Wetlands Mapping in North America by Decision Rule Classification Using MODIS and Ancillary Data

Abstract

An up-to-date wetlands map based on remote sensing data at a continental scale is urgently needed for estimating global environmental change. In this study, a wetlands map of North America was developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained in 2008 and ancillary data. For this purpose, a decision rule classification method was developed relied upon the hierarchical characteristics of land types and prior knowledge about the geographical location of wetlands. Two hierarchical levels of land types were used to extract wetlands. At the first level, non-vegetation land types including water, snow, urban, and bare areas were separately extracted from vegetation land types using threshold methods. At the second level, wetlands were discriminated from non-wetland vegetation land types with the MODIS tasseled cap (brightness, greenness, and wetness) indices using the decision tree method. In addition, elevation data were used to build the elevation mask and a climate map was used to subdivide the study area into five sub-regions. In the quantitative accuracy assessment, user's and producer's accuracies of wetlands for the whole study area were calculated as 80.3% and 83.7%, respectively. In a comparison with two existing global land cover datasets, GLC2000 and IGBP DISCover, our results show significant improvement in extracting coastal and narrow types of wetlands. This study indicates that decision rule classification, integrated with multi-temporal MODIS data and ancillary data, is useful to develop an improved wetlands map at a continental scale.