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
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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
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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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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
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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
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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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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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LiDAR Height of Each Ground Point Displayed Based on Dominant ClassLiDAR Height of Each Ground Point Displayed Based on Dominant Class
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Spartina patensSpartina patensSpartina patensSpartina patens
Phragmites australisPhragmites australisPhragmites australisPhragmites australis
Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia
LIDAR Height RenderingLIDAR Height Rendering
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Spartina patensSpartina patensSpartina patensSpartina patens
Phragmites australisPhragmites australisPhragmites australisPhragmites australis
Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia
LIDAR Height RenderingLIDAR Height Rendering
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Spartina patensSpartina patensSpartina patensSpartina patens
Phragmites australisPhragmites australisPhragmites australisPhragmites australis
Typha angustifoliaTypha angustifoliaTypha angustifoliaTypha angustifolia
LIDAR Height RenderingLIDAR Height Rendering
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LIDAR Height RenderingLIDAR Height Rendering
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Salt Marsh ClassificationSalt Marsh Classification
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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
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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
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Salt Marsh ClassificationSalt Marsh Classification
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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
![Page 55: 1 Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high resolution satellite remote sensing](https://reader035.vdocuments.us/reader035/viewer/2022062422/56649e7a5503460f94b7a6a7/html5/thumbnails/55.jpg)
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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
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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
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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
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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
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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
Thank YouThank You