Wetlands Ecology and Management

, Volume 10, Issue 5, pp 381–402 | Cite as

Satellite remote sensing of wetlands

  • Stacy L. Ozesmi
  • Marvin E. Bauer
Article

Abstract

To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification.

Classification techniques Comparison of methods Remote sensing Satellite imagery Wetland classification Wetland identification Wetlands inventory Wetlands 

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References

  1. Ackleson S.G.and Klemas V. 1987. Remote sensing of submerged aquatic vegetation in Lower Chesapeake Bay: a comparison of Landsat MSS to TM imagery. Remote Sensing of Environment 22: 235–248.CrossRefGoogle Scholar
  2. Ackleson S.G., Klemas V., McKim H.L. and Merry C.J. 1985. A comparison of SPOT simulator data with Landsat MSS imagery for delineating water masses in Delaware Bay, Broadkill River, and adjacent wetlands. Photogrammetric Engineering and Remote Sensing 51: 1123–1129.Google Scholar
  3. Anderson J.E. and Perry J.E. 1996. Characterization of wetland plant stress using leaf spectral reflectance: implications for wetland remote sensing. Wetlands 16: 477–487.CrossRefGoogle Scholar
  4. Barbier E.B., Burgess J.C. and Folke C. 1994. Paradise Lost? The Ecological Economics of Biodiversity. Earthscan, London, 267 pp.Google Scholar
  5. Bartlett D. and Klemas V. 1980. Quantitative assessment of tidal wetlands using remote sensing. Environmental Management 4: 337–345.CrossRefGoogle Scholar
  6. Bartlett D.S. and Klemas V. 1981. In situ spectral reflectance studies of tidal wetland grasses. Photogrammetric Engineering and Remote Sensing 47: 1695–1703.Google Scholar
  7. Best R.G. and Moore D.G. 1979. Landsat interpretation of prairie lakes and wetlands of eastern South Dakota. In: Deutsch M., Wiesnet D.R. and Rango A. (eds), Fifth Annual William T. Pecora Memorial Symposium on Remote Sensing 1979. American Water Resources Association, Souix Falls, South Dakota, USA, pp. 499–506.Google Scholar
  8. Best R.G., Wehde M.E. and Linder R.L. 1981. Spectral reflectance of hydrophytes. Remote Sensing of Environment 11: 27–35.CrossRefGoogle Scholar
  9. Bolstad P.V. and Lillesand T.M. 1992. Rule-based classification models: flexible integration of satellite imagery and thematic spatial data. Photogrammetric Engineering and Remote Sensing 58: 965–971.Google Scholar
  10. Budd J.T.C. and Milton E.J. 1982. Remote sensing of salt marsh vegetation in the first four proposed thematic mapper bands. International Journal of Remote Sensing 44: 303–314.Google Scholar
  11. Butera M.K. 1983. Remote sensing of wetlands. IEEE Transactions on Geoscience and Remote Sensing GE-21: 383–392.Google Scholar
  12. Cairns S.H., Dickson K.L. and Atkinson S.F. 1997. An examination of measuring selected water quality trophic indicators with SPOT satellite HRV data. Photogrammetric Engineering and Remote Sensing 63: 263–265.Google Scholar
  13. Carter V., Garrett M.K., Shima L. and Gammon P. 1977. The Great Dismal Swamp: management of a hydrologic resource with the aid of remote sensing. Water Resources Bulletin 13: 1–12.CrossRefGoogle Scholar
  14. Chopra R., Verma V.K., and Sharma P.K. 2001. Mapping, monitoring and conservation of Harike wetland ecosystem, Punjab, India, through remote sensing. International Journal of Remote Sensing 22: 89–98.CrossRefGoogle Scholar
  15. Coppin P.R. and Bauer M.E. 1994. Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features. IEEE Transactions on Geoscience and Remote Sensing 32: 918–927.CrossRefGoogle Scholar
  16. Daily G.C. (ed.) 1997. Nature's Services: Societal Dependence on Natural Ecosystems. Island Press, Washington, 329 pp.Google Scholar
  17. Dobson J.E., Bright E.A., Ferguson R.L., Field D.W., Wood L.L., Haddad K.D. et al. et al. 1995. NOAA Coastal Change Analysis Program (C-CAP). NOAA technical report NMFS 123. US Department of Commerce, Seattle, Washington, USA, 92 pp.Google Scholar
  18. Doren R.F., Rutchey K. and Welch R. 1999. The Everglades: a perspective on the requirements and applications for vegetation map and database products. Photogrammetric Engineering and Remote Sensing 62: 155–161.Google Scholar
  19. Ernst C.L. and Hoffer R.M. 1979. Digital processing of remotely sensed data for mapping wetland communities. LARS Technical Report 122079. Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, 119 pp.Google Scholar
  20. Ernst-Dottavio C.L., Hoffer R.M. and Mroczynski R.P. 1981. Spectral characteristics of wetland habitats. Photogrammetric Engineering and Remote Sensing 47: 223–227.Google Scholar
  21. FGDC 1992. Application of satellite data for mapping and moni toring wetlands. Technical Report 1, Wetlands Subcommittee. Federal Geographic Data Committee (FGDC), Washington, DC, USA, 32 pp.Google Scholar
  22. Forgette T.A. and Shuey J.A. 1997. A comparison of wetland mapping using SPOT satellite imagery and national wetland inventory data for a watershed in northern Michigan. In: Trettin C.C. (ed.), Northern Forested Wetlands; Ecology and Management. CRC Lewis Publishers, Boca Raton, Florida, USA, pp. 61–70.Google Scholar
  23. Franklin S.E., Gillespie R.T., Titus B.D. and Pike D.B. 1994. Aerial and satellite sensor detection of Kalmia angustifolia at forest regeneration sites in central Newfoundland. International Journal of Remote Sensing 15: 2553–2557.CrossRefGoogle Scholar
  24. Fuller R.M., Groom G.B., Mugisha S., Ipulet P., Pomeroy D., Katende A. et al. 1998. The integration of field survey and remote sensing for biodiversity assessment: a case study in the tropical forests and wetlands of Sango Bay, Uganda. Biological Conservation 86: 379–391.CrossRefGoogle Scholar
  25. Gammon P.T., Rohde W.G. and Carter V. 1979. Accuracy evaluation of Landsat digital classification of vegetation in the Great Dismal Swamp. In: Deutsch M., Wiesnet D.R. and Rango A. (eds), Fifth Annual William T. Pecora Memorial Symposium on Remote Sensing 1979. American Water Resources Association, Souix Falls, South Dakota, pp. 463–473.Google Scholar
  26. Gilmer D.S., Work E.A. Jr, Colwell J.E. and Rebel D.L. 1980. Enumeration of prairie wetlands with Landsat and aircraft data. Photogrammetric Engineering and Remote Sensing 46: 631–634.Google Scholar
  27. Gluck M., Rempel R. and Uhlig P.W.C. 1996. An evaluation of remote sensing for regional wetland mapping applications. Forest Research Report No. 137. Ontario Forest Research Institute, Sault Ste Marie, Ontario, Canada, 33 pp.Google Scholar
  28. Gomarasca M.A., Lozano-Garcia D.F., Fernandez R.N. and Johannsen C.J. 1992. Analysis of seasonal variation in the Niger river interior delta using satellite data. Geocarto International 7: 61–73.CrossRefGoogle Scholar
  29. Goodin D.G. 1995. Mapping the surface radiation budget and net radiation in a Sand Hills wetland using a combined modeling/ remote sensing method and Landsat thematic mapper imagery. Geocarto International 10: 19–29.CrossRefGoogle Scholar
  30. Gross M.F., Hardisky M.A. and Klemas V. 1988. Effects of solar angle on reflectance from wetland vegetation. Remote Sensing of Environment 26: 195–212.CrossRefGoogle Scholar
  31. Gross M.F., Hardisky M.A., Klemas V. and Wolf P.L. 1987. Quantification of biomass of the marsh grass Spartina alterniflora Louisel using Landsat Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing 53: 1577–1583.Google Scholar
  32. Gross M.F., Hardisky M.A., Wolf P.L. and Klemas V. 1993. Relationships among Typha biomass, pore water methane, and reflectance in a Delaware (USA) brackish marsh. Journal of Coastal Research 9: 339–355.Google Scholar
  33. Haack B. 1996. Monitoring wetland changes with remote sensing: an east African example. Environmental Management 20: 411–419.PubMedCrossRefGoogle Scholar
  34. Haack B. and Messina J. 1997. Monitoring the Omo River delta in East Africa using remote sensing. Earth Observation Magazine 6: 18–22.Google Scholar
  35. Hardisky M.A., Gross M.F. and Klemas V. 1986. Remote sensing of coastal wetlands. BioScience 36: 453–460.CrossRefGoogle Scholar
  36. Hardisky M.A., Smart R.M. and Klemas V. 1983. Seasonal spectral characteristics and aboveground biomass of the tidal marsh plant, Spartina alterniflora. Photogrammetric Engineering and Remote Sensing 49: 85–92.Google Scholar
  37. Harper J. and Ross G.A. 1982. Digital analysis of Landsat data in the Athabasca Delta. In: Foreman J. (ed.), International Society for Photogrammetry and Remote Sensing IV 1982. American Society of Photogrammetry and American Congress on Surveying and Mapping, Crystal City, Virginia, USA, pp. 319–327.Google Scholar
  38. Hess L.L., Melack J.M. and Simonett D.S. 1990. Radar detection of flooding beneath the forest canopy: a review. International Journal of Remote Sensing 11: 1313–1325.CrossRefGoogle Scholar
  39. Hewitt M.J. III 1990. Synoptic inventory of riparian ecosystems: the utility of Landsat Thematic Mapper data. Forest Ecology and Management 33/34: 605–620.CrossRefGoogle Scholar
  40. Hines M.E., Pelletier R.E. and Crill P.M. 1993. Emission of sulfur gases from marine and freshwater wetlands of the Florida Everglades: rates and extrapolation using remote sensing. Journal of Geophysical Research 98: 8991–8999.CrossRefGoogle Scholar
  41. Hinson J.M., German C.D. and Pulich W. Jr 1994. Accuracy assessment and validation of classified satellite imagery of Texas coastal wetlands. Marine Technology Society Journal 28: 4–9.Google Scholar
  42. Hodgson M.E., Jensen J.R., Mackey H.E. Jr and Coulter M.C. 1987. Remote sensing of wetland habitat: a wood stork example. Photogrammetric Engineering and Remote Sensing 53: 1075–1080.Google Scholar
  43. Houhoulis P.F. and Michener W.K. 2000. Detecting wetland change: a rule-based approach using NWI and SPOT-XS data. Photogrammetric Engineering and Remote Sensing 66: 205–211.Google Scholar
  44. Huguenin R.L., Karaska M.A., Blaricom D.V. and Jensen J.R. 1997. Subpixel classification of bald cypress and tupelo gum trees in Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing 63: 717–725.Google Scholar
  45. Hutton S.M. and Dincer T. 1979. Using Landsat imagery to study the Okavango Swamp, Botswana. In: Deutsch M., Wiesnet D.R. and Rango A. (eds), Fifth Annual William T. Pecora Memorial Symposium on Remote Sensing 1979. American Water Resources Association, Souix Falls, South Dakota, pp. 512–519.Google Scholar
  46. Jensen J.R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd edn. Prentice-Hall, Upper Saddle River, NJ, USA, 318 pp.Google Scholar
  47. Jensen J.R., Christensen E.J. and Sharitz R. 1984. Nontidal wetland mapping in South Carolina using airborne multi-spectral scanner data. Remote Sensing of Environment 16: 1–12.CrossRefGoogle Scholar
  48. Jensen J.R., Cowen D., Althausen J., Narumalani S. and Weatherbee O. 1993a. An evaluation of the Coast Watch change detection protocol in South Carolina. Photogrammetric Engineering and Remote Sensing 59: 1039–1046.Google Scholar
  49. Jensen J.R., Cowen D.J., Althausen J.D., Narumalani S. and Weatherbee O. 1993b. The detection and prediction of sea level changes on coastal wetlands using satellite imagery and a geographic information system. Geocarto International 4: 87–98.CrossRefGoogle Scholar
