1 Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques Sandy Prisloe Emily Wilson University

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Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques


Sandy PrisloeEmily WilsonUniversity of ConnecticutCooperative Extension System Haddam, CT

Sandy PrisloeEmily WilsonUniversity of ConnecticutCooperative Extension System Haddam, CT

Marty Gilmore (PI)Wesleyan UniversityEarth and Environmental Sciences Middletown, CT


Daniel Civco (PI)James HurdUniversity of ConnecticutNatural Resource Management

& Engineering, Storrs, CT



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OutlineOutline

1.1. Salt Marshes 101Salt Marshes 1012.2. Moderate resolution remote sensing Moderate resolution remote sensing

of tidal marshesof tidal marshes3.3. Airborne LIDAR and satellite high Airborne LIDAR and satellite high

resolution remote sensing of salt resolution remote sensing of salt marsh grassesmarsh grasses

4.4. Classification of tidal wetland Classification of tidal wetland communities using multi-temporal, communities using multi-temporal, single-season QuickBird imagerysingle-season QuickBird imagery





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OutlineOutline









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• Important transitional habitat between the ocean Important transitional habitat between the ocean and the landand the land

– estuaries where fresh and salt water mixestuaries where fresh and salt water mix• Among the most productive ecosystems on Among the most productive ecosystems on earth, rivaling that of an Iowa cornfieldearth, rivaling that of an Iowa cornfield

• Salt marsh plants (halophytes) are salt tolerant Salt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with and adapted to water levels that fluctuate with the tidethe tide

• Tides carry in nutrients that stimulate plant Tides carry in nutrients that stimulate plant growth in the marsh and carry out organic growth in the marsh and carry out organic material that feeds fish and other coastal material that feeds fish and other coastal organismsorganisms

• Over time, salt marshes accumulate organic Over time, salt marshes accumulate organic material, forming into a dense layer called peatmaterial, forming into a dense layer called peat

• Important transitional habitat between the ocean Important transitional habitat between the ocean and the landand the land

– estuaries where fresh and salt water mixestuaries where fresh and salt water mix• Among the most productive ecosystems on Among the most productive ecosystems on earth, rivaling that of an Iowa cornfieldearth, rivaling that of an Iowa cornfield

• Salt marsh plants (halophytes) are salt tolerant Salt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with and adapted to water levels that fluctuate with the tidethe tide

• Tides carry in nutrients that stimulate plant Tides carry in nutrients that stimulate plant growth in the marsh and carry out organic growth in the marsh and carry out organic material that feeds fish and other coastal material that feeds fish and other coastal organismsorganisms

• Over time, salt marshes accumulate organic Over time, salt marshes accumulate organic material, forming into a dense layer called peatmaterial, forming into a dense layer called peat

The Value of MarshesThe Value of Marshes

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• Position on the landscape and their productivity Position on the landscape and their productivity makes them important not only as a part of the makes them important not only as a part of the natural world but also to humansnatural world but also to humans

• About 15,309 acres of salt marsh in Connecticut, About 15,309 acres of salt marsh in Connecticut, many of which have been damaged by many of which have been damaged by management actions that have had unintentional management actions that have had unintentional consequencesconsequences

– Restricted tidal flowRestricted tidal flow– FillingFilling– DitchingDitching– Increased freshwater flowsIncreased freshwater flows

• Due to degradation, restoration is often necessary Due to degradation, restoration is often necessary to improve the following functions that salt to improve the following functions that salt marshes provide, such asmarshes provide, such as

– Nursery area for fish, crustacea, and insectsNursery area for fish, crustacea, and insects– Resting area for migratory waterfowlResting area for migratory waterfowl– Protection against waves and sea level riseProtection against waves and sea level rise– AestheticsAesthetics

• Position on the landscape and their productivity Position on the landscape and their productivity makes them important not only as a part of the makes them important not only as a part of the natural world but also to humansnatural world but also to humans

• About 15,309 acres of salt marsh in Connecticut, About 15,309 acres of salt marsh in Connecticut, many of which have been damaged by many of which have been damaged by management actions that have had unintentional management actions that have had unintentional consequencesconsequences

– Restricted tidal flowRestricted tidal flow– FillingFilling– DitchingDitching– Increased freshwater flowsIncreased freshwater flows

• Due to degradation, restoration is often necessary Due to degradation, restoration is often necessary to improve the following functions that salt to improve the following functions that salt marshes provide, such asmarshes provide, such as

– Nursery area for fish, crustacea, and insectsNursery area for fish, crustacea, and insects– Resting area for migratory waterfowlResting area for migratory waterfowl– Protection against waves and sea level riseProtection against waves and sea level rise– AestheticsAesthetics

The Value of MarshesThe Value of Marshes

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Marsh MorphologyMarsh Morphology

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Marsh MorphologyMarsh Morphology

Artist: Stephanie Schanda (Lee, NH)Project SMART Student, 1998http://www.smart.unh.edu/smartfmb98/saltmarsh/saltmarsh1.html

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The Marsh The Marsh AestheticAesthetic

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… … if it only weren’t for …if it only weren’t for …

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Want more ?Want more ?

