Detecting ice jams on the rivers in northern Finland using Sentinel-1

Ice jams related flooding causes tremendous loss both physically and economically in society by destroying habitats, damaging the infrastructures, water supply, navigation. Hence, it’s of great importance to account this phenomenon. Ice jam study using Synthetic Aperture Radar (SAR) data has gained...

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Main Author: Marthandavilakom Prakasam, Golda
Other Authors: Luojus, Kari, Insinööritieteiden korkeakoulu, Rautiainen, Miina, Aalto-yliopisto, Aalto University
Format: Master Thesis
Language:English
Published: 2022
Subjects:
SAR
Online Access:https://aaltodoc.aalto.fi/handle/123456789/118376
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record_format openpolar
spelling ftaaltouniv:oai:aaltodoc.aalto.fi:123456789/118376 2023-05-15T17:42:41+02:00 Detecting ice jams on the rivers in northern Finland using Sentinel-1 Marthandavilakom Prakasam, Golda Luojus, Kari Insinööritieteiden korkeakoulu Rautiainen, Miina Aalto-yliopisto Aalto University 2022-12-12 application/pdf https://aaltodoc.aalto.fi/handle/123456789/118376 en eng https://aaltodoc.aalto.fi/handle/123456789/118376 URN:NBN:fi:aalto-202212187118 ice jam Sentinel-1 synthetic aperture radar ground range detected SAR remote sensing G2 Pro gradu, diplomityö Master's thesis Diplomityö 2022 ftaaltouniv 2022-12-21T23:57:26Z Ice jams related flooding causes tremendous loss both physically and economically in society by destroying habitats, damaging the infrastructures, water supply, navigation. Hence, it’s of great importance to account this phenomenon. Ice jam study using Synthetic Aperture Radar (SAR) data has gained demand, however only a few studies came out using Sentinel-1 data, especially with vertical transmit & vertical receive (VV) and vertical transmit & horizontal transmit (VH) backscatter polarization for detecting ice jams. This study aims to put forward an automatic algorithm to detect the ice jams using Sentinel-1 backscattering intensities VV and VH for the first time in Kemijoki River system in Finland. 10 days average mean mosaics of Sentinel-1 Ground Range Detected (GRD) products were used as input. The algorithm was successful in detecting ice and ice jams during the study period from December to March between 2018 to 2021. VH backscatter polarization gave the best results in delineating the ice jam. To quantify the results, River Lake Ice Extent product based on Sentinel-2 and for visual validation RGB (red, green, blue) composites of Sentinel-2 Mosaics have been used. Visual validation was the most trusted in this method. The recall and precision were above 70 percent. Cloud cover and lack of good in-situ reference data was also a draw back for the validation of result. The potential ice jam detected in Rovaniemi is opening the wider possibility for applying similar technique for finding ice jams for the entire Finland. Master Thesis Northern Finland Rovaniemi Aalto University Publication Archive (Aaltodoc) Kemijoki ENVELOPE(24.500,24.500,65.783,65.783) Rovaniemi ENVELOPE(26.159,26.159,66.392,66.392)
institution Open Polar
collection Aalto University Publication Archive (Aaltodoc)
op_collection_id ftaaltouniv
language English
topic ice jam
Sentinel-1
synthetic aperture radar
ground range detected
SAR
remote sensing
spellingShingle ice jam
Sentinel-1
synthetic aperture radar
ground range detected
SAR
remote sensing
Marthandavilakom Prakasam, Golda
Detecting ice jams on the rivers in northern Finland using Sentinel-1
topic_facet ice jam
Sentinel-1
synthetic aperture radar
ground range detected
SAR
remote sensing
description Ice jams related flooding causes tremendous loss both physically and economically in society by destroying habitats, damaging the infrastructures, water supply, navigation. Hence, it’s of great importance to account this phenomenon. Ice jam study using Synthetic Aperture Radar (SAR) data has gained demand, however only a few studies came out using Sentinel-1 data, especially with vertical transmit & vertical receive (VV) and vertical transmit & horizontal transmit (VH) backscatter polarization for detecting ice jams. This study aims to put forward an automatic algorithm to detect the ice jams using Sentinel-1 backscattering intensities VV and VH for the first time in Kemijoki River system in Finland. 10 days average mean mosaics of Sentinel-1 Ground Range Detected (GRD) products were used as input. The algorithm was successful in detecting ice and ice jams during the study period from December to March between 2018 to 2021. VH backscatter polarization gave the best results in delineating the ice jam. To quantify the results, River Lake Ice Extent product based on Sentinel-2 and for visual validation RGB (red, green, blue) composites of Sentinel-2 Mosaics have been used. Visual validation was the most trusted in this method. The recall and precision were above 70 percent. Cloud cover and lack of good in-situ reference data was also a draw back for the validation of result. The potential ice jam detected in Rovaniemi is opening the wider possibility for applying similar technique for finding ice jams for the entire Finland.
author2 Luojus, Kari
Insinööritieteiden korkeakoulu
Rautiainen, Miina
Aalto-yliopisto
Aalto University
format Master Thesis
author Marthandavilakom Prakasam, Golda
author_facet Marthandavilakom Prakasam, Golda
author_sort Marthandavilakom Prakasam, Golda
title Detecting ice jams on the rivers in northern Finland using Sentinel-1
title_short Detecting ice jams on the rivers in northern Finland using Sentinel-1
title_full Detecting ice jams on the rivers in northern Finland using Sentinel-1
title_fullStr Detecting ice jams on the rivers in northern Finland using Sentinel-1
title_full_unstemmed Detecting ice jams on the rivers in northern Finland using Sentinel-1
title_sort detecting ice jams on the rivers in northern finland using sentinel-1
publishDate 2022
url https://aaltodoc.aalto.fi/handle/123456789/118376
long_lat ENVELOPE(24.500,24.500,65.783,65.783)
ENVELOPE(26.159,26.159,66.392,66.392)
geographic Kemijoki
Rovaniemi
geographic_facet Kemijoki
Rovaniemi
genre Northern Finland
Rovaniemi
genre_facet Northern Finland
Rovaniemi
op_relation https://aaltodoc.aalto.fi/handle/123456789/118376
URN:NBN:fi:aalto-202212187118
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