Mapping wetlands using ASTER data: a comparison between classification trees and logistic regression

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Abstract:

This study compared a non-parametric and a parametric model for discriminating among uplands (non-wetlands), woody wetlands, emergent wetlands and open water. Satellite images obtained on 6 March 2005 and 16 October 2005 from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and geographic information system (GIS) data layers formed the input for analysis using classification and regression tree (CART®) and multinomial logistic regression analysis. The overall accuracy of the CART model was 73.3%. The overall accuracy of the logit model was 76.7%. The accuracies were not statistically different from each other (McNemar  2 = 1.65, p = 0.19). The CART producer's accuracy of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), whereas woody wetlands identified by the multinomial logit model presented a producer's accuracy higher than that from the CART model (68.7% vs. 52.6%). A McNemar test between the two models and National Wetland Inventory (NWI) maps showed that their accuracies were not statistically different. Overall, these two models provided promising results, although they are not sufficiently accurate to replace current methods of wetland mapping based on feature extraction in high-resolution orthoimagery.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160802562214

Affiliations: 1: Institute of Agricultural and Environmental Research, 3500 John A. Merritt Blvd, Tennessee State University, Nashville, USA 2: Department of Forestry, 319 Cheatham Hall (0324), Virginia Polytechnic Institute and State University, Blacksburg, USA 3: Crop and Soil Environmental Sciences Department, 239 Smyth Hall, Polytechnic Institute and State University, Blacksburg, USA 4: Department of Geography, 109 Major Williams Hall (0115), Polytechnic Institute and State University, Blacksburg, USA

Publication date: January 1, 2009

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