Skip to main content

Advertisement

Log in

Multi-temporal Sub-pixel Landsat ETM+ Classification of Isolated Wetlands in Cuyahoga County, Ohio, USA

  • Article
  • Published:
Wetlands Aims and scope Submit manuscript

Abstract

The goal of this research was to determine the utility of subpixel processing of multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) data for the identification and mapping of isolated wetlands ≥ 0.20 ha (0.50 acres) in Cuyahoga County, Ohio. Segmentation and object-oriented analysis of Landsat ETM+ was used to map forested and emergent marsh isolated wetlands in Alachua County, Florida, previously; however, the isolated wetlands in our study area lacked the well-defined, high-contrast boundaries between wetland and surrounding upland needed to make this method successful. We developed a new methodology that incorporated Landsat ETM+; a Normalized Difference Vegetation Index mask; and subpixel matched filtering–which determines the apparent abundance of wetlands at subpixel levels in the presence of spectrally-mixed, unknown background through a partial unmixing algorithm–to map > 43 km2 (16 mi2) of isolated wetlands in our 1,189 km2 (459 mi2) study area. The final overall accuracy of the classification was 92.8%, with a Kappa coefficient of 0.86; producer accuracy for isolated wetlands was 87.9% (omission error 12.1%) and user accuracy was 97.4% (commission error 2.6%). The subpixel matched filtering method used in this research appears to provide an effective means for mapping isolated wetlands ≥ 0.20 ha, especially those with boundaries that are not easily identified.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Beach D (1998) The Greater Cleveland environment book: Caring for home and bioregion. EcoCity Cleveland, Cleveland

  • Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data. In: Green RO (ed) Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. JPL Publication, Washington, pp 23–26, 95–1, v. 1

    Google Scholar 

  • Broderick K (2005) Communities in catchments: Implications for natural resource management. Geographical Research 43:286–296. doi:10.1111/j.1745-5871.2005.00328.x

    Article  Google Scholar 

  • Burne MR (2001) Massachusetts aerial photo survey of potential vernal pools. Massachusetts Division of Fisheries and Wildlife, Natural Heritage and Endangered Species Program, Westborough

  • Burne MR, Lathrop RG Jr (2008) Remote and field identification of vernal pools. In: Calhoun AJK, deMaynadier PG (eds) Science and conservation of vernal pools. CRC Press, Boca Raton, pp 55–68

    Google Scholar 

  • Calhoun AJK, deMaynadier PG (eds) (2008) Science and conservation of vernal pools in northeastern North America. CRC Press, Boca Raton

    Google Scholar 

  • Carrino-Kyker SR, Swanson AK (2007) Seasonal physiochemical characteristics of thirty northern Ohio temporary pools along gradients of GIS-delineated human land-use. Wetlands 27:749–760. doi:10.1672/0277-5212(2007)27[749:spcotn]2.0.co;2

    Article  Google Scholar 

  • Colburn EA (2004) Vernal pools: Natural history and conservation. McDonald and Woodward Publishing Company, Blacksburg

    Google Scholar 

  • Colburn EA, Weeks SC, Reed SK (2008) Diversity and ecology of vernal pool invertebrates. In: Calhoun AJK, deMaynadier PG (eds) Science and conservation of vernal pools in northeastern North America. CRC Press, Boca Raton, pp 105–126

    Google Scholar 

  • Comer P, Goodin K, Tomaino A, Hammerson G, Kittel G, Menard S, Nordman C, Pyne M, Reid M, Sneddon L, Snow K (2005) Biodiversity values of geographically isolated wetlands in the United States. NatureServe, Arlington

    Google Scholar 

  • Congalton RG, Oderwald RG, Mead RA (1983) Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing 49:1671–1678

    Google Scholar 

  • Creed IF, Sanford SE, Beall FD, Molot LA, Dillon PJ (2003) Cryptic wetlands: Integrating hidden wetlands in regression models of the export of dissolved organic carbon from forested landscapes. Hydrological Processes 17:3629–3648. doi:10.1002/hyp.1357

    Article  Google Scholar 

  • Cutko A, Rawinski TJ (2008) Flora of northeastern vernal pools. In: Calhoun AJK, deMaynadier PG (eds) Science and conservation of vernal pools in northeastern North America. CRC Press, Boca Raton, pp 71–104

