Automated high-resolution satellite-derived coastal bathymetry mapping
Accurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243422000198 |
| _version_ | 1797961205944418304 |
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| author | Matthew J. McCarthy Daniel B. Otis David Hughes Frank E. Muller-Karger |
| author_facet | Matthew J. McCarthy Daniel B. Otis David Hughes Frank E. Muller-Karger |
| author_sort | Matthew J. McCarthy |
| collection | DOAJ |
| description | Accurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of which are easily or affordably replicated for repeat mapping and change monitoring. Satellite-derived bathymetry (SDB), however, offers the potential to map shallow water bodies repeatedly and efficiently with high spatial and temporal resolution (i.e., daily-weekly at 5 m or better). One challenge to large-scale implementation of SDB lies in the automated derivation of inherent water column properties such that they may be accurately compensated for across a variety of depth and substrate conditions. Here we present an algorithm that leverages WorldView (Maxar/Digital GlobeTM) satellite imagery to map the entire 3700 km2 Florida Keys (USA) island chain at 2-meter resolution without the need for any in-situ data collections. Preprocessing included radiometric calibration, atmospheric correction, automated deglinting, and automated detection of optically deep water, which was then used to estimate chlorophyll-a concentration assuming that the study area is primarily comprised of Case-I water (i.e., those where the optical signal is dominated by water, Chlorophyll-a in phytoplankton, and properties that vary in proportion to Chlorophyll-a concentration). Estimating Chlorophyll-a concentration allowed us to calculate the appropriate tuning coefficients used in a spectral band ratio equation for estimating bathymetry. The entire process was fully automated from ingestion of Level-1B image to bathymetry raster output. Mapping the Florida Keys from Key Largo to Key West required 34 WorldView images and was completed in approximately 27 min for an average processing time of 47 s per image using a single GPU core (i.e., supercomputing resources were not needed). After combining the products (mosaicking) in ArcMap, the wall-to-wall bathymetry map was validated against a LiDAR-derived bathymetry model with over 600,000 points; results show an RMSE of 1.95 m over depths from 0 to 15 m. |
| first_indexed | 2024-04-11T00:56:31Z |
| format | Article |
| id | doaj.art-64aedbcdfee44317a8194e82c39593e5 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T00:56:31Z |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-64aedbcdfee44317a8194e82c39593e52023-01-05T04:31:05ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-03-01107102693Automated high-resolution satellite-derived coastal bathymetry mappingMatthew J. McCarthy0Daniel B. Otis1David Hughes2Frank E. Muller-Karger3Oak Ridge National Laboratory, Geospatial Science and Human Security Division, Remote Sensing Group, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA; Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South, Saint Petersburg, FL 33701, USA; Corresponding author at: Oak Ridge National Laboratory, Geospatial Science and Human Security Division, Remote Sensing Group, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA.Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South, Saint Petersburg, FL 33701, USAOak Ridge National Laboratory, Geospatial Science and Human Security Division, Remote Sensing Group, 1 Bethel Valley Road, Oak Ridge, TN 37830, USAInstitute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Ave. South, Saint Petersburg, FL 33701, USAAccurate and up-to-date maps of coastal bathymetry are fundamental for coastal resource management, commercial and military navigation, and aquaculture, among many applications. Existing methods for bathymetry mapping require intensive and costly field surveys or targeted aerial captures, neither of which are easily or affordably replicated for repeat mapping and change monitoring. Satellite-derived bathymetry (SDB), however, offers the potential to map shallow water bodies repeatedly and efficiently with high spatial and temporal resolution (i.e., daily-weekly at 5 m or better). One challenge to large-scale implementation of SDB lies in the automated derivation of inherent water column properties such that they may be accurately compensated for across a variety of depth and substrate conditions. Here we present an algorithm that leverages WorldView (Maxar/Digital GlobeTM) satellite imagery to map the entire 3700 km2 Florida Keys (USA) island chain at 2-meter resolution without the need for any in-situ data collections. Preprocessing included radiometric calibration, atmospheric correction, automated deglinting, and automated detection of optically deep water, which was then used to estimate chlorophyll-a concentration assuming that the study area is primarily comprised of Case-I water (i.e., those where the optical signal is dominated by water, Chlorophyll-a in phytoplankton, and properties that vary in proportion to Chlorophyll-a concentration). Estimating Chlorophyll-a concentration allowed us to calculate the appropriate tuning coefficients used in a spectral band ratio equation for estimating bathymetry. The entire process was fully automated from ingestion of Level-1B image to bathymetry raster output. Mapping the Florida Keys from Key Largo to Key West required 34 WorldView images and was completed in approximately 27 min for an average processing time of 47 s per image using a single GPU core (i.e., supercomputing resources were not needed). After combining the products (mosaicking) in ArcMap, the wall-to-wall bathymetry map was validated against a LiDAR-derived bathymetry model with over 600,000 points; results show an RMSE of 1.95 m over depths from 0 to 15 m.http://www.sciencedirect.com/science/article/pii/S0303243422000198WorldViewChlorophyllTurbidityHigh-performance computing |
| spellingShingle | Matthew J. McCarthy Daniel B. Otis David Hughes Frank E. Muller-Karger Automated high-resolution satellite-derived coastal bathymetry mapping International Journal of Applied Earth Observations and Geoinformation WorldView Chlorophyll Turbidity High-performance computing |
| title | Automated high-resolution satellite-derived coastal bathymetry mapping |
| title_full | Automated high-resolution satellite-derived coastal bathymetry mapping |
| title_fullStr | Automated high-resolution satellite-derived coastal bathymetry mapping |
| title_full_unstemmed | Automated high-resolution satellite-derived coastal bathymetry mapping |
| title_short | Automated high-resolution satellite-derived coastal bathymetry mapping |
| title_sort | automated high resolution satellite derived coastal bathymetry mapping |
| topic | WorldView Chlorophyll Turbidity High-performance computing |
| url | http://www.sciencedirect.com/science/article/pii/S0303243422000198 |
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