Assessing Coastal Plain Wetland Composition using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery

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dc.contributor.authorPantaleoni, Evaen
dc.contributor.committeecochairGalbraith, John M.en
dc.contributor.committeecochairWynne, Randolph H.en
dc.contributor.committeememberCarstensen, Laurence W.en
dc.contributor.committeememberCampbell, James B. Jr.en
dc.contributor.committeememberDaniels, W. Leeen
dc.contributor.departmentCrop and Soil Environmental Sciencesen
dc.date.accessioned2014-03-14T20:14:22Zen
dc.date.adate2007-08-09en
dc.date.available2014-03-14T20:14:22Zen
dc.date.issued2007-05-03en
dc.date.rdate2010-10-08en
dc.date.sdate2007-07-27en
dc.description.abstractEstablishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type.en
dc.description.degreePh. D.en
dc.identifier.otheretd-07272007-134802en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07272007-134802/en
dc.identifier.urihttp://hdl.handle.net/10919/28419en
dc.publisherVirginia Techen
dc.relation.haspartEva_Pantaleoni_dissertation.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectASTERen
dc.subjectwetlandsen
dc.subjectmulti-temporal analysisen
dc.subjectlogit modelen
dc.subjectCARTen
dc.titleAssessing Coastal Plain Wetland Composition using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imageryen
dc.typeDissertationen
thesis.degree.disciplineCrop and Soil Environmental Sciencesen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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