1 Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and...

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Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high

resolution satellite remote sensing data

Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high

resolution satellite remote sensing data

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

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

Daniel Civco (PI)James HurdUniversity of ConnecticutNatural Resource Management

& Engineering, Storrs, CT

Daniel Civco (PI)James HurdUniversity of ConnecticutNatural Resource Management

& Engineering, Storrs, CT

Fourth International Workshop on the Analysis of Multitemporal Remote Sensing ImagesJuly 18-20, 2007Leuven, Belgium

Fourth International Workshop on the Analysis of Multitemporal Remote Sensing ImagesJuly 18-20, 2007Leuven, Belgium

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OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

3

OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

4

• 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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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

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OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

10

ObjectivesObjectives

… … examine the effectiveness of using multitemporal examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant canopy data to classify and map the common plant communities of the Ragged Rock Creek marshcommunities of the Ragged Rock Creek marsh

… … determine if phenological variations in spectral determine if phenological variations in spectral reflectance and structure of individual marsh plant reflectance and structure of individual marsh plant species in the field can be used to predict when species species in the field can be used to predict when species are best discriminated in multispectral image dataare best discriminated in multispectral image data

… … provide coastal resource managers, municipal provide coastal resource managers, municipal officials and researchers a set of recommended officials and researchers a set of recommended guidelines for remote sensing data collection for marsh guidelines for remote sensing data collection for marsh inventory and analysisinventory and analysis

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ObjectivesObjectives

… … examine the effectiveness of using multitemporal examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant canopy data to classify and map the common plant communities of the Ragged Rock Creek marshcommunities of the Ragged Rock Creek marsh

… … determine if phenological variations in spectral determine if phenological variations in spectral reflectance and structure of individual marsh plant reflectance and structure of individual marsh plant species in the field can be used to predict when species species in the field can be used to predict when species are best discriminated in multispectral image dataare best discriminated in multispectral image data

… … provide coastal resource managers, municipal provide coastal resource managers, municipal officials and researchers a set of recommended officials and researchers a set of recommended guidelines for remote sensing data collection for marsh guidelines for remote sensing data collection for marsh inventory and analysisinventory and analysis

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ObjectivesObjectives

… … examine the effectiveness of using multitemporal examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant canopy data to classify and map the common plant communities of the Ragged Rock Creek marshcommunities of the Ragged Rock Creek marsh

… … determine if phenological variations in spectral determine if phenological variations in spectral reflectance and structure of individual marsh plant reflectance and structure of individual marsh plant species in the field can be used to predict when species species in the field can be used to predict when species are best discriminated in multispectral image dataare best discriminated in multispectral image data

… … provide coastal resource managers, municipal provide coastal resource managers, municipal officials and researchers a set of recommended officials and researchers a set of recommended guidelines for remote sensing data collection for marsh guidelines for remote sensing data collection for marsh inventory and analysisinventory and analysis

13

OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

14

Ragged Rock Marsh Study AreaRagged Rock Marsh Study AreaRagged Rock Marsh Study AreaRagged Rock Marsh Study Area

ConnecticutConnecticut

Ragged RockRagged Rock

Connecticut RiverConnecticut RiverConnecticut RiverConnecticut River

Long Island SoundLong Island Sound

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Ragged Rock Marsh Study AreaRagged Rock Marsh Study AreaRagged Rock Marsh Study AreaRagged Rock Marsh Study Area

142 Hectare Estuarine Tidal Marsh142 Hectare Estuarine Tidal Marsh

Vegetation influenced byVegetation influenced bySalinitySalinityTidal inundationTidal inundationElevationElevation

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0’

12’

6’

Dominant Salt Marsh SpeciesDominant Salt Marsh Species

Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

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Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

Dominant Salt Marsh SpeciesDominant Salt Marsh SpeciesDominant Salt Marsh SpeciesDominant Salt Marsh Species

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Dominant Salt Marsh SpeciesDominant Salt Marsh Species

Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

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Phragmites Phragmites Phragmites Phragmites

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

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

Dominant Salt Marsh SpeciesDominant Salt Marsh Species

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Spartina patensSpartina patens

Phragmites australisPhragmites australis

Typha angustifoliaTypha angustifolia

Dominant Salt Marsh SpeciesDominant Salt Marsh Species

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OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

22

ProceduresProcedures

In Situ Radiometry In Situ Radiometry

QuickBird DataQuickBird Data

Floristic InventoryFloristic Inventory

Band AveragingBand Averaging

SegmentationSegmentation

Rule GenerationRule Generation

ClassificationClassificationLiDAR DataLiDAR Data

CalibrationCalibration

ValidationValidation

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An ADS40 FieldSpecAn ADS40 FieldSpec©© spectrometer was used to spectrometer was used to measure the energy measure the energy reflected from a variety of reflected from a variety of plant species at different plant species at different times during the growing times during the growing season.season.

