A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning
Abstract Since the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral refle...
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American Geophysical Union (AGU)
2024-11-01
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| Series: | Earth and Space Science |
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| Online Access: | https://doi.org/10.1029/2024EA003735 |
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| author | Forrest Corcoran Christopher E. Parrish Lori A. Magruder J. P. Swinski |
| author_facet | Forrest Corcoran Christopher E. Parrish Lori A. Magruder J. P. Swinski |
| author_sort | Forrest Corcoran |
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| description | Abstract Since the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral reflectance attributes, or even intensity values, which have been found useful for benthic habitat mapping with airborne bathymetric lidar. We present a novel method for extracting bathymetry from ICESat‐2 data and automatically adding spectral reflectance values from Sentinel‐2 imagery to each detected bathymetric point. This method, which leverages the cloud computing systems Google Earth Engine and NASA's SlideRule Earth, is ideally suited for “big data” projects with ICESat‐2 data products. To demonstrate the scalability of our workflow, we collected 3,500 ICESat‐2 segments containing approximately 1.4 million spectrally‐attributed bathymetric points. We then used this data set to facilitate training of a deep recurrent neural network for classifying benthic habitats at the ICESat‐2 photon level. We trained two identical models, one with and one without the spectral attributes, to investigate the benefits of fusing ICESat‐2 photons with Sentinel‐2. The results show an improvement in model performance of 18 percentage points, based on F1 score. The procedures and source code are publicly available and will enhance the value of the new ICESat‐2 bathymetry data product, ATL24, which is scheduled for release in Fall 2024. These procedures may also be applicable to data from NASA's upcoming CASALS mission. |
| format | Article |
| id | doaj-art-1f6f6889f08e4e5e9d9761bec0c0df39 |
| institution | OA Journals |
| issn | 2333-5084 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-1f6f6889f08e4e5e9d9761bec0c0df392025-08-20T01:55:41ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842024-11-011111n/an/a10.1029/2024EA003735A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep LearningForrest Corcoran0Christopher E. Parrish1Lori A. Magruder2J. P. Swinski3School of Civl and Contruction Engineering Oregon State University Corvallis OR USASchool of Civl and Contruction Engineering Oregon State University Corvallis OR USACenter for Space Research University of Texas at Austin Austin TX USANASA Goddard Space Flight Center Greenbelt MD USAAbstract Since the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral reflectance attributes, or even intensity values, which have been found useful for benthic habitat mapping with airborne bathymetric lidar. We present a novel method for extracting bathymetry from ICESat‐2 data and automatically adding spectral reflectance values from Sentinel‐2 imagery to each detected bathymetric point. This method, which leverages the cloud computing systems Google Earth Engine and NASA's SlideRule Earth, is ideally suited for “big data” projects with ICESat‐2 data products. To demonstrate the scalability of our workflow, we collected 3,500 ICESat‐2 segments containing approximately 1.4 million spectrally‐attributed bathymetric points. We then used this data set to facilitate training of a deep recurrent neural network for classifying benthic habitats at the ICESat‐2 photon level. We trained two identical models, one with and one without the spectral attributes, to investigate the benefits of fusing ICESat‐2 photons with Sentinel‐2. The results show an improvement in model performance of 18 percentage points, based on F1 score. The procedures and source code are publicly available and will enhance the value of the new ICESat‐2 bathymetry data product, ATL24, which is scheduled for release in Fall 2024. These procedures may also be applicable to data from NASA's upcoming CASALS mission.https://doi.org/10.1029/2024EA003735ICESat‐2cloud computingbathymetrybenthic habitatsdata fusiondeep learning |
| spellingShingle | Forrest Corcoran Christopher E. Parrish Lori A. Magruder J. P. Swinski A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning Earth and Space Science ICESat‐2 cloud computing bathymetry benthic habitats data fusion deep learning |
| title | A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning |
| title_full | A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning |
| title_fullStr | A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning |
| title_full_unstemmed | A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning |
| title_short | A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning |
| title_sort | scalable cloud based workflow for spectrally attributed icesat 2 bathymetry with application to benthic habitat mapping using deep learning |
| topic | ICESat‐2 cloud computing bathymetry benthic habitats data fusion deep learning |
| url | https://doi.org/10.1029/2024EA003735 |
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