studies of aquatic systems properties in the amazon ... · meris/envisat 15 bands modis hyperion...

Studies on aquatic remote sensing

Studies of Aquatic Systems Properties in the Amazon Floodplain and Reservoirs

using Remote Sensing

Cláudio Barbosa

Instituto Nacional de Pesquisas Espaciais

2SeminárioPetropolis-agosto-2008

Lecture

Present a syntheses of our in-process research on the development of

Remote Sensing applications aimed at improving the knowledge of water

properties dynamics of Amazon floodplain lakes and Brazilian reservoirs.

Examples of studies using multispectral, hyperspectral and radar data, as

well as their fusion.

Our studies in Amazon are focused in development of methodologies to

help us to understand the water dynamics in the Amazon basin. The

dynamics in terms of volume and water composition.


Presentation sequence

Introduction

Remote sensing of aquatic systems: the challenges

Multispectral and in situ data example

In-progress studies using Hyperspectral data

Experimental spectral library for water composition

Integrated System for Environmental Monitoring


Our research team

We are multidisciplinary team:

Physicist, oceanographer, geographer,

engineers, cartographer, biologist,

– 15 PhD (including collaborators from other research centers)

– 25 MS and PhDs students

– 6 projects in process

– Inpe, FURG, Universidade Federal Santa Maria, UNESP-Presidente

Prudente, UCSB, University of Victoria, LNCC (Mauricio/Claudia)


Our current study sites


Study sites in Amazon floodplain

Tabatinga

Tefé

ManausSantarém

Belém

working

schedule


Reservoirs study sites

Project:

Carbon Budgets of HydroeletricReservoirs of Furnas Centrais Elétricas S. A

Main objective:

To determine the emissions of greenhouse effect gasses: carbon gas, methane, and nitrous oxide of the reservoirs


http://www.dsr.inpe.br/projetofurnas/


Curuai siteLocated 900 km upstream from the Amazon River mouth.

Óbidos

100 Km

20 Km

O 550 01’S 010 49’

O 550 50’S 020 20’

Juruti

Óbidos

Satarem

JurutiJuruti

ÓbidosÓbidos

Satarem


Mamiraua/Amana site

Water flow and bathymetry


Natural diversity of water composition:The Amazonian basin drains 15% of the fresh water of the earth

Black water (Negro River)

White water (Amazon River)

ManausManaus

SantarSantaréémm

Clear Water (Tapajós River) Costa et al. 2007


Challenges on aquatic remote sensing


Modeling of aquatic systems using Remote sensing: the challenge

a – scattering by inorganic sediment

b – scattering due to water molecules

c – absorption by organic matter

d – bottom reflection (shallows waters)

e – scattering by phytoplankton

water-leaving signal (the radiance) is low.Is a 3D modeling

The spectral range is short (visible and little of near IR )

Professor Tony: very nice explanation RS


Spectral signatures of water column composition

Absorption spectrum of several plant pigments

Fato

r de

Re f

l ect

â nc i

a( %

)Fa

tor

de R

e fl e

ctâ n

c ia

( %)

Comprimento de onda (nm)

Dif

fere

nt c

hlor

ophy

ll co

ncen

trat

ions


The water spectra is shaped by water column composition

Characteristic spectra of High inorganic suspended sediment concentration

Characteristic spectra of high chlorophyll concentration

Characteristic spectra of of dissolved organic matter


Example of complexity

Dynamics of flooding in terms of water level fluctuation

high water stage Image

Annual flood amplitude about 7 meters

Daily water stage records

Low water stage Image

inter-annual fluctuations about 2 meters


Different water composition

1

2

1

3

3

4

56

62

3

6

4

1 – high chlorophyll concentration 2 – medium chlorophyll concentration 3 - high inorganic particle concentration 4 – medium inorganic particle concentration

5 – medium organic matter concentration or low inorganic particle6 - high organic matter concentration

(TM 1-> Blue, TM 2 -> Green, TM 3-> Red)


water composition in distinct year

1999 2002

Intensive water sampling (2003-2007)


In situ radiometric measures processing:Effect of dynamics composition on water spectral response

How changes on

water composition

affect both:

Amplitude and

Shape of the

spectra

Menor TSS => menor amplitude

Estado 1

Estado 4Estado 3

Estado 2

State 2 (low water)

Wave length (nm)

refl

ecta

nce

State 3 (rising water)

State 4 (decline) State 1 (high water)

Wave length (nm)

Wave length (nm)Wave length (nm)

refl

ecta

nce

refl

ecta

nce

refl

ecta

nce


Studies in process


From Radar to multispectral, hyperspectral and fusion

Jers1, Alos,Landsat TM/ETM , 7 bandsMERIS/Envisat 15 bandsMODISHyperionHigh resolution in situ

These sensor have discrete (discontinuous) bands.


MCCACC

ACPIMCPIACMOMCMO

Estado 1MCCACC

ACPIMCPIACMOMCMO

MCCACC

ACPIMCPIACMOMCMO

MCCACC

ACPIMCPIACMOMCMO

Estado 1

Legend

ACC – High Chlorophyll Concentration;MCC - Medium Chlorophyll ConcentrationACPI - High Inorganic Particle ConcentrationMCPI – Medium Inorganic Particle ConcentrationMACP – Very High Inorganic Particle ConcentrationACMO – High Dissolved Organic Matter ConcentrationMCMO – Midium Dissolved Organic Matter Concentration

MCCACC

ACPIMCPIACMOMCMO

Estado 4MCCACC

ACPIMCPIACMOMCMO

MCCACC

ACPIMCPIACMOMCMO

Estado 4

MCCACC

ACPIMCPIACMOMCMO

Estado 2 MCCACC

ACPIMCPIACMOMCMO

MCCACC

ACPIMCPIACMOMCMO

Estado 2 MACPIMCC

ACPIMCPIACMOMCMO

Estado 3MACPIMCC

ACPIMCPIACMOMCMO

MACPIMCC

ACPIMCPIACMOMCMO

Estado 3

State 1 State 4

State 2State 3

Results of mapping

It was needed for us to do an intensive campaigns for water sampling, to obtain theses results.


Curuai – survey to Model water flux

Bathymetry and flux measurements through

ADCP -Acoustic Doppler Current Profilers


Bathymetric survey and processing

Superfície do lago

Fundo do lago

Coluna d´água

Water level variation during survey

Data Total: 4600 Km 115 transects

Results

9 meters


Water flux - ADCP -Acoustic Doppler Current Profilers

Speed in water column

Working with LNCC (Mauricio and Claudia)


Mapping chlorophyll using modis


Seasonal changes in chlorophyll distributions in Amazon floodplain lakes derived from MODIS images

•Figura de campo e animacao evlynSeasonal changes :2002 and 2003

Limnology (2006) 7:153–161


Samples to calibrate the model


Reservoirs in cascade: The Problem

Reservatório de Ibitinga

The first reservoir remove the suspended sediment,but dissolved nutrients go to the next reservoir


Refining our results with:

Medium Resolution Imaging Spectrometer (MERIS)

and

Hyperspectral sensors ((HYPERION))


Working with Working with Medium and hyper spectral Resolution

Nº λ central (nm)

Largura espectral

(nm) Aplicações Potenciais

1 412,5 10 Substância amarela e detritos de pigmentos

2 442,5 10 Máximo de absorção pela clorofila

3 490 10 Clorofila e outros pigmentos

4 510 10 Sedimentos em suspensão, marés vermelhas

5 560 10 Mínimo de absorção pela clorofila

6 620 10 Sedimentos em suspensão

7 665 10 Absorção pela clorofila e referência da fluorescência

8 681,25 7,5 Pico de fluorescência da clorofila

9 708,75 10 Referência da fluorescência, correções atmosféricas

10 753,75 7,5 Vegetação, nuvens

11 760,625 3.75 Banda-R de absorção pelo Oxigênio

12 778,75 15 Correções atmosféricas

13 865 20 Vegetação, referência para o vapor d’água

14 885 10 Correções atmosféricas

15 900 10 Vapor d’água, terra

The Hyperion provides a high resolution hyperspectral imager capable of resolving 220 spectral bands (from .4 to 2.5 µm) with a 30 meter resolution


A tool: Spectral Angle Mapper -SAM

is a spectral classification that uses an n-dimensional angle to match pixels to reference (end-member) spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands.

Banda 1

Ban

da 2

t

rEspectro referência

Espectro teste

α

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛= −

r.tr.t1cosd

Need a set of end-members (spectral library)


Results of SAM classification over in situ spectra

Class 1,3,6 areSimilar

Major TSS

Características limnológicas


Experimental spectral library for water composition


Spectral library for water composition

The idea is to build a spectral library of end-members to classify in

situ spectra and hyperspectral images.

Test with chlorophyll:

obtained a set of reflectance spectra measured in cyanobacteria

cultures (laboratory culture)

obtained a set of reflectance spectra collected in natural bloom

3- obtained a set a spectral library created by running a bio-optical

model


spectra measured in cyanobacteria cultures in Lab with variable concentrations of chlorophyll.

reflectance spectra measured in cyanobacteria cultures at UPC - Unidade de Pesquisa em Cianobactéria -FURG.