    Google Scholar 

  • Davey Resource Group (2003) GIS wetlands inventory and restoration assessment, Cuyahoga River Watershed, Cuyahoga County, Ohio. Davey Resource Group, Kent

    Google Scholar 

  • Davey Resource Group (2006) GIS wetlands inventory and restoration assessment, Cuyahoga County, Ohio. Davey Resource Group, Kent

    Google Scholar 

  • Downing DM, Winer C, Wood LD (2003) Navigating through Clean Water Act jurisdiction: A legal review. Wetlands 23:475–493. doi:10.1672/0277-5212(2003)023[0475:NTCWAJ]2.0.CO;2

    Article  Google Scholar 

  • Fennessy MS, Mack JJ, Deimeke E, Sullivan MT, Bishop J, Cohen M, Micacchion M, Knapp M (2007) Assessment of wetlands in the Cuyahoga River watershed of northeast Ohio. Ohio Environmental Protection Agency, Division of Surface Water, Wetland Ecology Group, Columbus

    Google Scholar 

  • Frohn RC, Reif M, Lane C, Autrey B (2009) Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data. Wetlands 29:931–941. doi:10.1672/08-194.1

    Article  Google Scholar 

  • Frohn RC, Autrey BC, Lane CR, Reif M (2011) Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery. International Journal of Remote Sensing 32:1471–1489. doi:10.1080/01431160903559762

    Article  Google Scholar 

  • Harris JR, Rogge D, Hitchcock R, Ijewliw O, Wright D (2005) Mapping lithology in Canada’s Arctic: Application of hyperspectral data using the minimum noise fraction transformation and matched filtering. Canadian Journal of Earth Sciences 42:2173–2193. doi:10.1139/e05-064

    Article  Google Scholar 

  • Harsanyi JC, Chang CI (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing 32:779–785. doi:10.1109/36.298007

    Article  Google Scholar 

  • Heinz DC, Chang C-I (2001) Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39:529–545. doi:10.1109/36.911111

    Article  Google Scholar 

  • Hu YH, Lee HB, Scarpace FL (1999) Optimal linear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing 37:639–644. doi:10.1109/36.739139

    Article  Google Scholar 

  • Huguenin RL, Karaska MA, Van Blaricom D, Jensen JR (1997) Subpixel classification of bald cypress and tupelo gum trees in Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing 63:717–725

    Google Scholar 

  • Hui Y, Rongqun Z, Xianwen L (2009) Classification of wetland from TM imageries based on decision tree. WSEAS Transactions on Information Science and Applications 6:1155–1164

    Google Scholar 

  • Hutton SM, Dincer T (1979) Using Landsat imagery to study the Okavango Swamp, Botswana. In: Deutsch M, DR Wiesnet, A Rango (eds) Proceedings of the Fifth Annual William T. Pecora Memorial Symposium on Remote Sensing, Sioux Falls, SD. pp 512–519

  • Jensen JR (2004) Introductory digital image processing: A remote sensing perspective, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Jensen JR, Christensen EJ, Sharitz R (1984) Nontidal wetland mapping in South Carolina using airborne multispectral scanner data. Remote Sensing of Environment 16:1–12. doi:10.1016/0034-4257(84)90023-3

    Article  Google Scholar 

  • Kettlewell CI, Bouchard V, Porej D, Micacchion M, Mack JJ, White D, Fay L (2008) An assessment of wetland impacts and compensatory mitigation in the Cuyahoga River Watershed, Ohio, USA. Wetlands 28:57–67. doi:10.1672/07-01.1

    Article  Google Scholar 

  • Kuenzer C, Bachmann M, Mueller A, Lieckfeld L, Wagner W (2008) Partial unmixing as a tool for single surface class detection and time series analysis. International Journal of Remote Sensing 29:3233–3255. doi:10.1080/01431160701469107

    Article  Google Scholar 

  • Laben CA, Brower BV (2000) Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. Eastman Kodak Company, Rochester, patent no. 6011875. http://www.freepatentsonline.com/6011875.html

  • Lane CR, D’Amico E (2010) Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in north central Florida, USA. Wetlands 30:967–977. doi:10.1007/s13157-010-0085-z

    Article  Google Scholar 

  • Lathrop RG, Montesano P, Tesauro J, Zarate B (2005) Statewide mapping and assessment of vernal pools: A New Jersey case study. Journal of Environmental Management 76:230–238. doi:10.1016/j.jenvman.2005.02.006

