Automatic validation of Sentinel-1 borne snow avalanche detections

Snow avalanches threaten human lives, settlements and roads in snow covered mountainous areas. For avalanche forecasting, knowledge of the spatio-temporal occurrence of avalanche activity is critical. Automatic avalanche detection algorithms have been developed to enable consistent avalanche activit...

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Bibliographic Details
Main Author: Pedersen, Jarle Langseth
Format: Master Thesis
Language:English
Published: UiT The Arctic University of Norway 2020
Subjects:
Online Access:https://hdl.handle.net/10037/18985
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spelling ftunivtroemsoe:oai:munin.uit.no:10037/18985 2023-05-15T17:43:41+02:00 Automatic validation of Sentinel-1 borne snow avalanche detections Pedersen, Jarle Langseth 2020-05-30 https://hdl.handle.net/10037/18985 eng eng UiT The Arctic University of Norway UiT Norges arktiske universitet https://hdl.handle.net/10037/18985 openAccess Copyright 2020 The Author(s) VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Mathematics and natural science: 400::Physics: 430 EOM-3901 Master thesis Mastergradsoppgave 2020 ftunivtroemsoe 2021-06-25T17:57:36Z Snow avalanches threaten human lives, settlements and roads in snow covered mountainous areas. For avalanche forecasting, knowledge of the spatio-temporal occurrence of avalanche activity is critical. Automatic avalanche detection algorithms have been developed to enable consistent avalanche activity monitoring for large regions. The Satskred avalanche detection algorithm developed by NORCE applies synthetic aperture radar (SAR) data from the Sentinel-1 satellite constellation and detects avalanches through a relative increase in energy scattered back to the radar from avalanche debris. Field validation of all automatically detected features is desirable, but not achievable due to weather- , light- , and avalanche danger-conditions as well as avalanches occurring at remote locations. In this thesis, an algorithm is presented for automatic comparison of the Satskred avalanche detections to crowd-sourced avalanche observations from regObs, the Norwegian public registry for snow-, weather-, flood-, and ice observations. Thereby, the validation set of field observed avalanches grows with every registered observation and validation of detected features can be performed without further manual intervention. To evaluate whether a detection matches an observed avalanche, the comparison algorithm initially filters detections by time period to ensure temporal similarity. Then, the detection is evaluated with regards to distance, slope aspect and membership of the same drainage basin region as the observation to ensure spatial similarity. If the detection fulfills all the similarity requirements, it is considered to likely represent the same avalanche. Studying a 120 x 86 km area centered over Tromsø in Northern Norway, 308 avalanche observations from 2014 - 2019 were automatically compared to a set of avalanche detections from the same area and time period. The field observations were used as a truth-set and the resulting probability of detection (POD) for the Satskred algorithm was 25.3% (78 out of 308). Further analysis identified trends of larger POD for wet- than dry avalanches, and an increasing POD with avalanche size. A large proportion of avalanches entered to the regObs database are dry slab avalanches, which was found to partly explain the low POD. Master Thesis Northern Norway Tromsø University of Tromsø: Munin Open Research Archive Norway The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Tromsø
institution Open Polar
collection University of Tromsø: Munin Open Research Archive
op_collection_id ftunivtroemsoe
language English
topic VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Mathematics and natural science: 400::Physics: 430
EOM-3901
spellingShingle VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Mathematics and natural science: 400::Physics: 430
EOM-3901
Pedersen, Jarle Langseth
Automatic validation of Sentinel-1 borne snow avalanche detections
topic_facet VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430
VDP::Mathematics and natural science: 400::Physics: 430
EOM-3901
description Snow avalanches threaten human lives, settlements and roads in snow covered mountainous areas. For avalanche forecasting, knowledge of the spatio-temporal occurrence of avalanche activity is critical. Automatic avalanche detection algorithms have been developed to enable consistent avalanche activity monitoring for large regions. The Satskred avalanche detection algorithm developed by NORCE applies synthetic aperture radar (SAR) data from the Sentinel-1 satellite constellation and detects avalanches through a relative increase in energy scattered back to the radar from avalanche debris. Field validation of all automatically detected features is desirable, but not achievable due to weather- , light- , and avalanche danger-conditions as well as avalanches occurring at remote locations. In this thesis, an algorithm is presented for automatic comparison of the Satskred avalanche detections to crowd-sourced avalanche observations from regObs, the Norwegian public registry for snow-, weather-, flood-, and ice observations. Thereby, the validation set of field observed avalanches grows with every registered observation and validation of detected features can be performed without further manual intervention. To evaluate whether a detection matches an observed avalanche, the comparison algorithm initially filters detections by time period to ensure temporal similarity. Then, the detection is evaluated with regards to distance, slope aspect and membership of the same drainage basin region as the observation to ensure spatial similarity. If the detection fulfills all the similarity requirements, it is considered to likely represent the same avalanche. Studying a 120 x 86 km area centered over Tromsø in Northern Norway, 308 avalanche observations from 2014 - 2019 were automatically compared to a set of avalanche detections from the same area and time period. The field observations were used as a truth-set and the resulting probability of detection (POD) for the Satskred algorithm was 25.3% (78 out of 308). Further analysis identified trends of larger POD for wet- than dry avalanches, and an increasing POD with avalanche size. A large proportion of avalanches entered to the regObs database are dry slab avalanches, which was found to partly explain the low POD.
format Master Thesis
author Pedersen, Jarle Langseth
author_facet Pedersen, Jarle Langseth
author_sort Pedersen, Jarle Langseth
title Automatic validation of Sentinel-1 borne snow avalanche detections
title_short Automatic validation of Sentinel-1 borne snow avalanche detections
title_full Automatic validation of Sentinel-1 borne snow avalanche detections
title_fullStr Automatic validation of Sentinel-1 borne snow avalanche detections
title_full_unstemmed Automatic validation of Sentinel-1 borne snow avalanche detections
title_sort automatic validation of sentinel-1 borne snow avalanche detections
publisher UiT The Arctic University of Norway
publishDate 2020
url https://hdl.handle.net/10037/18985
long_lat ENVELOPE(73.317,73.317,-52.983,-52.983)
geographic Norway
The Sentinel
Tromsø
geographic_facet Norway
The Sentinel
Tromsø
genre Northern Norway
Tromsø
genre_facet Northern Norway
Tromsø
op_relation https://hdl.handle.net/10037/18985
op_rights openAccess
Copyright 2020 The Author(s)
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