Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager

There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of t...

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Main Authors: Shannon L. Savage, Rick L. Lawrence, John R. Squires, Joseph D. Holbrook, