1. 1 Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high resolution satellite remote sensing data Sandy Prisloe Emily Wilson University of Connecticut Cooperative Extension System Haddam, CT Sandy Prisloe Emily Wilson University of Connecticut Cooperative Extension System Haddam, CT Marty Gilmore (PI) Wesleyan University Earth and Environmental Sciences Middletown, CT Marty Gilmore (PI) Wesleyan University Earth and Environmental Sciences Middletown, CT Daniel Civco (PI) James Hurd University of Connecticut Natural Resource Management & Engineering, Storrs, CT Daniel Civco (PI) James Hurd University of Connecticut Natural Resource Management & Engineering, Storrs, CT Fourth International Workshop on the Analysis of Multitemporal Remote Sensing Images July 18-20, 2007 Leuven, Belgium

2. 2Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

3. 3Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

4. 4 Important transitional habitat between the ocean and the landImportant transitional habitat between the ocean and the land –estuaries where fresh and salt water mix Among the most productive ecosystems on earth, rivaling that of an Iowa cornfieldAmong the most productive ecosystems on earth, rivaling that of an Iowa cornfield Salt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with the tideSalt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with the tide Tides carry in nutrients that stimulate plant growth in the marsh and carry out organic material that feeds fish and other coastal organismsTides carry in nutrients that stimulate plant growth in the marsh and carry out organic material that feeds fish and other coastal organisms Over time, salt marshes accumulate organic material, forming into a dense layer called peatOver time, salt marshes accumulate organic material, forming into a dense layer called peat Important transitional habitat between the ocean and the landImportant transitional habitat between the ocean and the land –estuaries where fresh and salt water mix Among the most productive ecosystems on earth, rivaling that of an Iowa cornfieldAmong the most productive ecosystems on earth, rivaling that of an Iowa cornfield Salt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with the tideSalt marsh plants (halophytes) are salt tolerant and adapted to water levels that fluctuate with the tide Tides carry in nutrients that stimulate plant growth in the marsh and carry out organic material that feeds fish and other coastal organismsTides carry in nutrients that stimulate plant growth in the marsh and carry out organic material that feeds fish and other coastal organisms Over time, salt marshes accumulate organic material, forming into a dense layer called peatOver time, salt marshes accumulate organic material, forming into a dense layer called peat The Value of Marshes

5. 5 Position on the landscape and their productivity makes them important not only as a part of the natural world but also to humansPosition on the landscape and their productivity makes them important not only as a part of the natural world but also to humans About 15,309 acres of salt marsh in Connecticut, many of which have been damaged by management actions that have had unintentional consequencesAbout 15,309 acres of salt marsh in Connecticut, many of which have been damaged by management actions that have had unintentional consequences –Restricted tidal flow –Filling –Ditching –Increased freshwater flows Due to degradation, restoration is often necessary to improve the following functions that salt marshes provide, such asDue to degradation, restoration is often necessary to improve the following functions that salt marshes provide, such as –Nursery area for fish, crustacea, and insects –Resting area for migratory waterfowl –Protection against waves and sea level rise –Aesthetics Position on the landscape and their productivity makes them important not only as a part of the natural world but also to humansPosition on the landscape and their productivity makes them important not only as a part of the natural world but also to humans About 15,309 acres of salt marsh in Connecticut, many of which have been damaged by management actions that have had unintentional consequencesAbout 15,309 acres of salt marsh in Connecticut, many of which have been damaged by management actions that have had unintentional consequences –Restricted tidal flow –Filling –Ditching –Increased freshwater flows Due to degradation, restoration is often necessary to improve the following functions that salt marshes provide, such asDue to degradation, restoration is often necessary to improve the following functions that salt marshes provide, such as –Nursery area for fish, crustacea, and insects –Resting area for migratory waterfowl –Protection against waves and sea level rise –Aesthetics The Value of Marshes

6. 6 Marsh Morphology

7. 7 Artist: Stephanie Schanda (Lee, NH) Project SMART Student, 1998 http://www.smart.unh.edu/smartfmb98/saltmarsh/saltmarsh1.html

8. 8 Want more ? TIDAL MARSHES OF LONG ISLAND SOUND ECOLOGY, HISTORY, AND RESTORATION EDITED BY GLENN D. DREYER AND WILLIAM A. NIERING ILLUSTRATIONS BY THOMAS R. OUELLETTE http://www.conncoll.edu/ccrec/greennet/arbo/publications/34/frame.htm

9. 9Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

10. 10 Objectives … examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh … determine if phenological variations in spectral reflectance and structure of individual marsh plant species in the field can be used to predict when species are best discriminated in multispectral image data … provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and analysis

11. 11 Objectives … examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh … determine if phenological variations in spectral reflectance and structure of individual marsh plant species in the field can be used to predict when species are best discriminated in multispectral image data … provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and analysis

