Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation

Salmon populations are highly variable in both space and time. Accurate forecasting of the productivity of salmon stocks makes effective management and conservation of the resource extremely challenging. Furthermore, widespread and consistent data on the productivity of species-specific and total sa...

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Published in:Earth Surface Processes and Landforms
Main Authors: Luck, Matthew, Maumenee, Niels, Whited, Diane C, Lucotch, J., Chilcote, Samantha D., Lorang, Mark S., Goodman, Daniel, McDonald, Kyle C., Kimball, John S, Stanford, Jack A.
Format: Text
Language:unknown
Published: ScholarWorks at University of Montana 2010
Subjects:
Online Access:https://scholarworks.umt.edu/ntsg_pubs/213
https://doi.org/10.1002/esp.2044
id ftunivmontana:oai:scholarworks.umt.edu:ntsg_pubs-1212
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spelling ftunivmontana:oai:scholarworks.umt.edu:ntsg_pubs-1212 2024-09-09T19:27:45+00:00 Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation Luck, Matthew Maumenee, Niels Whited, Diane C Lucotch, J. Chilcote, Samantha D. Lorang, Mark S. Goodman, Daniel McDonald, Kyle C. Kimball, John S Stanford, Jack A. 2010-09-01T07:00:00Z application/pdf https://scholarworks.umt.edu/ntsg_pubs/213 https://doi.org/10.1002/esp.2044 unknown ScholarWorks at University of Montana https://scholarworks.umt.edu/ntsg_pubs/213 doi:10.1002/esp.2044 © 2010 John Wiley & Sons, Ltd. Numerical Terradynamic Simulation Group Publications geomorphology physical complexity ranking rivers salmon text 2010 ftunivmontana https://doi.org/10.1002/esp.2044 2024-06-20T05:32:53Z Salmon populations are highly variable in both space and time. Accurate forecasting of the productivity of salmon stocks makes effective management and conservation of the resource extremely challenging. Furthermore, widespread and consistent data on the productivity of species-specific and total salmon stocks in a river are almost nonexistent. Ranking rivers based on physical complexity derived from remote sensing allows rivers to be objectively compared. Our approach considered rivers with great geomorphic complexity (e.g. having expansive, multichanneled floodplains and/or on-channel lakes) as likely to have greater productivity of salmon than rivers flowing in constrained or canyon-bound channels. Our objective was to develop a database of landscape metrics that could be used to rank the rivers in relation to potential salmon productivity. We then examined the rankings in relation to existing empirical (monitoring) data describing productivity of salmon stocks. To extract the metrics for each river basin we used a digital elevation model and multispectral satellite imagery. We developed procedures to extract channel networks, floodplains, on-channel lakes and other catchment features; variables such as catchment area, channel elevation, main channel length, floodplain area, and density of hydrojunctions (nodes) were measured. We processed 1509 catchments in the North Pacific Rim including the Kamchatka Peninsula in Russia and western North America. Overall, catchments were most physically complex in western Kamchatka and western Alaska, and particularly on the Arctic North Slope of Alaska. We could not directly examine coherence between potential and measured productivity except for a few rivers, but the expected relationship generally held. The resulting database and systematic ranking are objective tools that can be used to address questions about landscape structure and biological productivity at regional to continental extents, and provide a way to begin to efficiently prioritize the allocation of ... Text Arctic Kamchatka Kamchatka Peninsula north slope Alaska University of Montana: ScholarWorks Arctic Pacific Kamchatka Peninsula ENVELOPE(160.000,160.000,56.000,56.000) Earth Surface Processes and Landforms 35 11 1330 1343
institution Open Polar
collection University of Montana: ScholarWorks
op_collection_id ftunivmontana
language unknown
topic geomorphology
physical complexity
ranking
rivers
salmon
spellingShingle geomorphology
physical complexity
ranking
rivers
salmon
Luck, Matthew
Maumenee, Niels
Whited, Diane C
Lucotch, J.
Chilcote, Samantha D.
Lorang, Mark S.
Goodman, Daniel
McDonald, Kyle C.
Kimball, John S
Stanford, Jack A.
Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
topic_facet geomorphology
physical complexity
ranking
rivers
salmon
description Salmon populations are highly variable in both space and time. Accurate forecasting of the productivity of salmon stocks makes effective management and conservation of the resource extremely challenging. Furthermore, widespread and consistent data on the productivity of species-specific and total salmon stocks in a river are almost nonexistent. Ranking rivers based on physical complexity derived from remote sensing allows rivers to be objectively compared. Our approach considered rivers with great geomorphic complexity (e.g. having expansive, multichanneled floodplains and/or on-channel lakes) as likely to have greater productivity of salmon than rivers flowing in constrained or canyon-bound channels. Our objective was to develop a database of landscape metrics that could be used to rank the rivers in relation to potential salmon productivity. We then examined the rankings in relation to existing empirical (monitoring) data describing productivity of salmon stocks. To extract the metrics for each river basin we used a digital elevation model and multispectral satellite imagery. We developed procedures to extract channel networks, floodplains, on-channel lakes and other catchment features; variables such as catchment area, channel elevation, main channel length, floodplain area, and density of hydrojunctions (nodes) were measured. We processed 1509 catchments in the North Pacific Rim including the Kamchatka Peninsula in Russia and western North America. Overall, catchments were most physically complex in western Kamchatka and western Alaska, and particularly on the Arctic North Slope of Alaska. We could not directly examine coherence between potential and measured productivity except for a few rivers, but the expected relationship generally held. The resulting database and systematic ranking are objective tools that can be used to address questions about landscape structure and biological productivity at regional to continental extents, and provide a way to begin to efficiently prioritize the allocation of ...
format Text
author Luck, Matthew
Maumenee, Niels
Whited, Diane C
Lucotch, J.
Chilcote, Samantha D.
Lorang, Mark S.
Goodman, Daniel
McDonald, Kyle C.
Kimball, John S
Stanford, Jack A.
author_facet Luck, Matthew
Maumenee, Niels
Whited, Diane C
Lucotch, J.
Chilcote, Samantha D.
Lorang, Mark S.
Goodman, Daniel
McDonald, Kyle C.
Kimball, John S
Stanford, Jack A.
author_sort Luck, Matthew
title Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
title_short Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
title_full Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
title_fullStr Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
title_full_unstemmed Remote sensing analysis of physical complexity of North Pacific Rim rivers to assist wild salmon conservation
title_sort remote sensing analysis of physical complexity of north pacific rim rivers to assist wild salmon conservation
publisher ScholarWorks at University of Montana
publishDate 2010
url https://scholarworks.umt.edu/ntsg_pubs/213
https://doi.org/10.1002/esp.2044
long_lat ENVELOPE(160.000,160.000,56.000,56.000)
geographic Arctic
Pacific
Kamchatka Peninsula
geographic_facet Arctic
Pacific
Kamchatka Peninsula
genre Arctic
Kamchatka
Kamchatka Peninsula
north slope
Alaska
genre_facet Arctic
Kamchatka
Kamchatka Peninsula
north slope
Alaska
op_source Numerical Terradynamic Simulation Group Publications
op_relation https://scholarworks.umt.edu/ntsg_pubs/213
doi:10.1002/esp.2044
op_rights © 2010 John Wiley & Sons, Ltd.
op_doi https://doi.org/10.1002/esp.2044
container_title Earth Surface Processes and Landforms
container_volume 35
container_issue 11
container_start_page 1330
op_container_end_page 1343
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