  50. Jensen J.R., Narumalani S., Weatherbee O. and Mackey H.E. Jr 1993c. Measurement of seasonal and yearly cattail and waterlily changes using multidate SPOT panchromatic data. Photogrammetric Engineering and Remote Sensing 59: 519–525.Google Scholar
  51. Jensen J.R., Hodgson M., Christensen E., Mackey H.E., Tinney L. and Sharitz R. 1986. Remote sensing of inland wetlands: a multi-spectral approach. Photogrammetric Engineering and Remote Sensing 52: 87–100.Google Scholar
  52. Jensen J.R., Ramsey E.W., Mackey H.E. Jr, Christensen E.J. and Sharitz R.R. 1987. Inland wetland change detection using aircraft MSS data. Photogrammetric Engineering and Remote Sensing 53: 521–529.Google Scholar
  53. Johnston R.M. and Barson M.M. 1993. Remote sensing of Australian wetlands: an evaluation of Landsat TM data for inventory and classification. Australian Journal of Marine and Freshwater Resources 44: 235–252.CrossRefGoogle Scholar
  54. Kasischke E.S. and Bourgeau-Chavez L.L. 1997. Monitoring south Florida wetlands using ERS-1 SAR imagery. Photogrammetric Engineering and Remote Sensing 63: 281–291.Google Scholar
  55. Kauth R.J. and Thomas G.S. 1976. The tasseled cap-a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat, Symposium on Machine Processing of Remotely Sensed Data 1976. Laboratory for Applications of Remote Sensing, West Lafayette, Indiana, USA, pp 41–51.Google Scholar
  56. Kempka R.G., Kollasch R.P. and Koeln G.T. 1992. Using GIS to preserve the Pacific flyway's wetland resource. GIS World 5: 46–52.Google Scholar
  57. Kindscher K., Fraser A., Jakubauskas M.E. and Debinski D.M. 1998. Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management 5: 265–273.CrossRefGoogle Scholar
  58. Klemas V. 2001. Remote sensing of landscape-level coastal environmental indicators. Environmental Management 27: 47–57.PubMedCrossRefGoogle Scholar
  59. Kushwaha S.P.S., Dwivedi R.S. and Rao B.R.M. 2000. Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data. International Journal of Remote Sensing 21: 565–579.CrossRefGoogle Scholar
  60. Lee C.T. and Marsh S.E. 1995. The use of archival Landsat MSS and ancillary data in a GIS environment to map historical change in an urban riparian habitat. Photogrammetric Engineering and Remote Sensing 61: 999–1008.Google Scholar
  61. Lee J.K. and Park R.A. 1992. Application of geoprocessing and simulation modeling to estimate impacts of sea level rise on the northeast coast of Florida. Photogrammetric Engineering and Remote Sensing 58: 1579–1586.Google Scholar
  62. Lee K.H. and Lunetta R.S. 1996. Wetland Detection Methods. In: Lyon J.G. and McCarthy J. (eds), Wetland and Environmental Applications of GIS. Lewis Publishers, New York, pp. 249–284.Google Scholar
  63. Llewellyn D.W., Shaffer G.P., Craig N.J., Creasman L., Pashley D., Swan M. et al. 1996. A decision-support system for prioritizing restoration sites on the Mississippi River alluvial plain. Conservation Biology 10: 1446–1455.CrossRefGoogle Scholar
  64. Lo C.P. and Watson L.J. 1998. The influence of geographic sam pling methods on vegetation map accuracy evaluation in a swampy environment. Photogrammetric Engineering and Remote Sensing 64: 1189–1200.Google Scholar
  65. Louiselle S., Bracchini L., Bonechi C. and Rossi C. 2001. Modelling energy fluxes in remote wetland ecosystems with the help of remote sensing. Ecological Modelling 145: 243–261.CrossRefGoogle Scholar
  66. Luczkovich J., Wagner T.W., Michalek J.L. and Stoffle R.W. 1993. Discrimination of coral reefs, seagrass meadows, and sandy bottom types from space: a Dominican Republic case study. Photogrammetric Engineering and Remote Sensing 59: 385–389.Google Scholar
