Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and f...
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ftuvicpubl:oai:dspace.library.uvic.ca:1828/12204 2023-05-15T15:15:25+02:00 Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization Fraser, Robert H. Olthof, Ian Kokelj, Steven V. Lantz, Trevor C. Lacelle, Denis Brooker, Alexander Wolfe, Stephen Schwarz, Steve 2014 application/pdf http://hdl.handle.net/1828/12204 https://doi.org/10.3390/rs61111533 en eng Remote Sensing Fraser, R. H., Olthof, I., Kokelj, S. V., Lantz, T. C., Lacelle, D., Brooker, A., Wolfe, S., & Schwarz, S. (2014). Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization. Remote Sensing, 6(11), 11533-11557. https://doi.org/10.3390/rs61111533. https://doi.org/10.3390/rs61111533 http://hdl.handle.net/1828/12204 arctic change detection image stacks disturbance lakes slumps fires environmental monitoring cumulative impacts Article 2014 ftuvicpubl https://doi.org/10.3390/rs61111533 2022-05-19T06:11:09Z Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes. We thank Marilee Pregitzer and Alice Deschamps for assistance with satellite image and GIS processing. Vern Singhroy and Christian Prevost from CCMEO and our anonymous reviewers offered helpful comments to improve the paper. The Polar Continental Shelf Program of Natural Resources Canada provided helicopter time from Great Slave Helicopters to acquire air photos. Funding for this work was provided by Natural Resources Canada’s TRACS project led by Stephen Wolfe and by the NWT Cumulative Impacts Monitoring Program under the projects “A Multi-scale Assessment of Cumulative Impacts in the Northern Mackenzie Basin” led by Claire Marchildon and “A watershed approach to monitoring cumulative impacts of ... Article in Journal/Newspaper Arctic Mackenzie Basin Northwest Territories Thermokarst Tundra University of Victoria (Canada): UVicDSpace Arctic Canada Northwest Territories Remote Sensing 6 11 11533 11557 |
institution |
Open Polar |
collection |
University of Victoria (Canada): UVicDSpace |
op_collection_id |
ftuvicpubl |
language |
English |
topic |
arctic change detection image stacks disturbance lakes slumps fires environmental monitoring cumulative impacts |
spellingShingle |
arctic change detection image stacks disturbance lakes slumps fires environmental monitoring cumulative impacts Fraser, Robert H. Olthof, Ian Kokelj, Steven V. Lantz, Trevor C. Lacelle, Denis Brooker, Alexander Wolfe, Stephen Schwarz, Steve Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
topic_facet |
arctic change detection image stacks disturbance lakes slumps fires environmental monitoring cumulative impacts |
description |
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes. We thank Marilee Pregitzer and Alice Deschamps for assistance with satellite image and GIS processing. Vern Singhroy and Christian Prevost from CCMEO and our anonymous reviewers offered helpful comments to improve the paper. The Polar Continental Shelf Program of Natural Resources Canada provided helicopter time from Great Slave Helicopters to acquire air photos. Funding for this work was provided by Natural Resources Canada’s TRACS project led by Stephen Wolfe and by the NWT Cumulative Impacts Monitoring Program under the projects “A Multi-scale Assessment of Cumulative Impacts in the Northern Mackenzie Basin” led by Claire Marchildon and “A watershed approach to monitoring cumulative impacts of ... |
format |
Article in Journal/Newspaper |
author |
Fraser, Robert H. Olthof, Ian Kokelj, Steven V. Lantz, Trevor C. Lacelle, Denis Brooker, Alexander Wolfe, Stephen Schwarz, Steve |
author_facet |
Fraser, Robert H. Olthof, Ian Kokelj, Steven V. Lantz, Trevor C. Lacelle, Denis Brooker, Alexander Wolfe, Stephen Schwarz, Steve |
author_sort |
Fraser, Robert H. |
title |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
title_short |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
title_full |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
title_fullStr |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
title_full_unstemmed |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization |
title_sort |
detecting landscape changes in high latitude environments using landsat trend analysis: 1. visualization |
publisher |
Remote Sensing |
publishDate |
2014 |
url |
http://hdl.handle.net/1828/12204 https://doi.org/10.3390/rs61111533 |
geographic |
Arctic Canada Northwest Territories |
geographic_facet |
Arctic Canada Northwest Territories |
genre |
Arctic Mackenzie Basin Northwest Territories Thermokarst Tundra |
genre_facet |
Arctic Mackenzie Basin Northwest Territories Thermokarst Tundra |
op_relation |
Fraser, R. H., Olthof, I., Kokelj, S. V., Lantz, T. C., Lacelle, D., Brooker, A., Wolfe, S., & Schwarz, S. (2014). Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization. Remote Sensing, 6(11), 11533-11557. https://doi.org/10.3390/rs61111533. https://doi.org/10.3390/rs61111533 http://hdl.handle.net/1828/12204 |
op_doi |
https://doi.org/10.3390/rs61111533 |
container_title |
Remote Sensing |
container_volume |
6 |
container_issue |
11 |
container_start_page |
11533 |
op_container_end_page |
11557 |
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1766345790983241728 |