Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

Remote Sensing

Daniel Clewley³  Jane Whitcomb³  Mahta Moghaddam³  Kyle McDonald¹  Bruce Chapman²  Peter Bunting⁵  Alisa L. Gallant⁴ 

[1] The City College of New York, The City University of New York, NY 10031, USA; E-Mail:;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA; E-Mail:;Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA; E-Mails:Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA;;Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, Wales SY23 3DB, UK; E-Mail:

关键词: wetlands;    SAR;    Alaska;    random forests;    PALSAR;   

DOI  :  10.3390/rs70607272

来源: mdpi

【 摘 要 】

As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km², or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland