REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS

High latitude rivers across the pan-Arctic domain are changing due to changes in climate and positive Arctic feedback loops. Understanding and contextualizing these changes is challenging due to a lack of data and methods for estimating and modeling river discharge, and mapping rivers. Remote sensin...

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Main Author: Harlan, Merritt E
Format: Text
Language:unknown
Published: ScholarWorks@UMass Amherst 2022
Subjects:
Online Access:https://scholarworks.umass.edu/dissertations_2/2542
https://doi.org/10.7275/28300904
https://scholarworks.umass.edu/context/dissertations_2/article/3572/viewcontent/Harlan_Dissertation_Revised.pdf
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spelling ftunivmassamh:oai:scholarworks.umass.edu:dissertations_2-3572 2024-04-28T08:09:01+00:00 REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS Harlan, Merritt E 2022-06-16T17:00:38Z application/pdf https://scholarworks.umass.edu/dissertations_2/2542 https://doi.org/10.7275/28300904 https://scholarworks.umass.edu/context/dissertations_2/article/3572/viewcontent/Harlan_Dissertation_Revised.pdf unknown ScholarWorks@UMass Amherst https://scholarworks.umass.edu/dissertations_2/2542 doi:10.7275/28300904 https://scholarworks.umass.edu/context/dissertations_2/article/3572/viewcontent/Harlan_Dissertation_Revised.pdf http://creativecommons.org/licenses/by/4.0/ Doctoral Dissertations high latitude river discharge remote sensing satellite imagery hydrologic models deep learning Civil and Environmental Engineering Hydrology text 2022 ftunivmassamh https://doi.org/10.7275/28300904 2024-04-03T14:49:41Z High latitude rivers across the pan-Arctic domain are changing due to changes in climate and positive Arctic feedback loops. Understanding and contextualizing these changes is challenging due to a lack of data and methods for estimating and modeling river discharge, and mapping rivers. Remote sensing, and the availability of satellite imagery can provide ways to overcome these challenges. Through combining various forms of fieldwork, modeling, deep learning, and remote sensing, we contribute methodologies and knowledge to three key challenges associated with better understanding high latitude rivers. In the first chapter, we combine field data that can be rapidly deployed with remote sensing discharge algorithms to estimate river discharge in a field setting that has the potential to outperform traditional discharge estimation techniques. In the second chapter, we combine high resolution satellite imagery with a deep learning approach to map an important yet understudied type of small tundra stream, a beaded stream. The third chapter combines remotely sensed discharge estimates with gauge data to improve hydrologic model calibration. The outcomes of this work contribute important advancements towards improving our understanding of high latitude rivers. Text Arctic Tundra University of Massachusetts: ScholarWorks@UMass Amherst
institution Open Polar
collection University of Massachusetts: ScholarWorks@UMass Amherst
op_collection_id ftunivmassamh
language unknown
topic high latitude
river discharge
remote sensing
satellite imagery
hydrologic models
deep learning
Civil and Environmental Engineering
Hydrology
spellingShingle high latitude
river discharge
remote sensing
satellite imagery
hydrologic models
deep learning
Civil and Environmental Engineering
Hydrology
Harlan, Merritt E
REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
topic_facet high latitude
river discharge
remote sensing
satellite imagery
hydrologic models
deep learning
Civil and Environmental Engineering
Hydrology
description High latitude rivers across the pan-Arctic domain are changing due to changes in climate and positive Arctic feedback loops. Understanding and contextualizing these changes is challenging due to a lack of data and methods for estimating and modeling river discharge, and mapping rivers. Remote sensing, and the availability of satellite imagery can provide ways to overcome these challenges. Through combining various forms of fieldwork, modeling, deep learning, and remote sensing, we contribute methodologies and knowledge to three key challenges associated with better understanding high latitude rivers. In the first chapter, we combine field data that can be rapidly deployed with remote sensing discharge algorithms to estimate river discharge in a field setting that has the potential to outperform traditional discharge estimation techniques. In the second chapter, we combine high resolution satellite imagery with a deep learning approach to map an important yet understudied type of small tundra stream, a beaded stream. The third chapter combines remotely sensed discharge estimates with gauge data to improve hydrologic model calibration. The outcomes of this work contribute important advancements towards improving our understanding of high latitude rivers.
format Text
author Harlan, Merritt E
author_facet Harlan, Merritt E
author_sort Harlan, Merritt E
title REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
title_short REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
title_full REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
title_fullStr REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
title_full_unstemmed REMOTE SENSING OF HIGH LATITUDE RIVERS: APPROACHES, INSIGHTS, AND FUTURE RAMIFICATIONS
title_sort remote sensing of high latitude rivers: approaches, insights, and future ramifications
publisher ScholarWorks@UMass Amherst
publishDate 2022
url https://scholarworks.umass.edu/dissertations_2/2542
https://doi.org/10.7275/28300904
https://scholarworks.umass.edu/context/dissertations_2/article/3572/viewcontent/Harlan_Dissertation_Revised.pdf
genre Arctic
Tundra
genre_facet Arctic
Tundra
op_source Doctoral Dissertations
op_relation https://scholarworks.umass.edu/dissertations_2/2542
doi:10.7275/28300904
https://scholarworks.umass.edu/context/dissertations_2/article/3572/viewcontent/Harlan_Dissertation_Revised.pdf
op_rights http://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.7275/28300904
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