12. 12 Objectives … examine the effectiveness of using multitemporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh … determine if phenological variations in spectral reflectance and structure of individual marsh plant species in the field can be used to predict when species are best discriminated in multispectral image data … provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and analysis

13. 13Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

14. 14 Ragged Rock Marsh Study Area Connecticut Ragged Rock Connecticut River Long Island Sound

15. 15 Ragged Rock Marsh Study Area 142 Hectare Estuarine Tidal Marsh Vegetation influenced by Salinity Tidal inundation Elevation

16. 16 0’ 12’ 6’ Dominant Salt Marsh Species Spartina patens Phragmites australis Typha angustifolia

17. 17 Spartina patens Phragmites australis Typha angustifolia Dominant Salt Marsh Species

18. 18 Dominant Salt Marsh Species Spartina patens Phragmites australis Typha angustifolia

19. 19 PhragmitesPhragmites S. patens Typha spp. Dominant Salt Marsh Species

20. 20 Spartina patens Phragmites australis Typha angustifolia Dominant Salt Marsh Species

21. 21Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

22. 22 Procedures In Situ Radiometry QuickBird Data Floristic Inventory Band Averaging Segmentation Rule Generation Classification LiDAR Data Calibration Validation

23. 23 An ADS40 FieldSpec© spectrometer was used to measure the energy reflected from a variety of plant species at different times during the growing season. May 27, 2004 Measuring Spectral Differences

24. 24 1 meter above canopy 1 meter above canopy Five scans per canopy Five scans per canopy Repeated ~ 10 times Repeated ~ 10 times Normalized to Spectralon© Normalized to Spectralon© Between 10 AM and 2 PM Between 10 AM and 2 PM Averaged over QuickBird Bands 1, 2, 3, and 4 Averaged over QuickBird Bands 1, 2, 3, and 4 1 meter above canopy 1 meter above canopy Five scans per canopy Five scans per canopy Repeated ~ 10 times Repeated ~ 10 times Normalized to Spectralon© Normalized to Spectralon© Between 10 AM and 2 PM Between 10 AM and 2 PM Averaged over QuickBird Bands 1, 2, 3, and 4 Averaged over QuickBird Bands 1, 2, 3, and 4 May 27, 2004 Measuring Spectral Differences

25. 25 Landsat Bands Landsat ETM+ band positions are indicated Reflectance spectra of Phragmites australis in Barn Island Marsh

26. 26 Calibration and Validation Field Samples Calibration Calibration (304) Validation Validation (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 from class 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 from class boundaries were used in accuracy assessment.

27. 27 Dominant species identified in field data and corresponding image classes Communities from floristic inventoryCommunities in image classification ClassSpeciesClassSpecies 1Phragmites australis1 2Typha spp.2 3Spartina patens3 4Water4 5Schoenoplectus spp.5Other/Mix 6Panicum virgatum5Other/Mix 7Spartina alterniflora5Other/Mix 8Bulboschoenus spp.5Other/Mix 9Flotsam5Other/Mix 10Phragmites mix5Other/Mix 11Other or mixed types5Other/Mix 12Phragmites australis and Typha spp. mix 5Other/Mix 13Juncus gerardii5Other/Mix 14Eleocharis spp. and Eleocharis spp./Spartina patens mix 5Other/Mix

28. 28 Multitemporal QuickBird Data of Ragged Rock Marsh

29. 29 Multitemporal QuickBird Data of Ragged Rock Marsh 17 June 2005 2 July 2004 2 July 2004 20 July 2004 23 July 2005 31 July 2006 13 August 2006 12 September 2004

30. 30 17 June 2005 2 July 2004 2 July 2004 20 July 2004 23 July 2005 31 July 2006 13 August 2006 12 September 2004 Multitemporal QuickBird Data of Ragged Rock Marsh

31. 31 Data Collection Dates 1414 1515 1414 1919 2121 2727 22020 1212 8 2727 92727 1313 11212 2626 91 41717 2323 8 3131 1313 YearMayJuneJulyAugSeptOct 2004 2005 2006 Month and Day of In Situ Spectrometry Extensive Floristic Inventory Month and Day of QuickBird Data Use in Classification Month and Day of Other QuickBird Data Acquired

32. 32 QuickBird Band Ratios Used for Image Segmentation Image date Weights Band 2 Band 1 Band 3 Band 2 Band 4 Band 2 Band 4 Band 3 Bands 1, 2, 3, 4LiDAR June 17, 2005--0.5 -- July 2, 20040.5 --- July 20, 20040--- Bands 1, 2, 3 = 0.8 Band 4 = 1.0 - Aug 13, 20060.5 -- Sept 12, 20040.5 - - 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

33. 33 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.

