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Using Satellite Imagery with Digitized Aerial Photography for Ecological Analysis in Small Urban Watersheds

Published online by Cambridge University Press:  13 July 2009

W. B. Clapham Jr
W. B. Clapham Jr*
Affiliation:
Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, Ohio
*
Professor, Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, OH 44115; (fax) 216-523-7200; (e-mail) [email protected].
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Small urban watersheds are difficult to characterize in terms appropriate to ecological assessment They are typically larger than the study areas common to ecological assessments, typically measuring in tens of square kilometers in area, even for a small urban stream. They are also more variable and fragmented than natural ecosystems, since they reflect socioeconomic and historical, as well as natural, factors. We have traditionally relied on land use/land cover classifications to characterize such areas, but these record a mixture of natural and socioeconomic variables, and they typically reflect neither the high diversity nor the extreme fragmentation of the urban environment. Doan Brook is a small urban stream on the east side of Cleveland, Ohio. Thematic Mapper (TM) and ½-m resolution Digital Aerial Photography were used together to characterize its roughly 60 km2 area in terms of ecologically meaningful gradients such as percent canopy closure and percent perviousness of the ground. Field checking was used to verify the consistency of the class definition and to correct the parameters. In the process, we found several patterns with ecological significance that would have been overlooked in a more standard classification of the satellite image. The approach used in this study provides an alternative to land-cover analysis that may be broadly applicable to environmental assessments in urban areas, including ecological simulation modeling, assessment of park resources, and analysis of the environmental change in heterogeneous regions.

Type
Features & Reviews
Information
Environmental Practice , Volume 3 , Issue 1 , March 2001 , pp. 48 - 62
Copyright
Copyright © National Association of Environmental Professionals 2001

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References

Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, W. E.. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. USGS Professional Paper 964. US Geological Survey, Reston, VA.CrossRefGoogle Scholar
Ball, G. H., and Hall, D. J.. 1965. A Novel Method of Data Analysis and Pattern Classification. Stanford Research Institute, Menlo Park, CA.Google Scholar
Bicknell, B. R., Imhoff, J. C., Kittle, J. L. Jr., Donigian, A. S. Jr., and Johanson, R. C.. 1997. Hydrological Simulation Program—Fortran: User's Manual for Version 11. Report EPA/600/R–97Z/080, USEPA, Washington, DC.Google Scholar
Biging, G. S., Colby, D. R., and Congalton, R. G.. 1999. Sampling Systems for Change Detection Accuracy Assessment In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, Lunetta, R. S. and Elvidge, C. D., eds. Taylor & Francis, London, 281–308.Google Scholar
Chrisman, N. 1997. Exploring Geographic Information Systems. John Wiley & Sons, New York, 298 pp.Google Scholar
Clapham, W. B. Jr., and Sgro, G.. 2000. Remote Sensing and Modeling as Ecological Tools For Small Urban Watersheds. Proceedings of the 2000 Conference, National Association of Environmental Professionals.Google Scholar
Congalton, R. G., Balogh, M., Bell, C., Green, K., Milliken, J. A., and Ottman, R.. 1998. Mapping and Monitoring Agricultural Crops and Other Land Cover in the Lower Colorado River Basin. Photogrammetric Engineering & Remote Sensing 64(11):1007–1113.Google Scholar
Congalton, R. G., and Green, K.. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton, FL, 137 pp.Google Scholar
Evans, J. E., and Seamon, D. E.. 1997. A GIS Model to Calculate Sediment Yields from a Small Rural Watershed, Old Woman Creek, Erie and Huron Counties. Ohio, Ohio Journal of Science 97(3):44–52.Google Scholar
Northeast Ohio Regional Sewer District. 1992. Greater Cleveland Area Environmental Water Quality Assessment 1991–1992. Northeast Ohio Regional Sewer District, Cleveland, 526 pp.Google Scholar
Northeast Ohio Regional Sewer District. 1994. Greater Cleveland Area Environmental Water Quality Assessment, 1989–1990. Northeast Ohio Regional Sewer District, Cleveland, 558 pp.Google Scholar
Northeast Ohio Regional Sewer District. 1997. Greater Cleveland Area Environmental Water Quality Assessment 1993–1995. Northeast Ohio Regional Sewer District, Cleveland, 569 pp.Google Scholar
Ohio Department of Administrative Services. 1998. GIS Support Center, Ohio Department of Administrative Services, Division of Computer Services. www.geodata.state.oh.us.Google Scholar
Roberts, D. A., Batista, G. T., Pereira, J. L. G., Waller, E. K., and Nelson, B. W.. 1999. Change Identification Using Multitemporal Spectral Mixture Analysis: Applications in Eastern Amazonia. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, Lunetta, R. S. and Elvidge, C. D., eds., Taylor & Francis, London, 137–161.Google Scholar
Schrader, S., and Pouncey, R.. 1997. ERDAS Field Guide, 4th ed.ERDAS, Atlantaa.Google Scholar
Shamsi, U. M. 1995. Water Resource Engineering Application of Geographic Information Systems. In Wetland and Environmental Applications of GIS, Lyon, J. G. and McCarthy, J., eds., Lewis Publishers, Boca Raton, FL, 173–191.Google Scholar
Smith, M. B., and Vidmar, A.. 1994. Data Set Derivation for GIS-based Hydrological Modeling. Photogrammetric Engineering & Remote Sensing 60(1):67–76.Google Scholar
Spence, C., Dalton, A., and Kite, G.. 1995. GIS supports Hydrological Modeling. GIS World 8(1):62–65.Google Scholar
Stevens, S. S. 1946. On the Theory of Scales of Measurement. Science 103:677–680.CrossRefGoogle ScholarPubMed