Resource and EnvironmentResource and EnvironmentInformation forInformation for

Management of Coastal HabitatsManagement of Coastal Habitats

Presented during thePresented during theManagement Models and StrategiesManagement Models and Strategies

for Coral Reef and Seagrass Ecosystems Training (MMSfor Coral Reef and Seagrass Ecosystems Training (MMS--CRS)CRS)

Enrico C. Paringit, Dr. Eng.College of Engineering

University of the PhilippinesDiliman, Quezon City

Outline of this PresentationOutline of this PresentationOutline of this Presentation

• Information Needs for Resource andEnvironmental Management

• Remote sensing• Remote sensing applications to coastal

resource assessment and monitoring• Some examples• Some cosiderations

Part I: Coastal Environment andResource Information

Management

Part I: Coastal Environment andPart I: Coastal Environment andResource InformationResource Information

ManagementManagement

Information Flow in ResourceInformation Management

Information Flow in ResourceInformation Flow in ResourceInformation ManagementInformation Management

Specify SystemSpecify System

Collect dataCollect data

Processraw information

Processraw information

ProduceInformation formanagement

ProduceInformation formanagement

ManagementDecisions

ManagementDecisions

ManagementNeeds

ManagementNeeds

Characteristics of Better ManagementCharacteristics of Better ManagementCharacteristics of Better Management

Requires information system that:1. Contains permanent, quantitative, and spatially

extensive records of relevant physical parameters“raw information”

2. Includes facilities to analyze this data to identifytrends, correlations and transform data“required information”

3. Incorporating models for both comparativeanalysis and prediction

4. Is cost-effective5. Provides consistent information for all

Resource Information RequirementsResource Information RequirementsResource Information Requirements

NEEDS:• Common information

– Base maps (topography, hydrography, geology, climate,cultural features)

• Baseline conditions• Resource uses• Resource constraintsUSES• Planning

– The best way to deal with a problem is to prevent it

• Management and Monitoring• Evaluation and Documentation

Integrated ApproachIntegrated ApproachIntegrated Approach

NumericalModeling/Simulation

Remote SensingData Analysis

Remote SensingData Analysis

GIS

FieldObservation

Resource & environment informationmanagement systems

Resource & environment informationResource & environment informationmanagement systemsmanagement systems

• Resource management information systems consistof:– Source data– GIS– Models

• Source Data– Field observations– Maps and plans– Remotely-sensed data

• GIS includes– Databases– Software– Analysis– Display

• Models to– Estimate parameters– Predict

effects/conditions

ERMIS Implementation RequiresERMIS Implementation RequiresERMIS Implementation Requires

• Precise definition of information needs– Type– Resolution– Accuracy and reliability when required

• Detailed information on physical environment inwhich the system will operate– Physical characteristics– Detailed description of resources to be monitored

• How the resources are to be managed– How is the information going to be used– Who will use the information– Who will collect and prepare the information

• Other relevant considerations– Financial, logistics, political, and other constraints

Managing the ChangeManaging the ChangeManaging the Change

Innovation Diffusion Theory

1. Knowledge1. Knowledge 2. Persuasion2. Persuasion 3. Decision3. Decision 4. Implementation4. Implementation 5. Confirmation5. Confirmation

Communication Channels

Characteristics ofdecision-making unit• Socio-economics• Personality• Communication behaviours

Perceived characteristics of innovation• Relative advantages• Compatibility• Trialibility• Observability

1. Adoption

2. Rejection

Continued AdoptionLater adoptionDiscontinuanceContinued rejection

PRIOR CONDITIONSPrevious practice• Needs/problems• Innovativeness• Norms or social systems

PART II. Introduction toPART II. Introduction toRemote SensingRemote Sensing

Features of the coastal zone and the remotesensing process

Features of the coastal zone and the remoteFeatures of the coastal zone and the remotesensing processsensing process

Path radiance

RadianceIrradiance

Scattering at edge ofatmosphere

Specularreflectance

Attenuation due toscattering and absorption

in atmosphere

Further attenuationdue to scattering andabsorption in water

Further scattering atattenuation atatmosphere

Sensor

•Plants and other vegetation

•People

Seagrasses

Coral reef

Mangroves

Sediment andnutrient (C, N, P

and trace metals)

Boat-based habitatmapping methods

BoatBoat--based habitatbased habitatmapping methodsmapping methods

From Mumby Journalof EnvironmentalManagement (1999) 55,157–166

Processes involved:Processes involved:Processes involved:

GG

FFEE

D

CC

AA

1. Energy Source orIllumination (A)

2. Radiation and theAtmosphere (B)

3. Interaction with the Target(C)

4. Recording of Energy by theSensor (D)

5. Transmission, Reception,and Processing (E)

6. Interpretation and Analysis(F)

7. Application (G)

B

λ

Electromagnetic wave (light)Electromagnetic wave (light)Electromagnetic wave (light)

Electromagnetic radiationis energy consisting of anelectrical (E) and magnetic(M) fields oriented at rightangles of each other thattravels throughatmosphere at the speedof light (c) at a certainwavelength (λ)

• Velocity is the speed of light, c=3 x 108 m/s• wavelength (ג) is the length of one wave

cycle, is measured in metres (m) or somefactor of metres such ascentimetres (cm) 10-2 mmicrometres (µm) 10-6 mnanometres (nm) 10-9 m

• Frequency (v) refers to the number ofcycles of a wave passing a fixed point perunit of time. Frequency is normally measuredin hertz (Hz), equivalent to one cycle persecond, and various multiples of hertz. unlike c and ג changing as propagatedthrough media of different densities, vremains constant.Hertz (Hz) 1kilohertz (KHz) 103

megahertz (MHz) 106

gigahertz (GHz) 109

The amplitude of an electromagnetic wave isthe height of the wave crest above theundisturbed position

Travel time from the Sun toEarth is 8 minutes

Three characteristics of electromagnetic waveThree characteristics of electromagnetic waveThree characteristics of electromagnetic wave

The Electromagnetic Spectrum and “AtmosphericWindows”

The Electromagnetic Spectrum andThe Electromagnetic Spectrum and ““AtmosphericAtmosphericWindowsWindows””

However, because of atmospheric effects (absorptionand scattering) only a range of wavelengths can be usedfor remote sensing

The electromagnetic spectrum rangesfrom the shorter wavelengths (includinggamma and x-rays) to the longerwavelengths (including microwaves andbroadcast radio waves). There are severalregions of the electromagnetic spectrumwhich are useful for remote sensing

Our eyes as "remotesensors" can detect onlypart of the visiblespectrum what we perceiveas colors listed below.

• Violet: 0.4 - 0.446 μm• Blue: 0.446 - 0.500 μm• Green: 0.500 - 0.578 μm• Yellow: 0.578 - 0.592μm• Orange: 0.592 - 0.620μm• Red: 0.620 - 0.7 μm

Lots of radiation around are"invisible" to our eyes, butcan be detected by otherremote sensinginstruments can be used toour advantage.

The Electromagnetic SpectrumThe Electromagnetic SpectrumThe Electromagnetic Spectrum

Light interaction with objectsLight interaction with objectsLight interaction with objects

Incident radiationIncident radiation thatreaches and interact with theEarth's surface can have anyof (3) forms of interaction:

• absorption (A);• reflection (R); and• transmission (T).

The total incident energy willinteract with the surface withproportions depending on thewavelength of the energyand the material andcondition of the feature.

Spectral signaturesSpectral signaturesSpectral signatures

The amount of energy that interacts with object varies withwavelength, an important property that enables identification ofdifferent substances or classes. This is called spectral signatures(spectral curves), as shown in the figure below:

Blueband

Green

bandR

edband

NIR

Band

Satellite

Airplane

Balloon

Terrestrialplatform

Platforms for remote sensingPlatforms for remote sensingPlatforms for remote sensing• Ground

– repeat or continuoussampling

– regional or local coverage– example: NEXRAD for

precipitation

• Aircraft– repeat sampling , any

sampling interval– regional or local coverage– examples: airplanes for

photographs; LIDAR forozone and aerosols

Satellite Orbit and SwathSatellite Orbit and SwathSatellite Orbit and Swath

Comparison of Sensor SystemsComparison of Sensor SystemsComparison of Sensor Systems

400 500 600 700 800 900

Landsat ETM+ PAN

Landsat ETM+

SPOT 5 PAN

SPOT 5 XS

ASTER

IKONOS Pan

IKONOS MS

Quickbird Pan

Quickbird Multi

Sate

llite

sens

ors

Wavelength (nm)

Analog (ex. film) vs. digital imaging (ex. CCD)

Image capture and storageImage capture and storageImage capture and storage

Example of a Digital Image?Example of a Digital Image?Example of a Digital Image?

Color Formation ProcessColor Formation ProcessColor Formation Process

SUBTRACTIVE PRIMARY COLORS

Additive Color DisplayAdditive Color DisplayAdditive Color Display

Green + Blue= Cyan

Red + Green= Yellow

Red + Blue= Magenta

Red + Green+ Blue= White

Digital Image DisplayDigital Image DisplayDigital Image DisplayHow your computer monitor works:

CRT displays use RGB color cube to map and display colors.

• Red, Green, and Blue are separate channels• 3 color guns in back of monitor, 1 for each additive primary color• Colors are formed on screen as RGB gets mixed.

This process has implications for how we view, interpret, print, andshare digital remote sensing data.

Digital Image Display (2)Digital Image Display (2)Digital Image Display (2)

Band 4 (0.7-0.9 μm)

Band 3 (0.55-0.7 μm)

Band 2 (0.45-0.55 μm)

RGB:432 (False Color Composite)

Quickbird (2.4m Multi) image Band 1 (Blue)QuickbirdQuickbird (2.4m Multi) image Band 1 (Blue)(2.4m Multi) image Band 1 (Blue)

500m

Quickbird (2.4m Multi) image Band 2 (Green)QuickbirdQuickbird (2.4m Multi) image Band 2 (Green)(2.4m Multi) image Band 2 (Green)

500m

Quickbird (2.4m Multi) image Band 3 (Red)QuickbirdQuickbird (2.4m Multi) image Band 3 (Red)(2.4m Multi) image Band 3 (Red)

500m

Quickbird (2.4m Multi) image Band 4 (NIR)QuickbirdQuickbird (2.4m Multi) image Band 4 (NIR)(2.4m Multi) image Band 4 (NIR)

500m

Quickbird (2.4m Multi) image True Color CompositeQuickbirdQuickbird (2.4m Multi) image True Color Composite(2.4m Multi) image True Color Composite

500m

Ikonos Multispectral08/26/2003 4m

Satellite DatasetsSatellite DatasetsLandsat ETM+02/23/2003 30m

ASTER VNIR02/14/2003 15m

SPOT XS02/07/200310m

Ikonos MultispectralTrue Color Composite Image08/26/2003

Sample ImageSample ImageSample Image

From Paringit and Nadaoka (2003)

Sample Image – Near Infrared Color CompositeSample ImageSample Image –– Near Infrared Color CompositeNear Infrared Color Composite

Ikonos MultispectralNIR Color Composite Image08/26/2003

From Paringit and Nadaoka (2003)

Spatial Resolution of Satellite ImagesSpatial Resolution of Satellite ImagesSpatial Resolution of Satellite Images

0.62.4Quickbird

105

2010

SPOT 5SPOT 4

15(2)-30(5)-90(9)ASTER

0.461.84Worldview

14Ikonos

1530LandsatTM/ETM+

Panchromatic(m)

Multispectral (m)

Spatial Resolution of Satellite ImagesSpatial Resolution of Satellite ImagesSpatial Resolution of Satellite Images

Quickbird Pan (0.6m)

Ikonos Pan (1m)

Quickbird Multi (2.4 m)

Ikonos Pan (4 m)

Spot 5 Pan (5 m)

SPOT 5 XS (10 m)

Landsat TM/ETM (5 m)

Landsat TM/ETM band8 (15 m)ASTER (15m)

SPOT 4 (20 m)

Simulated pixel resolutions common to multiand hyperspectral remote sensing systems

Simulated pixel resolutions common to multiSimulated pixel resolutions common to multiandand hyperspectralhyperspectral remote sensing systemsremote sensing systems

A: 1 m (aerial imaging).B: 2 m (space imaging,Quickbird).C: 4 m (aerial imaging, Ikonos).D: 10 m (several proposedspaceborne).E: 20 m (AVIRIS, SPOT).F: 30 m (Landsat).

From P.J. Mumby et al. /Marine Pollution Bulletin 48(2004) 219–228

Mapping Scales Mapping Scales Mapping Scales

Landsat TM, LISS501:500,000

OCTS,OCM5001,5,000,000

Aircraft MSS, Ikonos XS51:50,000

SPOT HRG101:100,000

NOAA AVHRR, MODIS10001,10,000,000

SPOT HRVIR, Landsat TM251:250,000

Ikonos Panchromatic11:10,000

Sensor (nominal)ApproximatePixel Size (m)

Scale

From Richards (2006)

sensor sun

Sea surface

atmosphere

Seawatercolumn

Corals

Spectral Mixture Model

- a way to considersdifferent benthic covertypes

Spectral Mixture Model

- a way to considersdifferent benthic covertypes

3-D Coral Model

- to treat coralmorphology

3-D Coral Model

- to treat coralmorphology

Water Column Model

- to take into account presence of sea watercomponents

Water Column Model

- to take into account presence of sea watercomponents

Reef Remote Sensing: COMPONENTSReef Remote Sensing: COMPONENTSReef Remote Sensing: COMPONENTS

Atmospheric modelAtmospheric model

Benthic cover

PART III: Applications of RemoteSensing For Coral and Seagrass

Habitat Assessment and Monitoring

PART III: Applications of RemotePART III: Applications of RemoteSensing For Coral and SeagrassSensing For Coral and Seagrass

Habitat Assessment and MonitoringHabitat Assessment and Monitoring

Satellite observation goals:low resolution

Satellite observation goalsSatellite observation goals::low resolutionlow resolution

• Ocean color• SST: spatial resolution 1 km,

accuracy 0.05-0.1°C• Atmospheric sounding: CO2 in

water, Temperature/humidityprofiles

• Sea surface salinity• Wind scatterometers: near coast,

more frequent• Altimeters• SAR: improve algorithms for wave

height/direction

Satellite observation goals: high resolutionSatellite observation goals: high resolutionSatellite observation goals: high resolution

• Protocols to map/monitorecosystem health

• Hyper 1 meter sensor• Multi non-pointable or

hyper-pointable• Resolution 1-5 meters• Coverage annual,

monthly pass• Radiometric accuracy >

12 bits

In situ needsInIn situsitu needsneeds

• Strategy to optimize information frominstrument platforms, scientificmonitoring and volunteer monitoring

• Integrate remote sensing into in situmonitoring via regional centres

• Determine capacity to extrapolate tolarge reef areas

• New underwater remote sensinginstrument packages

Mapping Corals and MacroalgaeMapping Corals andMapping Corals and MacroalgaeMacroalgae

Mapping Corals and MacroalgaeMapping Corals andMapping Corals and MacroalgaeMacroalgae

• Satellite images reveal a lot of informationthrough tone, color, texture, and pattern ofdifferent habitat

• CONTEXTUAL EDITING - “the application ofcommon sense to habitat mapping”

Image ClassificationImage ClassificationImage Classification

THREE Main Approaches1. Visual interpretation2. Unsupervised classification of multispectral

image3. Supervised multispectral classification

- Uses field survey information

Ecological habitat classificationEcological habitat classificationEcological habitat classification

Steps in creating an ecologicalhabitat classification scheme

From Edwards et. al (2000)

Field data multivariate classificationField data multivariate classificationField data multivariate classification

Right: Dendrogram for seagrasshabitats showing three levels ofdescriptive resolution. Clusters 1 and 2exist at all three levels of the hierarchyand small clusters such as thosebetween 5 and 6 (uncoloured), areremoved because they are too rare

From Edwards et. al (2000)

Preliminary marine classificationPreliminary marine classificationPreliminary marine classification

Calcareous green algaeFleshy brown algae

Algal dominated

Thalassia dominated (high density)Thalassia dominated (low density)Syringodium dominated (high density)Syringodium dominated (low density)Mixed seagrasses (high density)Mixed seagrasses (low density)

Seagrass

SandMudHard substratum

Bare Substratum

ForereefSpur and grooveGorgonian plainMontastraea reefAcropora palmata zone(i.e. branching corals)Reef crestBack reefMixed back reef community(seagrass / corals)Algal rubble, Porites spp. zoneCarbonate pavementPatch reef

Reef

Specific habitat classesGeneral habitat type

From Edwards et. al (2000)

Seagrass habitat classificationSeagrass habitat classificationSeagrass habitat classification

dense colonies of calcareous algae – principally Penicillus spp. (55 m-2)and Halimeda spp. (100 m-2)Thalassia testudinum of medium standing crop (~80 g. m-2)

Thalassia and densecalcareous algae

Algal habitats9

medium dense colonies of calcareous algae – principally Halimeda spp.(25 m-2)Thalassia testudinum of low standing crop (< 10 g.m-2)

low to mediumstanding crop

Thalassia testudinum of low standing crop (5 g.m-2) and sand

Thalassia testudinum of low standing crop (5 g.m-2) and Batophora sp.(33%)low standing crop

Thalassia and sparsealgaeSand habitats

6, 7, 8

Thalassia testudinum and Syringodium filiforme of standing crop (80-280 g.m-2)

Thalassia testudinum and Syringodium filiforme of standing crop (5-80 g.m-

2)Thalassia andSyringodium ofmedium to highstanding crop

Seagrasshabitats4, 5

Thalassia testudinum, Syringodium filiforme, and Halodule wrightii of low tomedium standing crop (< 10 g.m-2)

Syringodium filiforme of low standing crop (5 g.m-2)

Halodule wrightii of low standing crop (5 g.m-2)

Rare habitatclasses(removed)

FineMediumCoarse

Descriptive Resolution

MacroalgaeMacroalgaeMacroalgae

Describes algae that are large enough to see by theeye:

1. Fleshy algae2. Calcareous algae3. Turf algae4. Crustose algaeThree species -• Red - important reef-building organisms• Green algae (Chlorophyta) ex. Enteromorpha,

Halimeda• Brown algae (Phaeophyta) - contain brown

pigment fucoxanthin

General ClassesGeneral ClassesGeneral Classes

• Sand• Seagrass• Corals• Algae

Ikonos Multispectral True ColorComposite Image08/26/2003

Sample ImageSample ImageSample Image

From Paringit and Nadaoka (2003)

Benthic Cover from Image ClassificationBenthic Cover from Image ClassificationBenthic Cover from Image Classification

Fukido River Mouth Area Shiraho Reef Area

4 m

10 m

15 m

30 m

Sample of Image ClassificationSample of Image ClassificationSample of Image Classification

From Paringit and Nadaoka (2003)

Accuracy of satellite sensors formapping reefs

Accuracy of satellite sensors forAccuracy of satellite sensors formapping reefsmapping reefs

Coarse [4 habitats], medium [8 habitats], and fine [13 habitats]). Error barsdenote 95% confidence level of tau coefficients.

From P.J. Mumby, A.J. Edwards / Remote Sensing of Environment 82 (2002) 248–257

Mapping BathymetryMapping BathymetryMapping Bathymetry

Uses:• Mapping shipping hazards• Mapping transportation corridors• Updating/Augmenting existing charts• Planning hydrographic surveys• Coastal sediment/accumulation or loss• Interpretation of reef features

Shallow-water BathymetryShallowShallow--water Bathymetrywater Bathymetry

Bathymetry measurementBathymetry measurementusingusing echosounderechosounder

Bathymetry measurementBathymetry measurementusingusing stadiastadia levelingleveling

Mapping BathymetryMapping BathymetryMapping Bathymetry

• Basic Theory:where I0= intensity of incident light in one image band

Id = intensity of light after passing through depthz = depthK = attenuation coefficient

• Assumptions1. Light attenuates exponentially with depth2. Water quality does not vary with image3. Albedo of substrate is constant (ex. All sand)

• Steps1. Calculation of Depth-of-Penetration (DOP) zone2. Interpolation of depths within DOP zones3. Calibration of depths within DOP zones

( )0 expdI I Kz= −

Bathymetry Mapping from SatelliteBathymetry Mapping from SatelliteBathymetry Mapping from Satellite

Fukido River Mouth Shiraho Reef Area

Coastal Benthic Cover Mapping: The Product SuiteCoastal Benthic Cover Mapping: The Product SuiteCoastal Benthic Cover Mapping: The Product Suite

-30 -25 -20 -15 -10 -5 0

m

Estimated seagrasscoverage

Classification Image Bathymetry

Mapping Water QualityMapping Water QualityMapping Water Quality

1. Detection (presence or absence of apollutant)

2. Quantitative mapping – when relationshipbetween sensor signal and pollutant inwater can be established

3. Tracking, or pattern of dispersal – repeatedimaging allows for movement of thepollutant to be mapped

4. Damage Assessment

Suspended Particle Matter Concentration Mapping (1)Suspended Particle Matter Concentration Mapping (1)Suspended Particle Matter Concentration Mapping (1)

2001/01/03 2001/04/25 2001/11/03

2002/03/27 2002/09/03 2002/12/08

2003/01/16

ASTER IMAGERY acquired through the ERSDACASTER ARO Project

Note: All images are plotted in Red-Green-Blue Falsecolor composite: Bands 3, 2 and 1 respectively

Estimated SPM ConcentrationsEstimated SPM ConcentrationsEstimated SPM Concentrations

03 Jan’01 25 Apr’01

27 Mar’02

03 Nov ‘01

03 Sep’02 08 Dec ‘02

16 Jan ‘03

Part IV: Some Applicationsof Remote Sensing in Coastal

Resource Management

Part IV: Some ApplicationsPart IV: Some Applicationsof Remote Sensing in Coastalof Remote Sensing in Coastal

Resource ManagementResource Management

Coral reef and the problem of“red soil” pollution (sedimentation)

Coral reef and the problem ofCoral reef and the problem of““red soilred soil”” pollution (sedimentation)pollution (sedimentation)

Heavy rainfall in adjacent watershed

Soil erosion

Sedimentdischarge tocoral reefs

Coral damage oreven mortality

Nov 2000 Jan 2001 Feb 2001 Jun 2001

Computed Sediment Coverage from Satellite ImageryComputed Sediment Coverage from Satellite Imagery

TodorokiRiver

TodorokiRiver

TodorokiRiver

TodorokiRiver

0 5 10 15 20 25 30+ %

Percentage cover

Jun 2001 images reveals abrupt spread inhigh sediment coverage south of the TodorokiRiver.

