Automated quantification of surface water inundation in wetlands using optical satellite imagery

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

doi:10.3390/rs9080807

Automated quantification of surface water inundation in wetlands using optical satellite imagery

Remote Sensing

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

https://doi.org/10.3390/rs9080807

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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.

Additional publication details
Publication type	Article
Publication Subtype	Journal Article
Title	Automated quantification of surface water inundation in wetlands using optical satellite imagery
Series title	Remote Sensing
DOI	10.3390/rs9080807
Volume	9
Issue	8
Year Published	2017
Language	English
Publisher	MDPI
Contributing office(s)	Eastern Geographic Science Center
Description	Article 807; 22 p.
Google Analytic Metrics	Metrics page