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dc.contributor.authorMeyer, Thomas J.
dc.date.accessioned2024-07-11T00:28:08Z
dc.date.available2024-07-11T00:28:08Z
dc.date.issued2024-05
dc.identifier.urihttp://hdl.handle.net/11122/15162
dc.descriptionThesis (M.S.) University of Alaska Fairbanks, 2024en_US
dc.description.abstractTorrential rains, flooding, and storm surges are all considered meteorological and hydrological hazards. These hazards can quickly transition into disasters whenever they begin to effect human life or infrastructure. When these disasters occur, they can lead to devastating outcomes such as damage or complete loss of infrastructure, reduced crop yields and related impacts on food security, as well as risks to human life. For decades, these events have been monitored and tracked primarily from visible/infrared sensors. Unfortunately, flooding and storm surges often occur during extensive weather and cloud cover conditions, which can make monitoring these hazards across large spatial scales difficult if not impossible, especially in poorly developed regions. Synthetic Aperture Radar (SAR), with its capability to penetrate through clouds and monitor during both day and night cycles, may provide substantial improvements to flood hazard management, once robust techniques for the detection of surface water extent were developed. This thesis details the development of HYDRO30, an automatic algorithm to map surface water extent from Sentinel-1 SAR data. At its core the HYDRO30 approach automatically creates surface water extent maps by preforming adaptive thresholding on dual-polarized and radiometrically terrain corrected (RTC) SAR images. We use data from the Sentinel-1 C-band SAR constellation as input, as this constellation provides global access to free and open medium resolution SAR data that comes at a reliable sampling rate of six-to-twelve days. Such free-and-open, regularly sampled data is indispensable for hazard monitoring across regional to continental scales. In this thesis, I will show that the HYDRO30 algorithm achieves promising results in delivering robust and accurate surface water extent maps within an efficient timeline. The work presented in this thesis was developed as part of the HydroSAR project, a NASA-funded effort led by UAF to develop an automatic service for the monitoring of weather-related hazards in the Hindu Kush Himalaya. The thesis will also show an application of the HYDRO30 technology for the monitoring of flood hazards in northern Bangladesh during the 2020 south-Asian monsoon season.en_US
dc.language.isoen_USen_US
dc.subjectFlood forecastingen_US
dc.subjectRainstormsen_US
dc.subjectRemote sensingen_US
dc.subjectSevere stormsen_US
dc.subjectWeatheren_US
dc.subjectSynthetic aperture radaren_US
dc.subjectHydrological forecastingen_US
dc.subject.otherMaster of Science in Geoscienceen_US
dc.titleA method for automatic surface water extent mapping from Sentinel-1 SAR data for improved response to weather related hazardsen_US
dc.typeThesisen_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Geoscienceen_US
dc.contributor.chairMeyer, Franz J.
dc.contributor.committeeOsmanoglu, Batuhan
dc.contributor.committeeMaio, Chris
refterms.dateFOA2024-07-11T00:28:11Z


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