34. 34 With Respect to LiDAR Analysis, Digitized Dominant Plant Communities

35. 35 Average LiDAR Height Value for Each Vegetation Polygon

36. 36 Mean LIDAR Heights

37. 37Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

38. 38 Reflectance spectra of Phragmites australis in Ragged Rock Creek Marsh QuickBird band positions and absorptions due to plant pigments are indicated 2005 Growing Season

39. 39 Reflectance spectra of Major Species in Ragged Rock Creek Marsh 19 Aug 2004 QuickBird band positions and absorptions due to plant pigments are indicated

40. 40 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. NDVI

41. 41 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. Band 4 Band 3 Band 4 Band 3

42. 42 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. Band 2 Band 1 Band 2 Band 1

43. 43 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. Band 3 Band 2 Band 3 Band 2

44. 44 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. Band 4 Band 2 Band 4 Band 2

45. 45 Of the five simple band ratios calculated from the field reflectance spectra, four were determined to be most useful in identifying at least one major plant community – P. australis, the NIR/red ratio on September 8, 2006 – S. patens, the green/blue ratio on July 14, 2004 – Typha spp., the red/green ratio on August 12, 2005 and the NIR/green ratio on June 15, 2004 These dates both show the greatest spectral separability between individual species and best correspond with the dates (month, day) of the QuickBird images available for classification Reflectance spectra of Major Species

46. 46 LiDAR Height of Each Ground Point Displayed Based on Dominant Class

47. 47 Spartina patens Phragmites australis Typha angustifolia LIDAR Height Rendering

48. 48 Spartina patens Phragmites australis Typha angustifolia LIDAR Height Rendering

49. 49 Spartina patens Phragmites australis Typha angustifolia LIDAR Height Rendering

50. 50 LIDAR Height Rendering

51. 51 Salt Marsh Classification

52. 52 Confusion matrix for QuickBird classification Reference data indicate dominant species Classified Data Reference Field Data Class P. australisTypha sp.S. patensOther/Mix TotalUsers P. australis600096987.0% Typha spp.139184215459.1% S. patens0357329262.0% Other/Mix597497070.0% Total7810372133385 Producers76.9%88.3%79.2%37.1% Overall66.8% Kappa0.56

53. 53 Confusion matrix for QuickBird classification Reference data indicate presence of species Classified Data Reference Field Data Class P. australisTypha sp.S. patensOther/Mix TotalUsers P. australis67 0026995.1% Typha spp.911872015476.6% S. patens008579292.4% Other/Mix597497070.0% Total811279978385 Producers82.7%92.9%85.9%62.8% Overall82.9% Kappa0.77

54. 54 Salt Marsh Classification

55. 55Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

56. 56 The dominant marsh species, Spartina patens, Phragmites australis and Typha spp., have been found to be separable based on their individual spectral and structural characteristics that vary over the growing season. P. australis is found to be most distinct from other species in late summer and S. patens and Typha spp. species most distinct in midsummer. This study demonstrates the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. Conclusions

57. 57 The dominant marsh species, Spartina patens, Phragmites australis and Typha spp., have been found to be separable based on their individual spectral and structural characteristics that vary over the growing season. P. australis is found to be most distinct from other species in late summer and S. patens and Typha spp. species most distinct in midsummer. This study demonstrates the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. Conclusions

58. 58 The dominant marsh species, Spartina patens, Phragmites australis and Typha spp., have been found to be separable based on their individual spectral and structural characteristics that vary over the growing season. P. australis is found to be most distinct from other species in late summer and S. patens and Typha spp. species most distinct in midsummer. This study demonstrates the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. Conclusions

59. 59Outline 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments 1.Salt Marshes 101 2.Objectives 3.Study Area 4.Procedures 5.Results 6.Conclusions 7.Acknowledgments

60. 60Acknowledgments

61. 61 Object-oriented classification and mapping of salt marsh vegetation using in situ radiometry and multi-seasonal, high resolution satellite remote sensing data Sandy Prisloe Emily Wilson University of Connecticut Cooperative Extension System Haddam, CT Sandy Prisloe Emily Wilson University of Connecticut Cooperative Extension System Haddam, CT Marty Gilmore (PI) Wesleyan University Earth and Environmental Sciences Middletown, CT Marty Gilmore (PI) Wesleyan University Earth and Environmental Sciences Middletown, CT Daniel Civco (PI) James Hurd University of Connecticut Natural Resource Management & Engineering, Storrs, CT Daniel Civco (PI) James Hurd University of Connecticut Natural Resource Management & Engineering, Storrs, CT Fourth International Workshop on the Analysis of Multitemporal Remote Sensing Images July 18-20, 2007 Leuven, Belgium Thank You Thank You

62. 62 Conclusions

63. 63 Conclusions

64. 64

65. 65