An ADS40 FieldSpecAn ADS40 FieldSpec©© spectrometer was used to spectrometer was used to measure the energy measure the energy reflected from a variety of reflected from a variety of plant species at different plant species at different times during the growing times during the growing season.season.

May 27, 2004May 27, 2004

Measuring Spectral DifferencesMeasuring Spectral Differences

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• 1 meter above canopy1 meter above canopy• Five scans per canopyFive scans per canopy• Repeated ~ 10 timesRepeated ~ 10 times• Normalized to SpectralonNormalized to Spectralon©©• Between 10 AM and 2 PMBetween 10 AM and 2 PM• Averaged over QuickBird Averaged over QuickBird Bands 1, 2, 3, and 4Bands 1, 2, 3, and 4

• 1 meter above canopy1 meter above canopy• Five scans per canopyFive scans per canopy• Repeated ~ 10 timesRepeated ~ 10 times• Normalized to SpectralonNormalized to Spectralon©©• Between 10 AM and 2 PMBetween 10 AM and 2 PM• Averaged over QuickBird Averaged over QuickBird Bands 1, 2, 3, and 4Bands 1, 2, 3, and 4

May 27, 2004May 27, 2004

Measuring Spectral DifferencesMeasuring Spectral Differences

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LandsatBands

Landsat ETM+ band positions are indicatedLandsat ETM+ band positions are indicated

Reflectance spectra of Phragmites australis in Barn Island Marsh

Reflectance spectra of Phragmites australis in Barn Island Marsh

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Calibration and Validation Field Samples

Calibration and Validation Field Samples

CalibrationCalibration (304)

ValidationValidation (613)

Of the 917 Total Field sample points, 304 were used in the development of classification rules.

Of the remaining 613,only those > 2 meters fromclass boundaries were used in accuracy assessment.

Of the 917 Total Field sample points, 304 were used in the development of classification rules.

Of the remaining 613,only those > 2 meters fromclass boundaries were used in accuracy assessment.

27

Dominant species identified in field data and corresponding image classes

Dominant species identified in field data and corresponding image classes

Communities from floristic inventory Communities in image classification

Class Species Class Species1 Phragmites australis 1 Phragmites australis2 Typha spp. 2 Typha spp.3 Spartina patens 3 Spartina patens4 Water 4 Water5 Schoenoplectus spp. 5 Other/Mix6 Panicum virgatum 5 Other/Mix7 Spartina alterniflora 5 Other/Mix8 Bulboschoenus spp. 5 Other/Mix9 Flotsam 5 Other/Mix10 Phragmites mix 5 Other/Mix11 Other or mixed types 5 Other/Mix12 Phragmites australis and Typha

spp. mix5 Other/Mix

13 Juncus gerardii 5 Other/Mix14 Eleocharis spp. and Eleocharis

spp./Spartina patens mix5 Other/Mix

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MultitemporalQuickBird

Data of Ragged Rock

Marsh

MultitemporalQuickBird

Data of Ragged Rock

Marsh

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MultitemporalQuickBird

Data of Ragged Rock

Marsh

MultitemporalQuickBird

Data of Ragged Rock

Marsh17 June 200517 June 2005

2 July 20042 July 2004

20 July 200420 July 2004

23 July 200523 July 2005

31 July 200631 July 2006

13 August 200613 August 2006

12 September 200412 September 2004

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17 June 200517 June 2005

2 July 20042 July 2004

20 July 200420 July 2004

23 July 200523 July 2005

31 July 200631 July 2006

13 August 200613 August 2006

12 September 200412 September 2004

MultitemporalQuickBird

Data of Ragged Rock

Marsh

MultitemporalQuickBird

Data of Ragged Rock

Marsh

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Data Collection DatesData Collection Dates

14

15

14

19

21

27

2 20

12

8

27

9 27

13

1 12

26

9 1

4 17

23

8

31

13

Year May June July Aug Sept Oct

2004

2005

2006

Month and Day of Month and Day of In SituIn Situ SpectrometrySpectrometry

Extensive Floristic InventoryExtensive Floristic Inventory

Month and Day of QuickBird Data Month and Day of QuickBird Data Use in ClassificationUse in Classification

Month and Day of Other Month and Day of Other QuickBird Data AcquiredQuickBird Data Acquired

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QuickBird Band Ratios Used for QuickBird Band Ratios Used for Image SegmentationImage Segmentation

Image date Weights

Band 2Band 1

Band 3Band 2

Band 4Band 2

Band 4 Band 3

Bands 1, 2, 3, 4 LiDAR

June 17, 2005 - - 0.5 0.5 - -

July 2, 2004 0.5 0.5 0.5 - - -

July 20, 2004 0 - - - Bands 1, 2, 3 = 0.8Band 4 = 1.0

-

Aug 13, 2006 0.5 0.5 0.5 0.5 - -

Sept 12, 2004 0.5 0.5 - 0.5 -

Oct 8, 2004 - - - - - 1.0

The values indicate the weight applied in eCognition during image segmentation. The July 20, 2004 2:1 ratio is the only one to not have a weight of 0.5 due to the inclusion of the raw Quickbird bands from this date

The values indicate the weight applied in eCognition during image segmentation. The July 20, 2004 2:1 ratio is the only one to not have a weight of 0.5 due to the inclusion of the raw Quickbird bands from this date

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Knowledge-based Rules Implemented in eCognition for Classification of Image Objects

Knowledge-based Rules Implemented in eCognition for Classification of Image Objects

High values of the Sept. 12, 2004 NIR/red ratio and high values of LiDAR were used to classify P. australis segments. Middle values of June 17, 2005 NIR/green band ratio, high values of the August 13, 2006 red/green band ratio and middle heights of LiDAR identified Typha spp. objects. High values of the July 20, 2004 green/blue band ratio and low values of the LiDAR height data determined S. patens objects.

High values of the Sept. 12, 2004 NIR/red ratio and high values of LiDAR were used to classify P. australis segments. Middle values of June 17, 2005 NIR/green band ratio, high values of the August 13, 2006 red/green band ratio and middle heights of LiDAR identified Typha spp. objects. High values of the July 20, 2004 green/blue band ratio and low values of the LiDAR height data determined S. patens objects.

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With Respect to LiDAR Analysis, Digitized With Respect to LiDAR Analysis, Digitized Dominant Plant CommunitiesDominant Plant Communities

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Average LiDAR Average LiDAR HeightHeight Value for Each Value for Each Vegetation PolygonVegetation Polygon

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Mean LIDAR HeightsMean LIDAR Heights

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OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

38

Reflectance spectra of Phragmites australis in Ragged Rock Creek Marsh

Reflectance spectra of Phragmites australis in Ragged Rock Creek Marsh

QuickBird band positions and absorptions due to plant pigments are indicatedQuickBird band positions and absorptions due to plant pigments are indicated

2005 Growing2005 GrowingSeasonSeason

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Reflectance spectra of Major Species in Ragged Rock Creek Marsh

Reflectance spectra of Major Species in Ragged Rock Creek Marsh

19 Aug 200419 Aug 2004

QuickBird band positions and absorptions due to plant pigments are indicatedQuickBird band positions and absorptions due to plant pigments are indicated

40

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

NDVINDVI

41

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Band 4Band 3Band 4Band 3

42

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Band 2Band 1Band 2Band 1

43

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Band 3Band 2Band 3Band 2

44

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Field reflectance data recalculated as QB bands for the dominant species over the

2004 -2006 growing seasons

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Error bars are one standard deviation. Circles refer to relationships utilized to create classification rules for each species.

Band 4Band 2Band 4Band 2

46

LiDAR Height of Each Ground Point Displayed Based on Dominant ClassLiDAR Height of Each Ground Point Displayed Based on Dominant Class

47

Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australisPhragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

LIDAR Height RenderingLIDAR Height Rendering

48

Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australisPhragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

LIDAR Height RenderingLIDAR Height Rendering

49

Spartina patensSpartina patensSpartina patensSpartina patens

Phragmites australisPhragmites australisPhragmites australisPhragmites australis

Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia

LIDAR Height RenderingLIDAR Height Rendering

50

LIDAR Height RenderingLIDAR Height Rendering

51

Salt Marsh ClassificationSalt Marsh Classification

52

Confusion matrix for QuickBird classificationConfusion matrix for QuickBird classificationReference data indicate Reference data indicate dominantdominant species species