  67. Lulla K. 1983. The Landsat satellites and selected aspects of physical geography. Progress in Physical Geography 7: 1–45.CrossRefGoogle Scholar
  68. Lunetta R.S. and Barlogh M.E. 1999. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photo grammetric Engineering and Remote Sensing 65: 1303–1310.Google Scholar
  69. MacDonald T.A. 1999. Wetland rehabilitation and remote sensing. In: Streever W. (ed.), An International Perspective on Wetland Rehabilitation. Kluwer Academic Publishers, Boston, pp. 251–264.CrossRefGoogle Scholar
  70. Macleod R.D. and Congalton R.G. 1998. A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing 64: 207–216.Google Scholar
  71. McCarthy T.S., Franey N.J., Ellery W.N. and Ellery K. 1993. The use of SPOT imagery in the study of environmental processes of the Okavango Delta, Botswana. South African Journal of Sciences 89: 432–436.Google Scholar
  72. Mertes L.A.K., Daniel D.L., Melack J.M., Nelson B., Martinelli L.A. and Forsberg B.R. 1995. Spatial patterns of hydrology, geomorphology, and vegetation on the floodplain of the Amazon River in Brazil from a remote sensing perspective. Geomorphology 13: 215–232.CrossRefGoogle Scholar
  73. Mitsch W.J. and Gosselink J.G. 1993. Wetlands. 2nd edn. Van Nostrand Reinhold, New York, 722 pp.Google Scholar
  74. Munyati C. 2000. Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset. International Journal of Remote Sensing 21: 1787–1806.CrossRefGoogle Scholar
  75. Narumalani S., Jensen J.R., Burkhalter S., Althausen J.D. and Mackey H.E. Jr 1997. Aquatic macrophyte modeling using GIS and logistic multiple regression. Photogrammetric Engineering and Remote Sensing 63: 41–49.Google Scholar
  76. Nayak S.R. and Sahai B. 1985. Coastal morphology: a case study of the Gulf of Khambhat (Cambay). International Journal of Remote Sensing 6: 559–567.CrossRefGoogle Scholar
  77. Palylyk C.L., Crown P.H. and Turchenek L.W. 1987. Landsat MSS data for peatland inventory in Alberta, Symposium' 87 Wetlands / Peatlands 1987. Edmonton, Alberta, Canada, pp 365–371.Google Scholar
  78. Park R.A., Lee J.K. and Canning D.J. 1993. Potential effects of sea-level rise on Puget Sound wetlands. Geocarto International 4: 99–110.CrossRefGoogle Scholar
  79. Park R.A., Lee J.K., Mausel P.W. and Howe R.C. 1991. Use of remote sensing for modeling the impacts of sea level rise. World Resources Review 3: 184–205.Google Scholar
  80. Penuelas J., Gamon J.A., Griffin K.L. and Field C.B. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment 46: 110–118.CrossRefGoogle Scholar
  81. Pietroniro A., Prowse T., Hamlin L., Kouwen N. and Soulis R. 1996. Application of a grouped response unit hydrological model to a northern wetland region. Hydrological Processes 10: 1245–1261.CrossRefGoogle Scholar
  82. Pietroniro A., Prowse T.D. and Lalonde V. 1995. Classifying terrain in a muskeg-wetland regime for application to GRU-type distributed hydrologic models. Canadian Journal of Remote Sensing 22: 45–52.Google Scholar
  83. Pope K.O., Rejmankova E., Savage H.M., Arredondo-Jimenez J.I., Rodriguez M.H. and Roberts D.R. 1994. Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecological Applications 4: 81–90.PubMedCrossRefGoogle Scholar
  84. Raabe E.A. and Stumpf R.R. 1996. Image processing methods: procedures in selection, registration, normalization, and enhancement of satellite imagery in coastal wetlands. Open File Report 97–287. United States Geologic Survey.Google Scholar
  85. Ramsey E.W. III, Chappell D.K. and Baldwin D.G. 1997. AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana. Photogrammetric Engineering and Remote Sensing 63: 293–297.Google Scholar
