Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets
As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restorat...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9205661/ |
| _version_ | 1818574006241984512 |
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| author | Vanessa L. Valenti Erica C. Carcelen Kathleen Lange Nicholas J. Russo Bruce Chapman |
| author_facet | Vanessa L. Valenti Erica C. Carcelen Kathleen Lange Nicholas J. Russo Bruce Chapman |
| author_sort | Vanessa L. Valenti |
| collection | DOAJ |
| description | As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time. |
| first_indexed | 2024-12-15T00:20:40Z |
| format | Article |
| id | doaj.art-f5befe471c9d4d3bbc13c654888ef60a |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-15T00:20:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-f5befe471c9d4d3bbc13c654888ef60a2022-12-21T22:42:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01136008601810.1109/JSTARS.2020.30239019205661Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial DatasetsVanessa L. Valenti0https://orcid.org/0000-0003-0375-9859Erica C. Carcelen1Kathleen Lange2Nicholas J. Russo3Bruce Chapman4NASA DEVELOP, Jet Propulsion Laboratory (Science Systems and Applications, Inc.), California Institute of Technology, Pasadena, CA, USANASA DEVELOP, Jet Propulsion Laboratory (Science Systems and Applications, Inc.), California Institute of Technology, Pasadena, CA, USANASA DEVELOP, Jet Propulsion Laboratory (Science Systems and Applications, Inc.), California Institute of Technology, Pasadena, CA, USANASA DEVELOP, Jet Propulsion Laboratory (Science Systems and Applications, Inc.), California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAAs one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.https://ieeexplore.ieee.org/document/9205661/Graphical user interfaces (GUI)image classificationmonitoringoptical image processingsynthetic aperture radarsatellite applications |
| spellingShingle | Vanessa L. Valenti Erica C. Carcelen Kathleen Lange Nicholas J. Russo Bruce Chapman Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Graphical user interfaces (GUI) image classification monitoring optical image processing synthetic aperture radar satellite applications |
| title | Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets |
| title_full | Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets |
| title_fullStr | Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets |
| title_full_unstemmed | Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets |
| title_short | Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets |
| title_sort | leveraging google earth engine user interface for semiautomated wetland classification in the great lakes basin at 10 m with optical and radar geospatial datasets |
| topic | Graphical user interfaces (GUI) image classification monitoring optical image processing synthetic aperture radar satellite applications |
| url | https://ieeexplore.ieee.org/document/9205661/ |
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