Project Summary
| Project Issue. Improved information on wetlands in the Lower Maumee River watershed within Lucas County, Ohio is needed to help stop the loss of existing wetlands in this region. Urbanization continues to expand causing alteration of natural waterways and wetlands, increased water pollution, and destruction of wildlife habitat. Wetland mitigation funding generated from building projects in the Maumee River watershed has been used to protect wetlands in other areas outside the watershed. Wetlands within the Maumee River watershed that function to reduce runoff, minimize flooding, filter pollutants, control erosion and sedimentation, and provide wildlife habitat are disappearing. This loss of wetlands not only affects the urban area economically, it affects the quality of life and environmental health within this region. |
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| Project Goals. The Open Space and Wetlands Action Group of the Maumee RAP has been developing a revised wetlands classification and inventory for the Lower Maumee watershed in Lucas County, Ohio through a grant from the Ohio Environmental Protection Agency, the Maumee River Watershed Wetlands Protection and Enhancement Planning Project. The issues the project addresses include. 1. It identifies and evaluates existing wetlands and potential mitigation sites in the watershed. 2. It provides information to local planners, government officials, environmental consultants, and conservation agencies about local wetland locations, quality, and importance. 3. It creates an up-to-date, accessible GIS-based map of wetlands and potential wetlands in the Lower Maumee River watershed. 4. It identifies watershed restoration needs and action strategies. 5. It provides for advisory and implementation groups to facilitate the success of the project. The ultimate goal of this wetlands planning and mapping project is to protect existing wetlands, increase the number of wetland enhancement projects, and reduce non-point source pollution in the lower Maumee River watershed |
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| Wet forest at Kitty Todd Nature Preserve Lucus County, Ohio; North of Toledo Express Airport photo taken March 19, 2003. | |||||||||||
| Project Overview. Land use change has created a variety of impacts to wetland areas. These changes have many effects on a wetland in terms of climate, air and water quality, hydrological changes, and boundaries and fragmentation of flora and fauna (Ehrenfeld, 2000). Wetlands must be valued as they provide many public amenities with long-term functions, which may be unrecoverable if the wetlands are loss through development, including drainage, water supply and natural habitat provisions (Mitsch et al, 2000). Because public policy supported the filling and destruction of wetlands for so long land use planners are often reluctant or uninformed on how to integrate them into urban environments (Tiltons, 1995). The use of remote sensing technology for the identification, inventory, mapping and classification of land wetlands has been a common application of satellite imagery (MacDonald, 1999; Lyon, 2001). Numerous studies have discussed the positive benefits and opportunities presented by the technology as well as the barriers and limitations (Hardisky et al, 1986; Johnston and Barson, 1993; Kindscher et al., 1998; Lunetta and Barlogh, 1999; Schmidt and Skidmore, 2003, Shuman and Ambrose, 2003, Townsend and Walsh, 2001). With recent improvements in the methods, computing advances, and the easier access and availability of the satellite imagery and data, it has become more possible to consider further advancements in the use of remote sensing to specifically address issues associated with wetlands research and related policy implications. The results of such research have important applications in addressing wetland management issues – such as wetland loss, degradation, and potential for restoration and remediation – where land use pressures have negative impacts on the existence and health of wetland ecosystems. For a comprehensive review of the issues regarding the use of satellite remote sensing for wetlands the reader is referred to Ozesmi and Bauer (2002) . |
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| GIS and Remote Sensings components used to derive the final Northwest Ohio wetlands inventory product. | |||||||||||
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| Conceptual frame work of project work flow. | |||||||||||
| Project Methods. A hybrid model was created in an attempt to improve wetlands information in the study area. A GIS rule-based decision tree algorithm was designed to utilize Landsat 7 Enhanced Thematic Mapper Plus to classify four primary wetland types of interest. These were riparian, forest, prairie, and coastal wetlands. Hybrid models integrating remote sensed data with GIS ancillary information has been advantageous for improving wetlands classification (Bolstad et al 1992, Sader et al 1995, Cedfledt et al 2000, Ozesmi and Bauer 2002, Wolfson et al 2002). The model proved to have several advantages and was successful at advancing the wetlands mapping goals. References. Boldstad, P., Wehde, M., Linder, R. 1992. Rule-based classification models: flexible integration of satellite imagery and thematic spatial data. Photogrammetric Engineering and remote Sensing. V.58. P:965-971. Cedfeldt, P., Watzin, M., Richardson, B. 2000. Using GIS to Identify Functionally Significant Wetlands in the Northeastern United States. Environmental Management. V. 26. N.1. P:13-24. Ehrenfeld, J.G. 2000. Evaluating wetlands within an urban context. Ecological Engineering 15: 253-265. Hardisky M.A., Gross M.F., Klemas V. 1986. Remote Sensing of Coastal Wetlands. BioScience. V.36. P:453-460. Johnston, R., Barson, M. 1993. Remote Sensing of Australian Wetlands: An Evaluation of Landsat TM Data for Inventory and Classification. Kindscher, K., Fraser, A., Jakubauskas, M., Debinski, D. 1998. Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management. V.5. P:265-273. Lunetta, R., Balogh, M. 1999. Application of multitemporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering & Remote Sensing. V.65. N.11. P:1303-1310. Mitsch, J. and Gosselink, J.G. 2000. The value of wetlands: importance of scale and landscape setting. Ecological Economics 35: 25-33. Ozesmi, S., Bauer, M. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management. V.10. P:381-402. Sader, S.A., Ahl., D., and Wen-Shu, L. 1995. Accuracy of Landsat-TM and GIS Rule-Based Methods for Forest Wetland Classification in Maine. Remote Sensing of the Environment. 53:133-144. Shuman, C.S. and Ambrose, R.F. 2003. A Comparison of Remote Sensing and Ground-Based Methods for Monitoring Wetland Restoration Success. Restoration Ecology.11:325-333. Schmidt, K.S. and Skidmore, A.K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment. 85:92-108. Tilton, D. L. 1995. Integrating wetlands into planned landscapes. Landscape and Urban Planning 35: 205-209. Townsend, P. A., and Walsh, S. J. 2001. Remote sensing of forested wetlands: Application of multitemporaI and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecology 157:129-149. Wolfson, L., Mokma, D., Schultink, G., Dersch, E. 2002. Development and use of a wetlands information system for assessing wetland functions. Lakes & Resoviors: Research and Management. V.7. P:207-216. |
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