  86. Ramsey E.W. III, Chappell D.K., Jacobs D., Sapkota S.K. and Baldwin D.G. 1998. Resource management of forested wetlands: hurricane impact and recovery mapping by combining Landsat TM and NOAA AVHRR data. Photogrammetric Engineering and Remote Sensing 64: 733–738.Google Scholar
  87. Ramsey E.W. and Laine S.C. 1997. Comparison of Landsat Thematic Mapper and high resolution photography to identify change in complex coastal wetlands. Journal of Coastal Research 13: 281–292.Google Scholar
  88. Reed P.B. Jr 1988. National List of Plants that Occur in Wetlands: National Summary. US Fish and Wildlife Service Biological Report 88(24).Google Scholar
  89. Reimold R.J., Gallagher J.L. and Thompson D.E. 1973. Remote sensing of tidal marsh. Photogrammetric Engineering and Remote Sensing 477–488.Google Scholar
  90. Rutchey K. and Vilcheck L. 1994. Development of an Everglades vegetation map using a SPOT image and the Global Positioning System. Photogrammetric Engineering and Remote Sensing 60: 767–775.Google Scholar
  91. Rutchey K. and Vilchek L. 1999. Air photointerpretation and satellite imagery analysis techniques for mapping cattail coverage in a northern Everglades impoundment. Photogrammetric Engineering and Remote Sensing 65: 185–191.Google Scholar
  92. Sader S.A., Ahl D. and Liou W.-S. 1995. Accuracy of Landsat TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment 53: 133–144.CrossRefGoogle Scholar
  93. Schowengerdt R.A. 1997. Remote Sensing. 2nd edn. Academic Press, 522 pp.Google Scholar
  94. Singh A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10: 989–1003.CrossRefGoogle Scholar
  95. Spanglet H.J., Ustin S.L. and Rejmankova E. 1998. Spectral reflectance characteristics of California subalpine marsh plant communities. Wetlands 18: 307–319.CrossRefGoogle Scholar
  96. Tiner R.W. 1990. Use of high-altitude aerial photography for inventorying forested wetlands in the United States. Forest Ecology and Management 33: 593–604.CrossRefGoogle Scholar
  97. Townsend P.A. and Walsh S.J. 1998. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology 21: 295–312.CrossRefGoogle Scholar
  98. Weismiller R.A., Kristof S.J., Scholz D.K., Anuta P.E. and Momin S.A. 1977. Change detection in coastal zone environments. Photogrammetric Engineering and Remote Sensing 43: 1533–1539.Google Scholar
  99. Welch R., Madden M. and Doren R.F. 1999. Mapping the Everglades. Photogrammetric Engineering and Remote Sensing 65: 163–170.Google Scholar
  100. Welch R., Remillard M. and Doran R.F. 1995. GIS database development for South Florida's National Parks and Preserves. Photogrammetric Engineering and Remote Sensing 61: 1371–1381.Google Scholar
  101. Wickware G.M. and Howarth P.J. 1981. Change detection in the Peace-Athabasca Delta using digital Landsat data. Remote Sensing of Environment 11: 9–25.CrossRefGoogle Scholar
  102. Wilkinson G. and Shepherd I. 1995. Testing the water: monitoring and modelling wetlands using integrated GIS and RS techniques. GIS Europe 4: 38–40.Google Scholar
  103. Work E.A. Jr and Gilmer D.S. 1976. Utilization of satellite data for inventorying prairie ponds and lakes. Photogrammetric Engineering and Remote Sensing 42: 685–694.Google Scholar
  104. Yi G.-C., Risley D., Koneff M. and Davis C. 1994. Development of Ohio's GIS-based wetland inventory. Journal of Soil and Water Conservation 49: 23–28.Google Scholar
  105. Zainal A.J.M., Dalby D.H. and Robinson I.S. 1993. Monitoring marine ecological changes on the East Coast of Bahrain with Landsat TM. Photogrammetric Engineering and Remote Sensing 59: 415–421.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Stacy L. Ozesmi
    • 1
  • Marvin E. Bauer
    • 1
  1. 1.Department of Forest ResourcesUniversity of MinnesotaSt PaulUSA

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