Puerto Galera, PhilippinesPuertoPuerto GaleraGalera, Philippines, Philippines120°54'0"E 120°55'0"E 120°56'0"E 120°57'0"E 120°58'0"E 120°59'0"E

13°30'0"N

13°31'0"N

13°32'0"N

Muelle

0 21Kilometers

PUERTO GALERAPhilippines

White Beach

Sabang

PuertoGalera

Baysandbar(

LUZON ISLAND

MINDORO ISLAND

• Abound with rich biodiversity;• coral, seagrass beds and mangroves-

• Experienced degradation due to environmentalpressure

Puerto Galera: Field Campaign 2005PuertoPuerto GaleraGalera: Field Campaign 2005: Field Campaign 2005

Water sampler With local people

Current meter, Chl-a, turbiditymeter and Salinometer

Current meter

STD-type sensor Weather stationWeather station

contour interval ： 50m

2.5 km

(a) Time = 0 hour

2.5 km

1 m/s contour interval ： 50m

(c) Time = 4 hours

2.5 km

1 m/s contour interval ： 50m

(b) Time = 2 hours

1 m/s

Formation factor of the strong asymmetry flow pattern determined

Puerto Galera: Numerical Modeling ResultPuertoPuerto GaleraGalera: Numerical Modeling Result: Numerical Modeling Result

9/16 0:00 9/17 0:00 9/18 0:00

0.80.4

0-0.4-0.8

(m)measurement computation（Grid 2）

Comparison of water surface elevationmeasurement with computationalresult at O1 in Grid 2

(a)

9/15 0:00 9/16 0:00 9/17 0:00 9/18 0:00

80400

-40-80

(cm/s)C1 C2

(b) Computational flow velocity in theprincipal axis at C1 & C2 in Grid2

Validation of numerical model against measurements

Puerto Galera: Land Cover StudyPuertoPuerto GaleraGalera: Land Cover Study: Land Cover Study

Images taken from Advanced Spectral and Thermal Radiometer (ASTER)

2000

2005

White Beach White BeachWhite BeachWhite Beach White BeachWhite Beach

Sabang SabangSabangSabang SabangSabang

Muelle MuelleMuelleMuelle MuelleMuellebuilt-up 2001built-up expansion 2005forest 2005deforested 2005 (from 2001)

built-up 2001built-up expansion 2005forest 2005deforested 2005 (from 2001)

Satellite vs. Field-Derived BathymetrySatellite vs. FieldSatellite vs. Field--Derived BathymetryDerived Bathymetry

Bathymetry mapped by echosoundingtechnique

Bathymetry mapped by application ofoptical model on IKONOS multispectral

image (Correlation = 0.82)

0 5 10 15 20 25 m

Checklist of costs and timeconsiderations

Checklist of costs and timeChecklist of costs and timeconsiderationsconsiderations

• Set-up costs– Computers, printer, software, books, maps, etc

• Field survey– Boat, staff, DGPS, notebook PC, imagery prints,

echosounder, diving equipment, quadrat• Image acquisition

– Purchase, airborne data• Image processing and derivation of habitat

classes– Correction, class derivation, classification,

mosaicking, interpretation, digitization, editing.Accuracy assessment

Summary of key points (1)Summary of key points (1)Summary of key points (1)

• RS is used as “background” information• Match available technology with management

objectives• Integrate field surveys with remote sensing

– Accurate habitat mapping depends on adequate fieldsurvey

• Cloud cover is a main constraint• Maps of unknown accuracy are of little value• Categorize habitat according to natural groupings• RS needs prior pre-processing

– Atmospheric correction– Water column correction (when bathymetry is present)

Summary of key points (1)Summary of key points (1)Summary of key points (1)

• Landsat TM most cost effective forbroadscale coral reef mapping

• SPOT XS & Landsat perform well inassessing standing crop

• Investment in image processing is time wellspent!