Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their tr...
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ftmdpi:oai:mdpi.com:/2072-4292/6/11/11558/ 2023-08-20T04:04:17+02:00 Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification Ian Olthof Robert Fraser agris 2014-11-20 application/pdf https://doi.org/10.3390/rs61111558 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs61111558 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 6; Issue 11; Pages: 11558-11578 Landsat arctic time-series change profile matching trend regression Text 2014 ftmdpi https://doi.org/10.3390/rs61111558 2023-07-31T20:40:21Z Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes. Text Arctic Climate change Mackenzie Delta MDPI Open Access Publishing Arctic Mackenzie Delta ENVELOPE(-136.672,-136.672,68.833,68.833) Remote Sensing 6 11 11558 11578 |
institution |
Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Landsat arctic time-series change profile matching trend regression |
spellingShingle |
Landsat arctic time-series change profile matching trend regression Ian Olthof Robert Fraser Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
topic_facet |
Landsat arctic time-series change profile matching trend regression |
description |
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes. |
format |
Text |
author |
Ian Olthof Robert Fraser |
author_facet |
Ian Olthof Robert Fraser |
author_sort |
Ian Olthof |
title |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
title_short |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
title_full |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
title_fullStr |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
title_full_unstemmed |
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification |
title_sort |
detecting landscape changes in high latitude environments using landsat trend analysis: 2. classification |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2014 |
url |
https://doi.org/10.3390/rs61111558 |
op_coverage |
agris |
long_lat |
ENVELOPE(-136.672,-136.672,68.833,68.833) |
geographic |
Arctic Mackenzie Delta |
geographic_facet |
Arctic Mackenzie Delta |
genre |
Arctic Climate change Mackenzie Delta |
genre_facet |
Arctic Climate change Mackenzie Delta |
op_source |
Remote Sensing; Volume 6; Issue 11; Pages: 11558-11578 |
op_relation |
https://dx.doi.org/10.3390/rs61111558 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs61111558 |
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Remote Sensing |
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6 |
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11 |
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