Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery

DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.

doi:10.3390/rs9080807

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Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery

^1,*

¹,

²,

³,

⁴

and

Mark L. Carroll

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Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

U.S. Fish and Wildlife Service, National Wetland Inventory, Falls Church, VA 22041 USA

U.S. Geological Survey, Eastern Geographic Science Center, Reston, VA 20192-000, USA

⁴

Department of Biology, University of Western Ontario, London, ON N6A 3K7, Canada

⁵

Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁶

Science Systems and Applications Inc., Lanham, MD 20706, USA

Author to whom correspondence should be addressed.

Academic Editors: Alessio Domeneghetti, Angelica Tarpanelli and Richard Gloaguen

Remote Sens. 2017, 9(8), 807; https://doi.org/10.3390/rs9080807

Received: 17 May 2017 / Revised: 18 July 2017 / Accepted: 26 July 2017 / Published: 7 August 2017

(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)

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Abstract

We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. View Full-Text

Keywords: wetland; inundation; Landsat; sub-pixel water fraction

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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MDPI and ACS Style

DeVries, B.; Huang, C.; Lang, M.W.; Jones, J.W.; Huang, W.; Creed, I.F.; Carroll, M.L. Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery. Remote Sens. 2017, 9, 807. https://doi.org/10.3390/rs9080807

AMA Style

DeVries B, Huang C, Lang MW, Jones JW, Huang W, Creed IF, Carroll ML. Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery. Remote Sensing. 2017; 9(8):807. https://doi.org/10.3390/rs9080807

Chicago/Turabian Style

DeVries, Ben, Chengquan Huang, Megan W. Lang, John W. Jones, Wenli Huang, Irena F. Creed, and Mark L. Carroll. 2017. "Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery" Remote Sensing 9, no. 8: 807. https://doi.org/10.3390/rs9080807

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Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery

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