Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
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 wetl...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/9/8/807 |
| _version_ | 1818959297137082368 |
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| author | Ben DeVries Chengquan Huang Megan W. Lang John W. Jones Wenli Huang Irena F. Creed Mark L. Carroll |
| author_facet | Ben DeVries Chengquan Huang Megan W. Lang John W. Jones Wenli Huang Irena F. Creed Mark L. Carroll |
| author_sort | Ben DeVries |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T11:39:24Z |
| format | Article |
| id | doaj.art-95ac6640b24141f0920de7ed590efa46 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:39:24Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-95ac6640b24141f0920de7ed590efa462022-12-21T19:42:00ZengMDPI AGRemote Sensing2072-42922017-08-019880710.3390/rs9080807rs9080807Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite ImageryBen DeVries0Chengquan Huang1Megan W. Lang2John W. Jones3Wenli Huang4Irena F. Creed5Mark L. Carroll6Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USAU.S. Fish and Wildlife Service, National Wetland Inventory, Falls Church, VA 22041 USAU.S. Geological Survey, Eastern Geographic Science Center, Reston, VA 20192-000, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Biology, University of Western Ontario, London, ON N6A 3K7, CanadaBiospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAWe 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.https://www.mdpi.com/2072-4292/9/8/807wetlandinundationLandsatsub-pixel water fraction |
| spellingShingle | Ben DeVries Chengquan Huang Megan W. Lang John W. Jones Wenli Huang Irena F. Creed Mark L. Carroll Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery Remote Sensing wetland inundation Landsat sub-pixel water fraction |
| title | Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery |
| title_full | Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery |
| title_fullStr | Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery |
| title_full_unstemmed | Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery |
| title_short | Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery |
| title_sort | automated quantification of surface water inundation in wetlands using optical satellite imagery |
| topic | wetland inundation Landsat sub-pixel water fraction |
| url | https://www.mdpi.com/2072-4292/9/8/807 |
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