An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery
This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/20/4896 |
| _version_ | 1827720006966706176 |
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| author | Marouane Temimi Mohamed Abdelkader Achraf Tounsi Naira Chaouch Shawn Carter Bill Sjoberg Alison Macneil Norman Bingham-Maas |
| author_facet | Marouane Temimi Mohamed Abdelkader Achraf Tounsi Naira Chaouch Shawn Carter Bill Sjoberg Alison Macneil Norman Bingham-Maas |
| author_sort | Marouane Temimi |
| collection | DOAJ |
| description | This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring. |
| first_indexed | 2024-03-10T20:55:33Z |
| format | Article |
| id | doaj.art-4a011b261dac47cf82033da0220d13c1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:55:33Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4a011b261dac47cf82033da0220d13c12023-11-19T17:57:56ZengMDPI AGRemote Sensing2072-42922023-10-011520489610.3390/rs15204896An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite ImageryMarouane Temimi0Mohamed Abdelkader1Achraf Tounsi2Naira Chaouch3Shawn Carter4Bill Sjoberg5Alison Macneil6Norman Bingham-Maas7Department of Civil, Environmental and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USADepartment of Civil, Environmental and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USADepartment of Civil, Environmental and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USAGildart Haase School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ 07666, USANOAA, National Water Center, Tuscaloosa, AL 35401, USANOAA, JPSS Program Science Office, College Park, MD 20740, USANOAA, National Weather Service, Northeast River Forecast Center, Norton, MA 02766, USANOAA, National Weather Service, Northeast River Forecast Center, Norton, MA 02766, USAThis study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring.https://www.mdpi.com/2072-4292/15/20/4896VIIRSriver icenatural hazardsice jamsfloodfreeze up |
| spellingShingle | Marouane Temimi Mohamed Abdelkader Achraf Tounsi Naira Chaouch Shawn Carter Bill Sjoberg Alison Macneil Norman Bingham-Maas An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery Remote Sensing VIIRS river ice natural hazards ice jams flood freeze up |
| title | An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery |
| title_full | An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery |
| title_fullStr | An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery |
| title_full_unstemmed | An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery |
| title_short | An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery |
| title_sort | automated system to monitor river ice conditions using visible infrared imaging radiometer suite imagery |
| topic | VIIRS river ice natural hazards ice jams flood freeze up |
| url | https://www.mdpi.com/2072-4292/15/20/4896 |
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