[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 137–166

    Google Scholar 

  • Chubey MS, Franklin SE, Wulder MA (2006) Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogramm Eng Remote Sens 72:383–394

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 28:679–685

    Article  Google Scholar 

  • Definiens AG (2007) Definiens Developer 7 Reference Book. Definiens AG, München

    Google Scholar 

  • 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

  • Dillabaugh KA, King DJ (2008) Riparian marshland composition and biomass mapping using Ikonos imagery. Can J Remote Sens 34:143–158

    Article  Google Scholar 

  • 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 29:441–452

    Article  Google Scholar 

  • 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–S16

    Article  Google Scholar 

  • 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 2006-5051

  • 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

    Article  Google Scholar 

  • 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, Ontario

  • 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 33:28–45

    Article  Google Scholar 

  • 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 34:398–413

    Article  Google Scholar 

  • 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 Commission

  • 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 59:1039–1046

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Lawrence R, Hurst R, Weaver T, Aspinall R (2006) Mapping prarie pothole communities with multitemporal Ikonos satellite imagery. Photogramm Eng Remote Sens 72:169–174

    Google Scholar 

  • Lillesand TM, Kiefer RW (2004) Remote sensing and image interpretation. Wiley, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Maynard L, Wilcox D (1997) Coastal wetlands of the Great Lakes. State of the Lakes Ecosystem Conference 1996. U.S. EPA

  • 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

    Article  Google Scholar 

  • Mitsch MJ, Gosslink JG (2000) Wetlands. Wiley, New York

    Google Scholar 

  • Navulur K (2006) Multispectral image analysis using the object-oriented paradigm. CRC Press, New York

    Book  Google Scholar 

  • OMNR (1993) Ontario Wetland Evaluation System. Northern Manual. Ontario Ministry of Natural Resources (OMNR), No. 50254

  • Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetl Ecol Manag 10:381–402. doi:10.1023/A:1020908432489

    Article  Google Scholar 

  • Poulin M, Careau D, Rochefort L, Desrochers A (2002) From satellite imagery to peatland vegetation diversity: how reliable are habitat maps? Conserv Ecol 6:16–56

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Sly PG, Munawar M (1988) Great Lake Manitoulin: Georgian Bay and the North Channel. Hydrobiologia 163:1–19. doi:10.1007/BF00026917

    Article  CAS  Google Scholar 

  • 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 Canada

  • Story M, Congalton R (1986) Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sens 52:397–399

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 72:799–811

    Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by an NSERC PGS-M grant awarded to DR-W and a research grant from the GBA Foundation to PC-F. We thank everyone who helped in collecting field data and in mapping especially, R. de Catanzaro, M. Cvetkovic, K. Cimaroli, B. Gidley, A. Stevens, L. Smith, S. Yantsis, M. Crouch, and G. Honsberger and anonymous reviewers. Technical advice from J. Midwood was greatly appreciated. We would like to thank the Georgian Bay Forever for the use of the IKONOS imagery and the Georgian Bay Association, especially Mary Muter, for helping with logistics during field sampling.

Author information

Authors and Affiliations

  1. Department of Biology, McMaster University, 1280 Main St. W, Hamilton, ON, L8S 4K1, Canada

    Daniel Rokitnicki-Wojcik, Anhua Wei & Patricia Chow-Fraser

Authors
  1. Daniel Rokitnicki-Wojcik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anhua Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patricia Chow-Fraser
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Rokitnicki-Wojcik.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rokitnicki-Wojcik, D., Wei, A. & Chow-Fraser, P. Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay, Canada. Wetlands Ecol Manage 19, 223–236 (2011). https://doi.org/10.1007/s11273-011-9213-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-011-9213-7

Keywords