Assessing coastal plain wetland composition using advanced spaceborne thermal emission and reflection radiometer imagery.

摘要

Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. 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 (0.52-2.185μm). 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 classes, 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 for the logit model was 76.7%. The CART producer's accuracy of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%). However, 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 subpixel analysis of the ASTER images to estimate canopy cover of forested wetlands. We used top-of-atmosphere reflectance from the visible and near infrared bands, Delta Normalized Difference Vegetation Index, and a tasseled cap brightness, greenness, and wetness in linear regression model with canopy cover as the dependent variable. The model achieved an adjusted-R 2 of 0.69 (RMSE = 2.7%) for canopy cover less than 16%, and an adjusted-R 2 of 0.04 (RMSE = 19.8%) for higher canopy cover values. 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.