of 86%
A spectral database (392 spectra) were compiled into spectral library
resulting in 10 reference spectra, that describe four limnological
characteristics
MERISHyperion
Clear water- low concentrations of OAC
Black water- rich in CDOM
Water dominated by chlorophyll-a
Water dominated by
inorganic suspended
solids
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27Blue Planet Symposium:
Advances of inland water Remote sensing in BrazilCláudio Barbosa
Mapping potential cyanobacterial bloom using Hyperion data in Patos
Lagoon estuary -Brazil
Empirical
analytical
Hyperion image
Spectral angle mapping using
two spectral library :
analytical (Kutser, 2004)
empirical
88% of similarity
Lobo at al 2009
Acta Limnol. Bras
vol. 21, no. 3
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28Blue Planet Symposium:
Advances of inland water Remote sensing in BrazilCláudio Barbosa
Project: Spatio-temporal evaluation of inland aquatic system’s eutrophication in response to sugarcane expansion
SpecTIR V-S: Flight lines
Phyto bloom
357 spectral bands
Airborne Hyperspectral Imaging
sunglint
removal
affected by
sunglint
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29Brazilian Inland WaterCláudio Barbosa
Allowed characterizing patterns of spatio-temporal dynamics of water masses
composition without however characterizing the spectral composition of the
underwater light field.
key information to support primary productivity studies.
First period: Above water measurements (2001-2010)
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30Brazilian Inland WaterCláudio Barbosa
In 2010, we moved towards to measure the Inherent and Apparent Optical
Properties in water column in order to do:
the spectral characterization of the underwater light field,
the bio-optical characterization of lakes and reservoirs
Use semi analytical algorithms to retrieve constituents.
Second period: Water column measurements (2011-2016)
Why? semi analytical algorithms have time frame coverage
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31Brazilian Inland WaterCláudio Barbosa
Second period: Water column measurements (2011-2016)
The history timeline (two periods)
Above water measurements & empirical algorithms (2001-2010)
Focused in the seasonal dynamics of volume and water composition
in the Amazon floodplain lakes.
Water column profiles measurements & semi analytical algorithms
Focused in bio-optics characterization and the seasonal dynamics
of bio-optics properties
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3236th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Motivation
MODIS
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3336th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Motivation
MERIS
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34Brazilian Inland WaterCláudio Barbosa
It took us nearly three years and be involved in six projects to acquire the
whole set of instruments
ANEEL
Three FAPESP Projects
FUNDO AMAZONAS
CNPq UFC
FAPESP - mamiraua
Second period: Water column measurements (2011-2016)
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35Brazilian Inland WaterCláudio Barbosa
Some of the Projects that funded instruments
Modeling human impacts on ecological properties of wetland and aquatic
ecosystems of central Amazon floodplain.
Emissions of Greenhouse Gases in Hydroelectric Reservoirs
Spatio-temporal evaluation of inland aquatic system’s eutrophication in response
to sugarcane expansion using remote sensing images
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36Brazilian Inland WaterCláudio Barbosa
ASD HandHeld 2: VNIR
Two AC-S (10 and 25 cm)
HydroScat-6P
Six RAMSES radiometer
LISST-PortableUV-VIS-2600 Shimadzu
ECO BB910AU Field and Laboratory Fluorometer
ADP- Acoustic Doppler Profiler
Instrumentation Laboratory for Aquatic Systems (LabISA)
CTD
http://www.dpi.inpe.br/labisa/index_en.html
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3736th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Course "Radiative Transfer Theory, Optical Oceanography, and
Hydrolight" (Curtis Mobley, INPE, 2013)
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38Brazilian Inland WaterCláudio Barbosa
Examples of recent results
(2012 – 2015)
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3936th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Sampled sites Site Area (Km2) Biome
1 Itaipu 1.350 Atlantic Tropical Forest
2 Três Marias 1.040 Brazilian Savana (Cerrado)
3 Funil 40 Atlantic Tropical Forest
4 Ibitinga 114 Atlantic Tropical Forest
5 Tucuruí 2.430 Amazon
6 Curuai 2000 Amazon
7 Açude Orós 190 Semi-arid Caatinga
Sampled sites (six reservoirs and lakes at Amazonian floodplain)
Selected for accomplishing the goals of the projects that funded the instruments.
Goals: providing support for
Effects of climate change in the Amazon region
Environmental impacts and the net carbon budget in Brazilian reservoirs.
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40Brazilian Inland WaterCláudio Barbosa
Curuai
Três
Marias
Tucurui
Ibitinga
Funil
Itaipú
Orós
Optical and limnological data were gathered along 13 field campaigns
Between 2012 and 2015
Area (Km2)
1 2000
2 2.430
3 190
4 1.040
5 40
6 114
7 1.350
28
224
16
22
20
32
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4136th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Measured variables:
13
0 K
m
Defined taking as reference the ones listed on NASA document that describes key in
situ variables to be measured for satellite ocean color sensor validation, algorithm
development and algorithm validation
Sampling station were defined
based on homogeneous spectral
response mapped from an
unsupervised classification
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4236th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Downward & Upward irradiances /radiances
Es, Ed, Lw, Lu, Eu, Esky (320 to 950nm)
Amazonian floodplainRAMES-TriOs
Six inter-calibrated spectroradiometers
Amazon floodplainReservoirs
Measured variables:
Profiles of Apparent Optical Properties (AOP)
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4336th ISRSE:
Brazilian Inland Water Bio-Optical DatasetCláudio Barbosa
Measured variables:
Profiles of Inherent Optical Properties (IOP) & Probe
Attenuation & Absorption & Backscattering
WET Labs AC-S
hydrostat
Amazonian floodplain
Probe (Tu,Te,Co,pH,DO)
Specific coefficients (aph*, aNAP*, aTP*)
4 Hz sampling rate
OAC (Chl-a, TSS,TSI,TSO,DOC,DIC,CDOM)
Reservoirs
Due to remoteness during Amazonian campaigns , the team stay all time onboard.
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44Brazilian Inland WaterCláudio Barbosa
Pre-processing
Absorption measurements be corrected for scatter absorption tube
Backscattering have be corrected for attenuation in optical path
AOP measurements have be normalized for sunlight changes
Algorithms were developed and tested for case I waters
Some data has to be submitted to correction protocols
Have to be assessed since efficiency is related to water composition
Assessments were made for Amazonian lakes water.
(Carvalho et al, 2015 special edition on inland waters of RSE ).
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45Brazilian Inland WaterCláudio Barbosa
Descriptive statistics of some bio-optical properties of Brazilian inland aquatic systems
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46Brazilian Inland WaterCláudio Barbosa
Range of values
In reservoirs, the chlorophyll-a ranged from 0.6 to 243µg/L while in
Amazonian lakes ranged from 0.90 to 92 µg/L.
In reservoirs the highest Total Suspended Solids (TSS) was 30 mg/L
while at the Amazonian lakes was as high as 160 mg/L.
The median value of beam attenuation coefficient (450 nm)
reaches 5 m-1 in reservoirs and 19 m-1 at the Amazonian lakes.
The Kd (PAR)** reaches 1.5 m-1 in reservoirs and 3.3 at the
Amazonian lakes
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47Brazilian Inland WaterCláudio Barbosa
Red curves are the depth of euphotic zone, and white curves are the attenuation depth (the
thickness of the layer from which 90% of the signal recorded by satellite sensor originates).
Spectral composition of normalized downwelling irradiance relative to surface
incoming light (incident irradiance Es(λ)
Underwater light field:
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48Brazilian Inland WaterCláudio Barbosa
Conclusion
We are building a dataset which is the first and most comprehensive bio-optical
information available for the Brazilian inland waters.
Main difficulty faced The huge amount of data of each campaign
Developing an interactive toolbox for processing and analyzing
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49Brazilian Inland WaterCláudio Barbosa
OPTICAL MODELS AND IN SITU DATA COLLECTED IN FUNIL (RJ)
RESERVOIR USED TO AID RESEARCHES ON REMOTE SENSING OF
CONTINENTAL WATERS
BIO-OPTICAL MODELS TO SUPPORT THE RETRIEVAL OF OPTICALLY
ACTIVE CONSTITUENTS IN AMAZON FLOODPLAIN LAKES
OPTICAL AND DISSOLVED ORGANIC CARBON
CHARACTERIZATION IN TRÊS MARIAS/MG RESERVOIR
Some master dissertation and PhD Thesis
TEMPORAL CHARACTERIZATION OF THE BIO-OPTICAL
PROPERTIES OF THE IBITINGA/SP RESERVOIR
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50Brazilian Inland WaterCláudio Barbosa
Obrigado