Curvas de Referência - Ibitinga

0,00

0,05

0,10

0,15

0,20

0,25

0,30

400 450 500 550 600 650 700 750

nm

R%

900 472 176

99 49 14

Spectral signatures collected in natural bloom

reflectance spectra collected from the bloom area at Ibitinga reservoir. 35 spectra were clustered into 6 classes, and the medium spectra of each class was generated.


C urvas R eferênc ia ‐ K uts er 2004

0

2

4

6

8

10

12

14

400 450 500 550 600 650 700 750

nm

R%

1024 256 128

64 32 8

1

Modeled spectral library (Kutser 2004)

a spectral library created by running a bio-optical model with variable concentrations of chlorophyll.


Result of SAM classification over a hyperion imagenatural bloom •bio-optical modelHYPERION


MERIS/ENVISAT test

•imagem


Meris/envsat versus in situ spectra: spectra 1

In situ

MERIS


Correlation: in situ versus MERIS

To do the correlation analysis, in situ spectra were convertedto the same MERIS bands


Example of fusion : optical and radar


Discrimination of genre of aquatic plants in Tucuruí reservoir

Optics sensorPhysiologic

aspects

Radarmorphologic /

structural

optical-radar fusion

Fonte:Graciani,2002

cyperacea

typha

eicchornia


SIMA: A Near Real Time Data Acquisition System as a Support for Limnological Studies


SIMA-Integrated System for Environmental Monitoring

A Near Real Time Buoy Data Acquisition and Telemetry System as a Support for Limnological Studies – Monitoring of reservoirs

An autonomous anchored system, able to regularly acquire a minimum set of aquatic variables, transmit the data to a processing center that can make the information available immediately to interested users.

The SIMA is composed of data storage systems, sensors, solar

panel, battery and the transmission antenna.


SIMA – Integrated System for Environmental Monitoring

1 - PH 7 - relative humidity2 - Turbidity 8 - atmospheric pressure3 - Dissolved oxygen 9 - wind direction intensity4 - dissolved CO2 10 - water Temperatures in four levels5 - conductivity 11 – Solar Radiance6 - Air temperature (incoming and reflected radiation )

INPE

•Corrigir texto


Installed systems


Examples of every hour collected data

20

22

24

26

28

30

32

34

19/1/04 26/1/04 2/2/04 9/2/04 16/2/04 23/2/04 1/3/04 8/3/04 15/3/04 22/3/04

Time (days)

Nea

r Sur

face

Air

Tem

pera

ture

(°C

)

Wind speed

Air Temperature


Data base available on internet


Example of analysis: The influence of cold fronts

Wind Mixing Índex - Tax with that the wind transfers mechanical energy to the water body to produce turbulent mixture in its upper layer is proportional to the third potency of its speed.

Reservatório de Manso

MANSO RESERVOIR

May 1 to June 22, 2004


Thanks you for your attention


Underflow suspension induced by the interplay between upwelling and Kelvin-Helmholtz instability

Water surface temperature

seasonal plunge point variation (red circle). The images from Landsat-5-TM (a,c)(bands 1, 2, 3 in blue, green and red channels, respectively) and the water surface temperature (b,d) estimated from thermal band (band 6) of Landsat-5-TM

Assireu et al, 2007)

(March 26 )

(July 16 )

•Landsat-5-TM true colour composite


we monitored continuously meteorological parameters, water temperature, dissolved oxygen, and the conductivity in the Manso Reservoir. We also used remote sensing data for the monitoring of surface temperature in the river feeding the reservoir and at the surface of the reservoir. We evidenced a seasonal cycle of thermal stratification/destratification due to different mechanisms: upwelling, upward entrainment engendered by KH instabilities and similar temperature for water from the watershed and reservoir waters


Projects

GEOMA Project: Thematic research cooperative network in environmental modeling

of Amazonian:

PROGRAMA HIDRO: Researches and developments of the processes of the hydrosphere with the objective to understand the processes of evolution of the aquatic systems (marine, coastal and continental), in the space and in the time, through the use of data obtained from remote sensing, data collected in situ and results of models

Carbon Budgets of Hydroeletric Reservoirs of Furnas Centrais Elétricas S. A

Study of the circulation, water quality and land use in the watershed of Itumbiara reservoir.

Evlyn/laura

studies of aquatic systems properties in the amazon ... · meris/envisat 15 bands modis hyperion...

Documents