TIDAL MARSHES OF TIDAL MARSHES OF

LONG ISLAND SOUND LONG ISLAND SOUND

ECOLOGY, HISTORY, AND RESTORATIONECOLOGY, HISTORY, AND RESTORATION

EDITED BY GLENN D. DREYEREDITED BY GLENN D. DREYER

AND WILLIAM A. NIERINGAND WILLIAM A. NIERING

ILLUSTRATIONS BY THOMAS R. OUELLETTE ILLUSTRATIONS BY THOMAS R. OUELLETTE

http://www.conncoll.edu/ccrec/greennet/arbo/publications/34/frame.htm

http://www.conncoll.edu/ccrec/greennet/arbo/publications/34/FRAME.HTM

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OutlineOutline









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Can tidal wetlands, such as those foundCan tidal wetlands, such as those foundthroughout Long Island Sound throughout Long Island Sound ((thosethosebeing of various shapes and relatively small being of various shapes and relatively small in size, and varying distances from openin size, and varying distances from openwaterwater)) be adequately classified from be adequately classified from onlyonly a asingle date Landsat image single date Landsat image ((and derivedand derivedproductsproducts) ?) ?

Can tidal wetlands, such as those foundCan tidal wetlands, such as those foundthroughout Long Island Sound throughout Long Island Sound ((thosethosebeing of various shapes and relatively small being of various shapes and relatively small in size, and varying distances from openin size, and varying distances from openwaterwater)) be adequately classified from be adequately classified from onlyonly a asingle date Landsat image single date Landsat image ((and derivedand derivedproductsproducts) ?) ?

A basic question …A basic question …

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Integrated Pixel-based andIntegrated Pixel-based andObject-based ClassificationObject-based ClassificationIntegrated Pixel-based andIntegrated Pixel-based andObject-based ClassificationObject-based Classification

… … approachapproach

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Mosaic Path 13, Row 31 & Path 13, Row 32Landsat ETM Sept. 8, 2002.

Mosaic Path 13, Row 31 & Path 13, Row 32Landsat ETM Sept. 8, 2002.

Stitch line

Landsat Enhanced Thematic MapperLandsat Enhanced Thematic Mapper

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Sept 8, 2002 Landsat ETMSept 8, 2002 Landsat ETMSept 8, 2002 Landsat ETMSept 8, 2002 Landsat ETM

Water Index to identify water pixelsWater Index to identify water pixels

digitize some smaller creeksdigitize some smaller creeks

40 pixel buffer from waterpixels40 pixel buffer from waterpixels

Extract image pixelsExtract image pixels

Extracting Analysis AreaExtracting Analysis Area

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Long Island SoundLong Island SoundGardiner’sGardiner’s

BayBay

ConnecticutConnecticutRiverRiver ThamesThames

RiverRiver

HousatonicHousatonicRiverRiver

Analysis AreaAnalysis AreaLandsat ETM, Sept 8, 2002

New YorkNew YorkCityCity

BridgeportBridgeport

NewNewHavenHaven

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Pixel-based ClassificationPixel-based ClassificationPixel-based ClassificationPixel-based Classification

(Unsupervised “Cluster-busting”)(Unsupervised “Cluster-busting”)(Unsupervised “Cluster-busting”)(Unsupervised “Cluster-busting”)

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Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002 ISODATA Unsupervised ClassificationISODATA Unsupervised ClassificationISODATA Unsupervised ClassificationISODATA Unsupervised Classification2nd pass

1002nd pass

1003rd pass

503rd pass

504th pass

504th pass

501st pass

1501st pass

150Clusters:Clusters:

Water, Coastal Marsh, Upland, and OTHERWater, Coastal Marsh, Upland, and OTHERWater, Coastal Marsh, Upland, and OTHERWater, Coastal Marsh, Upland, and OTHER

Pixel-based ClassificationPixel-based ClassificationWheeler Marsh Area, Milford, CTWheeler Marsh Area, Milford, CT

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Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002Landsat ETM Sept. 8, 2002 ISODATA Unsupervised ClassificationISODATA Unsupervised ClassificationISODATA Unsupervised ClassificationISODATA Unsupervised Classification

3x3 Majority Filter (coastal marsh & upland)3x3 Majority Filter (coastal marsh & upland)3x3 Majority Filter (coastal marsh & upland)3x3 Majority Filter (coastal marsh & upland)

Wheeler Marsh Area, Milford, CTWheeler Marsh Area, Milford, CT

Pixel-based ClassificationPixel-based Classification

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Pixel-based ClassificationPixel-based Classification

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Object-based ClassificationObject-based ClassificationObject-based ClassificationObject-based Classification

(eCognition Image Segmentation(eCognition Image Segmentationand Object Oriented Classification)and Object Oriented Classification)(eCognition Image Segmentation(eCognition Image Segmentation

and Object Oriented Classification)and Object Oriented Classification)

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Object-based ClassificationObject-based ClassificationeCognitioneCognition

ColorColor

Shape/SizeShape/Size

TextureTexture

HierarchyHierarchy

OBJECT FEATURESOBJECT FEATURESOBJECT FEATURESOBJECT FEATURES

To Neighbor ObjectsTo Neighbor Objects

To Sub-objectsTo Sub-objects

To Super-objectsTo Super-objects

CLASS RELATEDCLASS RELATEDFEATURESFEATURES

CLASS RELATEDCLASS RELATEDFEATURESFEATURES

Pixels do not have any meaningfulPixels do not have any meaningfulshape characteristics other thenshape characteristics other then

their sizetheir size

Pixels do not have any meaningfulPixels do not have any meaningfulshape characteristics other thenshape characteristics other then

their sizetheir size

• Image segmentation and image object classifierImage segmentation and image object classifier• Does not classify single pixels, but image objects Does not classify single pixels, but image objects produced through image segmentationproduced through image segmentation• Image objects are, simply put, contiguous groups of Image objects are, simply put, contiguous groups of image pixels with similar characteristics.image pixels with similar characteristics.

• Image segmentation and image object classifierImage segmentation and image object classifier• Does not classify single pixels, but image objects Does not classify single pixels, but image objects produced through image segmentationproduced through image segmentation• Image objects are, simply put, contiguous groups of Image objects are, simply put, contiguous groups of image pixels with similar characteristics.image pixels with similar characteristics.

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Object-based ClassificationObject-based ClassificationInput DataInput Data

Landsat 30m 7-band multispectralLandsat 30m 7-band multispectralLandsat 30m 7-band multispectralLandsat 30m 7-band multispectral

Landsat 15m PanchromaticLandsat 15m PanchromaticLandsat 15m PanchromaticLandsat 15m Panchromatic

Principal Components 1,2,4,6Principal Components 1,2,4,6

NDVINDVI

WetnessWetness

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Object-based ClassificationObject-based ClassificationImage Segmentation LEVEL 2Image Segmentation LEVEL 2

Scale parameter: 75Scale parameter: 75Color: 0.9Color: 0.9Shape: 0.1Shape: 0.1

Smoothness: 0.5Smoothness: 0.5Compactness: 0.5Compactness: 0.5

Scale parameter: 75Scale parameter: 75Color: 0.9Color: 0.9Shape: 0.1Shape: 0.1


INPUT LAYERSINPUT LAYERSLandsat REDLandsat REDLandsat NEAR-INFRAREDLandsat NEAR-INFRAREDLandsat MID-INFRAREDLandsat MID-INFRAREDLandsat MID-INFRAREDLandsat MID-INFRAREDNDVI (greenness)NDVI (greenness)PC1 (brightness)PC1 (brightness)Wetness (wetness)Wetness (wetness)PanchromaticPanchromatic


WEIGHTSWEIGHTS1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0

WEIGHTSWEIGHTS1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0

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Object-based ClassificationObject-based ClassificationImage Segmentation LEVEL 2

Water Like (based on WETNESS)Land Like (based on WETNESS)

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Scale parameter: Scale parameter: 1515Color: 0.9Color: 0.9Shape: 0.1Shape: 0.1


Scale parameter: Scale parameter: 1515Color: 0.9Color: 0.9Shape: 0.1Shape: 0.1




WEIGHTSWEIGHTS1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0

WEIGHTSWEIGHTS1.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.0

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Object-based ClassificationSample Training Data

ForestForestHigh Tidal MarshHigh Tidal MarshLow Tidal MarshLow Tidal MarshWaterWaterDevelopedDeveloped

ForestForestHigh Tidal MarshHigh Tidal MarshLow Tidal MarshLow Tidal MarshWaterWaterDevelopedDeveloped

BarrenBarrenBright Dev.Bright Dev.Dark Dev.Dark Dev.Dense Dev.Dense Dev.ForestForestGreen GrassGreen GrassYellow GrassYellow Grass

BarrenBarrenBright Dev.Bright Dev.Dark Dev.Dark Dev.Dense Dev.Dense Dev.ForestForestGreen GrassGreen GrassYellow GrassYellow Grass

High MarshHigh MarshHigh Marsh BrightHigh Marsh BrightLow MarshLow MarshSparse Dev.Sparse Dev.Water RoundedWater RoundedWater LinearWater Linear

High MarshHigh MarshHigh Marsh BrightHigh Marsh BrightLow MarshLow MarshSparse Dev.Sparse Dev.Water RoundedWater RoundedWater LinearWater Linear

Image objects exportedImage objects exportedto ESRI shapefileto ESRI shapefile

Image objects exportedImage objects exportedto ESRI shapefileto ESRI shapefile

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Object-based ClassificationSee5 Data Mining Tool

• See5 is used to extract patterns from substantial databases.See5 is used to extract patterns from substantial databases. • See5 is used to extract patterns from substantial databases.See5 is used to extract patterns from substantial databases.

• See5 analyzes the data and develops decision trees or sets See5 analyzes the data and develops decision trees or sets of if-then rules. of if-then rules. • See5 analyzes the data and develops decision trees or sets See5 analyzes the data and develops decision trees or sets of if-then rules. of if-then rules.

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Object-based ClassificationObject-based Classification

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Classification IntegrationClassification Integration

IMAGINE Knowledge-based EngineerIMAGINE Knowledge-based Engineer

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Classification IntegrationClassification IntegrationIMAGINE Knowledge-based Engineer

Object-based ClassificationObject-based Classification1. Water1. Water2. Low Tidal Marsh2. Low Tidal Marsh3. High Tidal Marsh3. High Tidal Marsh4. Upland4. Upland

Object-based ClassificationObject-based Classification1. Water1. Water2. Low Tidal Marsh2. Low Tidal Marsh3. High Tidal Marsh3. High Tidal Marsh4. Upland4. Upland

ISODATA ClassificationISODATA Classification1. Water1. Water2. Tidal Wetland2. Tidal Wetland3. Upland3. Upland

ISODATA ClassificationISODATA Classification1. Water1. Water2. Tidal Wetland2. Tidal Wetland3. Upland3. Upland

Pixels not captured in thePixels not captured in theintegration were extractedintegration were extractedand classified using ISODATAand classified using ISODATA

Pixels not captured in thePixels not captured in theintegration were extractedintegration were extractedand classified using ISODATAand classified using ISODATA

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Final Tidal Wetland ClassificationFinal Tidal Wetland Classification

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Final Tidal Wetland ClassificationFinal Tidal Wetland Classification

UplandUplandHigh Tidal MarshHigh Tidal MarshLow Tidal MarshLow Tidal MarshWaterWater

UplandUplandHigh Tidal MarshHigh Tidal MarshLow Tidal MarshLow Tidal MarshWaterWater

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Final Tidal Wetland ClassificationFinal Tidal Wetland ClassificationComparison with Photointerpreted MarshesComparison with Photointerpreted Marshes

Photointerpreted MarshPhotointerpreted MarshClassified as High MarshClassified as High MarshClassified as Low MarshClassified as Low MarshClassified as WaterClassified as Water

Photointerpreted MarshPhotointerpreted MarshClassified as High MarshClassified as High MarshClassified as Low MarshClassified as Low MarshClassified as WaterClassified as Water

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OutlineOutline









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Phragmites australisPhragmites australis

• Very aggressiveVery aggressive

• Forms dense Forms dense monoculturesmonocultures

• Displaces Displaces indigenous indigenous vegetationvegetation

• Does not support Does not support rich mix of wildliferich mix of wildlife

• Spreads rapidlySpreads rapidly

• Physically changes Physically changes marshesmarshes

• Very aggressiveVery aggressive

• Forms dense Forms dense monoculturesmonocultures

• Displaces Displaces indigenous indigenous vegetationvegetation

• Does not support Does not support rich mix of wildliferich mix of wildlife

• Spreads rapidlySpreads rapidly

• Physically changes Physically changes marshesmarshes

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Spreads Rapidly - Lord Cove Spreads Rapidly - Lord Cove

19681968 19941994

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GoalsGoals

• Primary goal is to use remote sensing data Primary goal is to use remote sensing data to map to map Phragmites australisPhragmites australis

• Secondary goal is to map other commonly Secondary goal is to map other commonly vegetation types / communitiesvegetation types / communities

• Using a combination of remote sensing Using a combination of remote sensing data including Landsat, QuickBird, ADS40 data including Landsat, QuickBird, ADS40 and LIDAR.and LIDAR.

• Primary goal is to use remote sensing data Primary goal is to use remote sensing data to map to map Phragmites australisPhragmites australis

• Secondary goal is to map other commonly Secondary goal is to map other commonly vegetation types / communitiesvegetation types / communities

• Using a combination of remote sensing Using a combination of remote sensing data including Landsat, QuickBird, ADS40 data including Landsat, QuickBird, ADS40 and LIDAR.and LIDAR.

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Project LocationProject Location

100 km100 km22 area at the mouth area at the mouth of the Connecticut Riverof the Connecticut River100 km100 km22 area at the mouth area at the mouth of the Connecticut Riverof the Connecticut River

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550 m

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Phragmites Phragmites Phragmites Phragmites

S. patens S. patens S. patens S. patens

Typha spp. Typha spp. Typha spp. Typha spp.

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LIDAR Data Collection AreaLIDAR Data Collection Area

LIDAR data provide through a grant from LIDAR data provide through a grant from the NOAA Coastal Service Centerthe NOAA Coastal Service CenterLIDAR data provide through a grant from LIDAR data provide through a grant from the NOAA Coastal Service Centerthe NOAA Coastal Service Center

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LIDAR Data SpecificationsLIDAR Data Specifications

• Acquired October 8, 2004 using a Leica ALS50 Acquired October 8, 2004 using a Leica ALS50 scanner (multiple returns)scanner (multiple returns)

• ~ 300 square kilometer area, 41 Flight Lines~ 300 square kilometer area, 41 Flight Lines

• Average ground sampling distance 0.9 mAverage ground sampling distance 0.9 m

• Vertical accuracy assessment – at 21 QC test points, Vertical accuracy assessment – at 21 QC test points, the average error = 0.002 mthe average error = 0.002 m

• Horizontal accuracy 0.5 mHorizontal accuracy 0.5 m

• Data delivered as 264 tiles of LAS (~14 GB) and text Data delivered as 264 tiles of LAS (~14 GB) and text files (~30 GB)files (~30 GB)

• Attribute data includes: x, y, z, intensity, # of Attribute data includes: x, y, z, intensity, # of returns, return #, classification (non-ground, returns, return #, classification (non-ground, ground, outliers, water)ground, outliers, water)

• Acquired October 8, 2004 using a Leica ALS50 Acquired October 8, 2004 using a Leica ALS50 scanner (multiple returns)scanner (multiple returns)

• ~ 300 square kilometer area, 41 Flight Lines~ 300 square kilometer area, 41 Flight Lines

• Average ground sampling distance 0.9 mAverage ground sampling distance 0.9 m

• Vertical accuracy assessment – at 21 QC test points, Vertical accuracy assessment – at 21 QC test points, the average error = 0.002 mthe average error = 0.002 m

• Horizontal accuracy 0.5 mHorizontal accuracy 0.5 m

• Data delivered as 264 tiles of LAS (~14 GB) and text Data delivered as 264 tiles of LAS (~14 GB) and text files (~30 GB)files (~30 GB)

• Attribute data includes: x, y, z, intensity, # of Attribute data includes: x, y, z, intensity, # of returns, return #, classification (non-ground, returns, return #, classification (non-ground, ground, outliers, water)ground, outliers, water)

HuskyPC

Check verticle accuracy

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Spartina patensSpartina patensSpartina patensSpartina patens


Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

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47

0’

12’

6’

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Spartina patensSpartina patens


Typha angustifoliaTypha angustifolia

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Ground & Non-ground ReturnsGround & Non-ground ReturnsComparison of ground and non-ground returns along Transect 1

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0.0 50.0 100.0 150.0 200.0 250.0 300.0

Distance in meters along transect

Z v

alue

in m

eter

s

Non-ground

Ground

Typha spp. S. patens Typha spp. S. patens P. australis S. patens Typha spp. P. australis S. patens P. australis mixTypha spp. S. patens Typha spp. S. patens P. australis S. patens Typha spp. P. australis S. patens P. australis mixTypha spp. S. patens Typha spp. S. patens P. australis S. patens Typha spp. P. australis S. patens P. australis mixTypha spp. S. patens Typha spp. S. patens P. australis S. patens Typha spp. P. australis S. patens P. australis mix

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Digitized Dominant Plant CommunitiesDigitized Dominant Plant Communities

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Determined Average Lidar Z Value for Determined Average Lidar Z Value for Each Vegetation PolygonEach Vegetation Polygon

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Mean LIDAR HeightsMean LIDAR Heights

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Phragmites australisPhragmites australisPhragmites australisPhragmites australis


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55




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T. angustifolia

S. patens

P. australis

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OutlineOutline









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Goals for Mapping Goals for Mapping PhragmitesPhragmites with with QuickBirdQuickBird

• Explore the use of high-resolution satellite Explore the use of high-resolution satellite imagery to classify and map imagery to classify and map P. australisP. australis

• Use multi-temporal images and utilize Use multi-temporal images and utilize spectral differences due to phenology and spectral differences due to phenology and develop mapping protocoldevelop mapping protocol

• Develop maps of untreated Develop maps of untreated P. australisP. australis and and areas recently treatedareas recently treated

• Create baseline classification of species or Create baseline classification of species or communities besides communities besides Phragmites Phragmites

• Explore the use of high-resolution satellite Explore the use of high-resolution satellite imagery to classify and map imagery to classify and map P. australisP. australis

• Use multi-temporal images and utilize Use multi-temporal images and utilize spectral differences due to phenology and spectral differences due to phenology and develop mapping protocoldevelop mapping protocol

• Develop maps of untreated Develop maps of untreated P. australisP. australis and and areas recently treatedareas recently treated

• Create baseline classification of species or Create baseline classification of species or communities besides communities besides Phragmites Phragmites

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The Need to Map and MonitorThe Need to Map and Monitor

• Where has Where has P. australisP. australis been eradicated? been eradicated?

• How much area has been treated?How much area has been treated?

• Where does Where does P. australisP. australis still grow? still grow?

• Is Is P. australisP. australis reinvading areas? reinvading areas?

• What other plants can be classified from What other plants can be classified from imagery?imagery?

• Where has Where has P. australisP. australis been eradicated? been eradicated?

• How much area has been treated?How much area has been treated?

• Where does Where does P. australisP. australis still grow? still grow?

• Is Is P. australisP. australis reinvading areas? reinvading areas?

• What other plants can be classified from What other plants can be classified from imagery?imagery?

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High Resolution ImageryHigh Resolution Imagery

DigitalGlobe QuickBird satellite imageryDigitalGlobe QuickBird satellite imageryDigitalGlobe QuickBird satellite imageryDigitalGlobe QuickBird satellite imagery

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Lower Connecticut RiverDrains to Long Island Sound

Lord Cove

Upper Island

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QuickBird Image Acquisition QuickBird Image Acquisition

• 20042004– IAGT Ordered QuickBird imagery every 2 weeks IAGT Ordered QuickBird imagery every 2 weeks

from May to Octoberfrom May to October– Best case would be 13 imagesBest case would be 13 images– Actually acquired 4 (due to weather conditions and Actually acquired 4 (due to weather conditions and

competition)competition)• 20052005

– Acquisition extended to 2005 summer seasonAcquisition extended to 2005 summer season– Collected 3 more images Collected 3 more images

• 20062006– Acquisition extended to 2006 summer seasonAcquisition extended to 2006 summer season

• 20042004– IAGT Ordered QuickBird imagery every 2 weeks IAGT Ordered QuickBird imagery every 2 weeks

from May to Octoberfrom May to October– Best case would be 13 imagesBest case would be 13 images– Actually acquired 4 (due to weather conditions and Actually acquired 4 (due to weather conditions and

competition)competition)• 20052005

– Acquisition extended to 2005 summer seasonAcquisition extended to 2005 summer season– Collected 3 more images Collected 3 more images

• 20062006– Acquisition extended to 2006 summer seasonAcquisition extended to 2006 summer season

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May 272004

June 22005

172005

July 22004

82003

202004

232005

August

Sept. 122004

Big Data Gap

4 images from 20044 images from 20043 images from 20053 images from 2005Collection continues in 2006Collection continues in 2006

4 images from 20044 images from 20043 images from 20053 images from 2005Collection continues in 2006Collection continues in 2006

MO

NTH

DAY

Quickbird Image AcquisitionQuickbird Image Acquisition

Take home message: Satellite image acquisition can be unreliableTake home message: Satellite image acquisition can be unreliableTake home message: Satellite image acquisition can be unreliableTake home message: Satellite image acquisition can be unreliable

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Measuring Spectral DifferencesMeasuring Spectral Differences• Wetland species change throughout the growing Wetland species change throughout the growing

seasonseason• Each species greens up and dies off at different Each species greens up and dies off at different

timestimes• These differences can be unique and revealingThese differences can be unique and revealing

• Wetland species change throughout the growing Wetland species change throughout the growing seasonseason

• Each species greens up and dies off at different Each species greens up and dies off at different timestimes

• These differences can be unique and revealingThese differences can be unique and revealing

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A portable spectrometer A portable spectrometer has been used to measure has been used to measure the energy reflected from a the energy reflected from a variety of plant species at variety of plant species at different times during the different times during the growing season.growing season.

A portable spectrometer A portable spectrometer has been used to measure has been used to measure the energy reflected from a the energy reflected from a variety of plant species at variety of plant species at different times during the different times during the growing season.growing season.

May 27, 2004May 27, 2004

Measuring Spectral DifferencesMeasuring Spectral Differences

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Measuring Spectral DifferencesMeasuring Spectral Differences

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GPS-referenced Field ObservationsGPS-referenced Field Observations

• Guide Guide classification classification effortsefforts

• Train image Train image analystsanalysts

• Eventual Eventual accuracy accuracy assessmentassessment

• Guide Guide classification classification effortsefforts

• Train image Train image analystsanalysts

• Eventual Eventual accuracy accuracy assessmentassessment

S. patensS. alternifloraP. australisT. angustifolia

S. patensS. alternifloraP. australisT. angustifolia

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Image Segmentation and ClassificationImage Segmentation and Classification

• Create eCognition project with Create eCognition project with each ratioeach ratio for for each date along with each date along with original bandsoriginal bands from from July 20 imageJuly 20 image

• Experiment and decide on segment sizeExperiment and decide on segment size• Write rules based on Write rules based on a prioria priori knowledge and knowledge and

results from field spectrometryresults from field spectrometry

• Create eCognition project with Create eCognition project with each ratioeach ratio for for each date along with each date along with original bandsoriginal bands from from July 20 imageJuly 20 image

• Experiment and decide on segment sizeExperiment and decide on segment size• Write rules based on Write rules based on a prioria priori knowledge and knowledge and

results from field spectrometryresults from field spectrometry

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Ragged Rock Creek MarshRagged Rock Creek Marsh

LEVEL 4 LEVEL 3 LEVEL 2 LEVEL 1

Image Segmentation and ClassificationImage Segmentation and Classification

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Development of Mapping ProtocolDevelopment of Mapping Protocol

For Phragmites• Use late summer

image• High 4/3 ratio

For Phragmites• Use late summer

image• High 4/3 ratio

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Feature View:Blue areas indicate Phragmites areas used in rule

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Feature View:Blue areas indicate S. patens areas used in rule

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Preliminary ResultsPreliminary Results

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TyphaPhragPatens

Preliminary ResultsPreliminary Results

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ObservationsObservations

Moderate resolution remote Moderate resolution remote

sensing of tidal marshessensing of tidal marshes



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1.1. In general, the classification does a In general, the classification does a fairfair job of capturing tidal wetlands job of capturing tidal wetlands throughout the Long Island Sound throughout the Long Island Sound region.region.



1.1. In general, the classification does a In general, the classification does a fairfair job of capturing tidal wetlands job of capturing tidal wetlands throughout the Long Island Sound throughout the Long Island Sound region.region.

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1.1. In general, the classification does a In general, the classification does a fairfair job of capturing tidal wetlands job of capturing tidal wetlands throughout the Long Island Sound regionthroughout the Long Island Sound region

2.2. Larger marsh complexes are identified Larger marsh complexes are identified more readily. Smaller marsh complexes more readily. Smaller marsh complexes are difficult to detect due to spatial are difficult to detect due to spatial resolution of Landsat.resolution of Landsat.



1.1. In general, the classification does a In general, the classification does a fairfair job of capturing tidal wetlands job of capturing tidal wetlands throughout the Long Island Sound regionthroughout the Long Island Sound region

2.2. Larger marsh complexes are identified Larger marsh complexes are identified more readily. Smaller marsh complexes more readily. Smaller marsh complexes are difficult to detect due to spatial are difficult to detect due to spatial resolution of Landsat.resolution of Landsat.