    Article  PubMed  Google Scholar 

  • Leibowitz SG (2003) Isolated wetlands and their functions: An ecological perspective. Wetlands 23:517–531. doi:10.1672/0277-5212(2003)023[0517:IWATFA]2.0.CO;2

    Article  Google Scholar 

  • McKinney RA, Charpentier MA (2009) Extent, properties, and landscape setting of geographically isolated wetlands in urban southern New England watersheds. Wetlands Ecology and Management 17:331–344. doi:10.1007/s11273-008-9110-x

    Article  Google Scholar 

  • Melesse AM, Jordan JD, Graham WD (2001) Enhancing land cover mapping using Landsat derived surface temperature and NDVI. In: Phelps D, G Shelke (eds) Proceedings of the World Water and Environmental Resources Congress, Orlando, Florida. p 439

  • Mitchell JJ, Glenn NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.). International Journal of Remote Sensing 30:6099–6119. doi:10.1080/01431160902810620

    Article  Google Scholar 

  • Mitsch WJ, Gosselink JG (2000) Wetlands, 3rd edn. John Wiley and Sons, New York

    Google Scholar 

  • Mundt JT, Streutker DR, Glenn NF (2007) Partial unmixing of hyperspectral imagery: theory and methods. Proceedings of the American Society of Photogrammetry and Remote Sensing, Tampa, Florida

  • Munoz B, Lesser VM, Dorney JR, Savage R (2009) A proposed methodology to determine accuracy of location and extent of geographically isolated wetlands. Environmental Monitoring and Assessment 150:53–64. doi:10.1007/s10661-008-0672-0

    Article  PubMed  Google Scholar 

  • Narumalani S, Jensen JR, Althausen JD, Burkhalter S, Mackey HE Jr (1997) Aquatic macrophyte modeling using GIS and logistic multiple regression. Photogrammetric Engineering and Remote Sensing 63:41–49

    Google Scholar 

  • Nielsen AA (2001) Spectral mixture analysis: Linear and semi-parametric full and iterated partial unmixing in multi- and hyperspectral image data. International Journal of Computer Vision 42:17–37. doi:10.1023/a:1011181216297

    Article  Google Scholar 

  • Oki K, Oguma H, Sugita M (2002) Subpixel classification of alder trees using multitemporal Landsat Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing 68:77–82

    Google Scholar 

  • Omernik JM (1987) Ecoregions of the conterminous United States. Annals of the Association of American Geographers 77:118–125

    Article  Google Scholar 

  • Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetlands Ecology and Management 10:381–402. doi:10.1023/A:1020908432489

    Article  Google Scholar 

  • Reif M, Frohn RC, Lane CR, Autrey B (2009) Mapping isolated wetlands in a karst landscape: GIS and remote sensing methods. GIScience and Remote Sensing 46:187–211. doi:10.2747/1548-1603.46.2.187

    Article  Google Scholar 

  • Research Systems Inc. (2007) ENVI Version 4.4 User’s Guide. ITT Visual Information Solutions, Boulder

    Google Scholar 

  • Semlitsch RD, Bodie JR (1998) Are small, isolated wetlands expendable? Conservation Biology 12:1129–1133. doi:10.1046/j.1523-1739.1998.98166.x

    Article  Google Scholar 

  • Semlitsch RD, Skelly DK (2008) Ecology and conservation of pond-breeding amphibians. In: Calhoun AJK, deMaynadier PG (eds) Science and conservation of vernal pools in northeastern North America. CRC Press, Boca Raton, pp 127–148

    Google Scholar 

  • Settle J (2002) On constrained energy minimization and the partial unmixing of multispectral images. IEEE Transactions on Geoscience and Remote Sensing 40:718–721. doi:10.1109/TGRS.2002.1000332

    Article  Google Scholar 

  • Shanmugam P, Ahn Y, Sanjeevi S (2006) A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India. Ecological Modelling 194:379–394. doi:10.1016/j.ecolmodel.2005.10.033

    Article  Google Scholar 

  • Skerl KL (2001) Implementing wetland protection for agricultural lands in Cuyahoga Valley National Park, Ohio. In: Harmon D (ed) Crossing Boundaries in Park Management: Proceedings of the 11th Conference on Research and Resource Management in Parks and on Public Lands, Hancock, MI. pp 375–381

  • Stankiewicz K, Dabrowska-Zielinska K, Gruszczynska M, Hoscilo A (2003) Mapping vegetation of a wetland ecosystem by fuzzy classification of optical and microwave satellite images supported by various ancillary data. In: Owe M, G D’Urso, L Toulios (eds) Proceedings of the SPIE, Remote Sensing for Agriculture, Ecosystems, and Hydrology IV, pp 352–361

  • Story M, Congalton R (1986) Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing 52:397–399

    Google Scholar 

  • Tamura M, Oguma H, Yasuoka Y (1998) Measurements of vegetation reflectance using a 2-D imaging spectrometer. Proceedings of the 25th Conference of the Remote Sensing Society, pp 223–224

  • Tiner RW (2003a) Estimated extent of geographically isolated wetlands in selected areas of the United States. Wetlands 23:636–652. doi:10.1672/0277-5212(2003)023[0636:EEOGIW]2.0.CO;2

    Article  Google Scholar 

  • Tiner RW (2003b) Geographically isolated wetlands of the United States. Wetlands 23:494–516. doi:10.1672/0277-5212(2003)023[0494:GIWOTU]2.0.CO;2

    Article  Google Scholar 

  • Tiner RW, Bergquist HC, DeAlessio GP, Starr MJ (2002) Geographically isolated wetlands: A preliminary assessment of their characteristics and status in selected areas of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Northeast Region, Hadley

  • Töyrä J, Pietroniro A (2005) Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sensing of Environment 97:174–191. doi:10.1016/j.rse.2005.03.012

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:127–150. doi:10.1016/0034-4257(79)90013-0

    Article  Google Scholar 

  • Turin G (2002) An introduction to matched filters. IRE Transactions on Information Theory 6:311–329. doi:10.1109/TIT.1960.1057571

    Article  Google Scholar 

  • Wang J, Lang PA (2009) Detection of cypress canopies in the Florida Panhandle using subpixel analysis and GIS. Remote Sensing 1:1028–1042. doi:10.3390/rs1041028

    Article  Google Scholar 

  • Wei S-C, Wu C-F, Yang Y, Huang M-Y, Yang Z-R (2008) Land use change and ecological security in Yellow River Delta based on RS and GIS technology - A case study of Dongying city. Journal of Soil and Water Conservation 22:185–189, doi: CNKI:SUN:TRQS.0.2008-01-038

    Google Scholar 

  • Whigham DF, Jordan TE (2003) Isolated wetlands and water quality. Wetlands 23:541–549. doi:10.1672/0277-5212(2003)023[0541:IWAWQ]2.0.CO;2

    Article  Google Scholar 

  • White D, Fennessy S (2005) Modeling the suitability of wetland restoration potential at the watershed scale. Ecological Engineering 24:359–377. doi:10.1016/j.ecoleng.2005.01.012

    Article  Google Scholar 

  • Williams AP, Hunt ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sensing of Environment 82:446–456. doi:10.1016/S0034-4257(02)00061-5

    Article  Google Scholar 

  • Yavitt JB (2010) Biogeochemistry: Cryptic wetlands. Nature Geoscience 3:749–750

    Article  CAS  Google Scholar 

  • Zhao B, Yan Y, Guo HQ, He MM, Gu YJ, Li B (2009) Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area. Ecological Indicators 9:346–356. doi:10.1016/j.ecolind.2008.05.009

    Article  Google Scholar 

Download references

Acknowledgments

The United States Environmental Protection Agency through its Office of Research and Development partially funded and collaborated in the research described here under contract EP-D-06-096 to Dynamac Corporation. This paper has been subjected to Agency review and approved for publication. The views expressed herein are those of the authors and do not necessarily reflect the views and policies of the United States Environmental Protection Agency. We received the DaRG dataset from the Cuyahoga River Community Planning Organization (Marie Sullivan, CRCPO GIS Expert, personal communication, August 2008), and Kevin Skerl provided the data from Cuyahoga Valley National Park. We appreciate the review and insightful comments provided by Ken Bailey, US EPA, and editing and formatting provided by Justicia Rhodus, Dynamac Corporation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charles Lane.

Additional information

Dr. Robert Frohn passed away on October 16, 2010.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Frohn, R.C., D’Amico, E., Lane, C. et al. Multi-temporal Sub-pixel Landsat ETM+ Classification of Isolated Wetlands in Cuyahoga County, Ohio, USA. Wetlands 32, 289–299 (2012). https://doi.org/10.1007/s13157-011-0254-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13157-011-0254-8

Keywords

Navigation