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!