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Airborne LIDAR and satellite high Airborne LIDAR and satellite high resolution remote sensing of resolution remote sensing of

salt marsh grassessalt marsh grasses



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1.1. LIDAR data effectively can be used to help LIDAR data effectively can be used to help characterize plant heights for the three characterize plant heights for the three species of interestspecies of interest



1.1. LIDAR data effectively can be used to help LIDAR data effectively can be used to help characterize plant heights for the three characterize plant heights for the three species of interestspecies of interest

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1.1. LIDAR data effectively can be used to help LIDAR data effectively can be used to help characterize plant heights for the three characterize plant heights for the three species of interest species of interest

2.2. Based solely on LIDAR non-ground returns, Based solely on LIDAR non-ground returns, it is possible to discriminate among areas it is possible to discriminate among areas dominated by dominated by Phragmites australis, Typha Phragmites australis, Typha angustifolia and Spartina patensangustifolia and Spartina patens



1.1. LIDAR data effectively can be used to help LIDAR data effectively can be used to help characterize plant heights for the three characterize plant heights for the three species of interest species of interest

2.2. Based solely on LIDAR non-ground returns, Based solely on LIDAR non-ground returns, it is possible to discriminate among areas it is possible to discriminate among areas dominated by dominated by Phragmites australis, Typha Phragmites australis, Typha angustifolia and Spartina patensangustifolia and Spartina patens

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Classification of tidal wetland Classification of tidal wetland communities using multi-temporal, communities using multi-temporal, single-season QuickBird imagerysingle-season QuickBird imagery


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1.1. Multitemporal, single-season Multitemporal, single-season in situin situ radiometry and satellite remote sensing can radiometry and satellite remote sensing can lead to maximum spectral separability of lead to maximum spectral separability of salt marsh speciessalt marsh species



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2.2. Rules based on Rules based on in situin situ radiometry can be radiometry can be used to drive an object-oriented used to drive an object-oriented classification of salt marsh speciesclassification of salt marsh species

a)a) S. patensS. patens, , P. australisP. australis, , T. T. sppspp.., and , and CarexCarex spp. spp.



2.2. Rules based on Rules based on in situin situ radiometry can be radiometry can be used to drive an object-oriented used to drive an object-oriented classification of salt marsh speciesclassification of salt marsh species

a)a) S. patensS. patens, , P. australisP. australis, , T. T. sppspp.., and , and CarexCarex spp. spp.

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Further WorkFurther Work

• Extend Landsat-based classification to adjacent Extend Landsat-based classification to adjacent scenesscenes

• Acquire additional multitemporal QuickBird imagery Acquire additional multitemporal QuickBird imagery (especially August – September)(especially August – September)– Also acquiring John Deere (Geovantage) digital MSSAlso acquiring John Deere (Geovantage) digital MSS

• Integrate LIDAR data into the classification process Integrate LIDAR data into the classification process to improve vegetation classesto improve vegetation classes

• Acquire detailed field data for the entire Ragged Acquire detailed field data for the entire Ragged Rock Creek tidal marsh to aid in the development of Rock Creek tidal marsh to aid in the development of an eCognition classification protocol and to serve as an eCognition classification protocol and to serve as validation datavalidation data

• Apply and test classification protocols for other Apply and test classification protocols for other areasareas

• Extend Landsat-based classification to adjacent Extend Landsat-based classification to adjacent scenesscenes

• Acquire additional multitemporal QuickBird imagery Acquire additional multitemporal QuickBird imagery (especially August – September)(especially August – September)– Also acquiring John Deere (Geovantage) digital MSSAlso acquiring John Deere (Geovantage) digital MSS

• Integrate LIDAR data into the classification process Integrate LIDAR data into the classification process to improve vegetation classesto improve vegetation classes

• Acquire detailed field data for the entire Ragged Acquire detailed field data for the entire Ragged Rock Creek tidal marsh to aid in the development of Rock Creek tidal marsh to aid in the development of an eCognition classification protocol and to serve as an eCognition classification protocol and to serve as validation datavalidation data

• Apply and test classification protocols for other Apply and test classification protocols for other areasareas

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AcknowledgmentsAcknowledgments

http://www.iagt.org/

http://www.csc.noaa.gov/

Thank YouThank You







Sandy PrisloeEmily Wilson

University of ConnecticutCooperative Extension System

Haddam, CT

Sandy PrisloeEmily Wilson

University of ConnecticutCooperative Extension System

Haddam, CT



Documents

1 Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques Sandy Prisloe Emily Wilson University