Wetlands Ecology and Management

, Volume 19, Issue 3, pp 223–236

Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay, Canada

  • Daniel Rokitnicki-Wojcik
  • Anhua Wei
  • Patricia Chow-Fraser
Original Paper

Abstract

Coastal wetlands of eastern and northern Georgian Bay, Canada provide critical habitat for a variety of biota yet few have been delineated and mapped because of their widespread distribution and remoteness. This is an impediment to conservation efforts aimed at identifying significant habitat in the Laurentian Great Lakes. We propose to address this deficiency by developing an approach that relies on use of high-resolution remote sensing imagery to map wetland habitat. In this study, we use IKONOS satellite imagery to classify coastal high marsh vegetation (seasonally inundated) and assess the transferability of object-based rule sets among different regions in eastern Georgian Bay. We classified 24 wetlands in three separate satellite scenes and developed an object-based approach to map four habitat classes: emergent, meadow/shrub, senescent vegetation and rock. Independent rule sets were created for each scene and applied to the other images to empirically examine transferability at broad spatial scales. For a given habitat feature, the internally derived rule sets based on field data collected from the same scene provided significantly greater accuracy than those derived from a different scene (80.0 and 74.3%, respectively). Although we present a significant effect of ruleset origin on accuracy, the difference in accuracy is minimal at 5.7%. We argue that this should not detract from its transferability on a regional scale. We conclude that locally derived and object-based rule sets developed from IKONOS imagery can successfully classify complex vegetation classes and be applied to different regions without much loss of accuracy. This indicates that large–scale mapping automation may be feasible with images with similar spectral, spatial, contextual, and textural properties.

Keywords

Transferability Object-based rule sets Habitat Wetland Landscape Georgian Bay IKONOS 

References

  1. Baker C, Lawrence R, Montagne C, Patten D (2006) Mapping wetlands and riparian areas using LANDSAT ETM imagery and decision-tree-based models. Wetlands 26:465–474. doi:10.1672/0277-5212(2006)26[465:MWARAU]2.0.CO;2 CrossRefGoogle Scholar
  2. Belluco E, Camuffo M, Ferrari S, Modenese L, Silvestri S, Marani A, Marani M (2006) Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sens Environ 105:54–67. doi:10.1016/j.rse.2006.06.006 CrossRefGoogle Scholar
  3. Chow-Fraser P (2006) Development of the wetland water quality index for assessing the quality of Great Lakes coastal wetlands. In: Simon TP, Stewart PM (eds) Coastal wetlands of the Laurentian Great lakes: health Habitat and indicators. Indiana Biological Survey, Bloomington, pp 137–166Google Scholar
  4. Chubey MS, Franklin SE, Wulder MA (2006) Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogramm Eng Remote Sens 72:383–394Google Scholar
  5. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46. doi:10.1016/0034-4257(91)90048-B CrossRefGoogle Scholar
  6. Croft MV, Chow-Fraser P (2009) Non-random sampling and its role in habitat conservation: a comparison of three wetland macrophyte sampling protocols. Biodivers Conserv 18:2283–2306. doi:10.1007/s10531-009-9588-4 CrossRefGoogle Scholar
  7. Croft MV, Chow-Fraster P (2007) Use and development of the wetland macrophyte index to detect water quality impairment in fish habitat of Great Lakes coastal marshes. J Great Lakes Res 33:172–197. doi:10.3394/0380-1330(2007)33[172:UADOTW]2.0.CO;2 CrossRefGoogle Scholar
  8. DeCatanzaro R, Cvetkovic M, Chow-Fraser P (2009) The relative importance of road density and physical watershed features in determining coastal marsh water quality in Georgian Bay. Environ Manag 44:456–567. doi:10.1007/s00267-009-9338-0 CrossRefGoogle Scholar
  9. Dechka JA, Franklin SE, Watmough MD, Bennett RP, Ingstrup DW (2002) Classification of wetland habitat and vegetation communities using multi-temporal IKONOS imagery in southern Saskatchewan. Can J Remote Sens 28:679–685CrossRefGoogle Scholar
  10. Definiens AG (2007) Definiens Developer 7 Reference Book. Definiens AG, MünchenGoogle Scholar
  11. DFO (2010) Historical monthly and yearly mean water level graphs 1918–2009. Canadian Hydrographic Services (Department of Fisheries and Oceans). http://www.waterlevels.gc.ca/C&A/netgraphs_e.html. Accessed 01 Apr 2010
  12. Dillabaugh KA, King DJ (2008) Riparian marshland composition and biomass mapping using Ikonos imagery. Can J Remote Sens 34:143–158CrossRefGoogle Scholar
  13. Flanders D, Hall-Beyer M, Pereverzoff J (2003) Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Can J Remote Sens 29:441–452CrossRefGoogle Scholar
  14. Fournier RA, Grenier M, Lavoie A, Hélie R (2007) Towards a strategy to implement the Canadian Wetland Inventory using satellite remote sensing. Can J Remote Sens 33:S1–S16CrossRefGoogle Scholar
  15. Fuller LM, Morgan TR, Aichele SS (2006) Wetland delineation with IKONOS high-resolution satellite imagery, Fort Custer Training Center, Battle Creek, Michigan, 2005. U.S. Geological Survey, Scientific Investigations Report 2006-5051Google Scholar
  16. Ghioca-Robrecht DM, Johnston CA, Tulbure MG (2008) Assessing the use if multiseason Quickbird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands 28:1028–1039. doi:10.1672/08-34.1 CrossRefGoogle Scholar
  17. Gluck M, Rempel R, Uhlig PWC (1996) An evaluation of remote sensing for regional wetland mapping applications. Forest Research Report No. 137 Ontario Research Institute, Sault Ste Marie, OntarioGoogle Scholar
  18. Grenier M, Demers AM, Labreque S, Benoit M, Fournier RA, Drolet B (2007) An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec, Canada. Can J Remote Sens 33:28–45CrossRefGoogle Scholar
  19. Grenier M, Labreque S, Garneau M, Tremblay A (2008) Object-based classification of a SPOT-4 image for mapping wetlands in the context of greenhouse gases emissions: the case of the Eastmain region, Quebec, Canada. Can J Remote Sens 34:398–413CrossRefGoogle Scholar
  20. Ingram J, Holmes K, Grabas G, Watton P, Potter B, Gomer T, Stow N (2004) Development of a Coastal Wetlands Database for the Great Lakes Canadian Shoreline. Final Report to the Great Lakes CommissionGoogle Scholar
  21. Jensen JR, Cowen D, Althausen J, Narumalani S, Weatherbee O (1993) An evaluation of the Coast Watch change detection protocol in South Carolina. Photogramm Eng Remote Sens 59:1039–1046Google Scholar
  22. Keddy PA, Reznicek AA (1986) Great Lakes vegetation dynamics: the role of fluctuating water levels and buried seeds. J Great Lakes Res 12:25–36. doi:10.1016/S0380-1330(86)71697-3 CrossRefGoogle Scholar
  23. Laliberte AS, Rango A, Havstad KM, Paris JF, Beck RF, McNeely R, Gonzalez AL (2004) Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sens Environ 93:198–210. doi:10.1016/j.rse.2004.07.011 CrossRefGoogle Scholar
  24. Lawrence R, Hurst R, Weaver T, Aspinall R (2006) Mapping prarie pothole communities with multitemporal Ikonos satellite imagery. Photogramm Eng Remote Sens 72:169–174Google Scholar
  25. Lillesand TM, Kiefer RW (2004) Remote sensing and image interpretation. Wiley, New YorkGoogle Scholar
  26. Maxa M, Bolstad P (2009) Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands 29:248–260. doi:10.1672/08-91.1 CrossRefGoogle Scholar
  27. Maynard L, Wilcox D (1997) Coastal wetlands of the Great Lakes. State of the Lakes Ecosystem Conference 1996. U.S. EPAGoogle Scholar
  28. Midwood JM, Chow-Fraser P (2010) Mapping floating and emergent aquatic vegetation in coastal wetlands of eastern Georgian Bay, Lake Huron, Canada. Wetlands 30:1141–1152. doi:10.1007/s13157-010-0105-z CrossRefGoogle Scholar
  29. Mitsch MJ, Gosslink JG (2000) Wetlands. Wiley, New YorkGoogle Scholar
  30. Navulur K (2006) Multispectral image analysis using the object-oriented paradigm. CRC Press, New YorkCrossRefGoogle Scholar
  31. OMNR (1993) Ontario Wetland Evaluation System. Northern Manual. Ontario Ministry of Natural Resources (OMNR), No. 50254Google Scholar
  32. Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetl Ecol Manag 10:381–402. doi:10.1023/A:1020908432489 CrossRefGoogle Scholar
  33. Poulin M, Careau D, Rochefort L, Desrochers A (2002) From satellite imagery to peatland vegetation diversity: how reliable are habitat maps? Conserv Ecol 6:16–56Google Scholar
  34. Sawaya KE, Olmanson LG, Hein NJ, Brezonic PL, Bauer ME (2003) Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sens Environ 88:144–156. doi:10.1016/j.rse.2003.04.006 CrossRefGoogle Scholar
  35. Sly PG, Munawar M (1988) Great Lake Manitoulin: Georgian Bay and the North Channel. Hydrobiologia 163:1–19. doi:10.1007/BF00026917 CrossRefGoogle Scholar
  36. Snell EA (1987) Wetland Distribution and Conversion in Southern Ontario. Canada Land Use Monitoring Program. Working Paper No. 48. Inland Waters and Lands Directorate, Environment CanadaGoogle Scholar
  37. Story M, Congalton R (1986) Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sens 52:397–399Google Scholar
  38. Töyrä J, Pietroniro A, Martz LW (2001) Multisensor hydrologic assessment of a freshwater wetland. Remote Sens Environ 75:162–173. doi:10.1016/S0034-4257(00)00164-4 CrossRefGoogle Scholar
  39. Wang L, Sousa WP, Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens 25:5655–5668. doi:10.1080/014311602331291215 CrossRefGoogle Scholar
  40. Wei A, Chow-Fraser P (2007) Use of IKONOS imagery to map coastal wetlands of Georgian Bay. Fisheries 32:167–173. doi:10.1577/1548-8446(2007)32[167:UOIITM]2.0.CO;2 CrossRefGoogle Scholar
  41. Wei A, Chow-Fraser P (2008) Testing the transferability of a marsh-inundation model across two landscapes. Hydrobiologia 600:41–47. doi:10.1007/s10750-007-9174-2 CrossRefGoogle Scholar
  42. Wolter PT, Johnston CA, Niemi GJ (2005) Mapping submergent aquatic vegetation in the US Great Lakes using QuickBird data. Int J Remote Sens 26:5255–5274. doi:10.1080/01431160500219208 CrossRefGoogle Scholar
  43. Wulder MA, Hall RJ, Coops NC, Franklin SE (2004) High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54:511–522. doi:10.1641/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2 CrossRefGoogle Scholar
  44. Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm Eng Remote Sens 72:799–811Google Scholar
  45. Zhou W, Troy A, Grove M (2008) Object-based land cover classification and change analysis in the baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 8:1613–1636. doi:10.3390/s8031613 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Daniel Rokitnicki-Wojcik
    • 1
  • Anhua Wei
    • 1
  • Patricia Chow-Fraser
    • 1
  1. 1.Department of BiologyMcMaster UniversityHamiltonCanada

Personalised recommendations