Classified Data Reference Field DataReference Field Data

Class P. australis Typha sp. S. patens Other/Mix Total Users

P. australis 60 0 0 9 69 87.0%

Typha spp. 13 91 8 42 154 59.1%

S. patens 0 3 57 32 92 62.0%

Other/Mix 5 9 7 49 70 70.0%

Total 78 103 72 133 385

Producers 76.9% 88.3% 79.2% 37.1%

Overall 66.8%

Kappa 0.56

53

Confusion matrix for QuickBird classificationConfusion matrix for QuickBird classificationReference data indicate Reference data indicate presencepresence ofof species species

Classified Data Reference Field DataReference Field Data

Class P. australis Typha sp. S. patens Other/Mix Total Users

P. australis 67  0 0 2 69 95.1%

Typha spp. 9 118 7 20 154 76.6%

S. patens 0 0 85 7 92 92.4%

Other/Mix 5 9 7 49 70 70.0%

Total 81 127 99 78 385

Producers 82.7% 92.9% 85.9% 62.8%

Overall 82.9%

Kappa 0.77

54

Salt Marsh ClassificationSalt Marsh Classification

55

OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

56

The dominant marsh species, The dominant marsh species, Spartina patensSpartina patens, , Phragmites australisPhragmites australis and and TyphaTypha spp., have been spp., have been found to be separable based on their individual found to be separable based on their individual spectral and structural characteristics that vary spectral and structural characteristics that vary over the growing season. over the growing season.

P. australis is found to be most distinct from P. australis is found to be most distinct from other species in late summer and S. patens and other species in late summer and S. patens and Typha spp. species most distinct in midsummer.Typha spp. species most distinct in midsummer.

This study demonstrates the importance of the This study demonstrates the importance of the timing of image acquisition for the identification timing of image acquisition for the identification of targeted plant species in a heterogeneous of targeted plant species in a heterogeneous marsh.marsh.

ConclusionsConclusions

57

The dominant marsh species, The dominant marsh species, Spartina patensSpartina patens, , Phragmites australisPhragmites australis and and TyphaTypha spp., have been spp., have been found to be separable based on their individual found to be separable based on their individual spectral and structural characteristics that vary spectral and structural characteristics that vary over the growing season. over the growing season.

P. australis is found to be most distinct from P. australis is found to be most distinct from other species in late summer and S. patens and other species in late summer and S. patens and Typha spp. species most distinct in midsummer.Typha spp. species most distinct in midsummer.

This study demonstrates the importance of the This study demonstrates the importance of the timing of image acquisition for the identification timing of image acquisition for the identification of targeted plant species in a heterogeneous of targeted plant species in a heterogeneous marsh.marsh.

ConclusionsConclusions

58

The dominant marsh species, The dominant marsh species, Spartina patensSpartina patens, , Phragmites australisPhragmites australis and and TyphaTypha spp., have been spp., have been found to be separable based on their individual found to be separable based on their individual spectral and structural characteristics that vary spectral and structural characteristics that vary over the growing season. over the growing season.

P. australis is found to be most distinct from P. australis is found to be most distinct from other species in late summer and S. patens and other species in late summer and S. patens and Typha spp. species most distinct in midsummer.Typha spp. species most distinct in midsummer.

This study demonstrates the importance of the This study demonstrates the importance of the timing of image acquisition for the identification timing of image acquisition for the identification of targeted plant species in a heterogeneous of targeted plant species in a heterogeneous marsh.marsh.

ConclusionsConclusions

59

OutlineOutline

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

1.1. Salt Marshes 101Salt Marshes 101

2.2. ObjectivesObjectives

3.3. Study AreaStudy Area

4.4. ProceduresProcedures

5.5. ResultsResults

6.6. ConclusionsConclusions

7.7. AcknowledgmentsAcknowledgments

60

AcknowledgmentsAcknowledgments

61

Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high

resolution satellite remote sensing data

Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high

resolution satellite remote sensing data

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

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

Daniel Civco (PI)James HurdUniversity of ConnecticutNatural Resource Management

& Engineering, Storrs, CT

Daniel Civco (PI)James HurdUniversity of ConnecticutNatural Resource Management

& Engineering, Storrs, CT

Fourth International Workshop on the Analysis of Multitemporal Remote Sensing ImagesJuly 18-20, 2007Leuven, Belgium

Fourth International Workshop on the Analysis of Multitemporal Remote Sensing ImagesJuly 18-20, 2007Leuven, Belgium

Thank YouThank You

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