TY - JOUR
T1 - An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery
AU - Temimi, Marouane
AU - Abdelkader, Mohamed
AU - Tounsi, Achraf
AU - Chaouch, Naira
AU - Carter, Shawn
AU - Sjoberg, Bill
AU - Macneil, Alison
AU - Bingham-Maas, Norman
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - U-Net
KW - VIIRS
KW - breakup
KW - flood
KW - freeze up
KW - ice jams
KW - natural hazards
KW - river ice
UR - http://www.scopus.com/inward/record.url?scp=85175365948&partnerID=8YFLogxK
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U2 - 10.3390/rs15204896
DO - 10.3390/rs15204896
M3 - Article
AN - SCOPUS:85175365948
VL - 15
JO - Remote Sensing
JF - Remote Sensing
IS - 20
M1 - 4896
ER -