Abstract
This study developed a Geographic Information System (GIS) framework using datasets such as light detection and ranging (lidar) data, soils, and land use/land cover for analyzing distribution and structure of over 10,000 coastal prairie wetlands (CPWs) around Galveston Bay, Texas, USA. Lidar data were used to estimate volumes and catchment areas. The CPWs were small (median 0.37 ha) with 72% of wetlands less than 1 ha in size. However, CPWs and their catchments occupy 40.8% of the land area within the study area. Field data from 12 CPWs were used to assess the accuracy of selected structural features. Error analysis suggests that relative elevation, land use/land cover and NWI datasets were reasonably accurate, while National Agriculture Imagery Program (NAIP) derived vegetation cover and Soil Survey Geographic Database (SSURGO) soils data were less reliable. This approach not only provides a detailed inventory of wetland resources, it also supports cumulative estimates of regional wetland functions that are critical to regional policy makers and wetland scientists. Additionally, correlations of geospatial datasets to field measurements should interest those who use geospatial datasets to estimate wetland condition.
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Acknowledgments
We thank Steven Johnston and the Galveston Bay Estuary Program, the Texas Commission on Environmental Quality, the Texas General Land Office, the Environmental Protection Agency, and the Association of American Geographers Anne White Fund for funding this research, Andy Sipocz, John Jacobs, Wes Miller, Mark Kramer, Jim Herrington, Jamie Schubert and many others for sharing their knowledge of coastal prairie wetlands, Dow Chemical, Armand Bayou Nature Center, Don Wilcox and other private landowners that provided access to study sites, and finally Baylor University for supporting this project.
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Enwright, N., Forbes, M.G., Doyle, R.D. et al. Using Geographic Information Systems (GIS) to Inventory Coastal Prairie Wetlands Along the Upper Gulf Coast, Texas. Wetlands 31, 687–697 (2011). https://doi.org/10.1007/s13157-011-0184-5
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DOI: https://doi.org/10.1007/s13157-011-0184-5