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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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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1809897123428171776 |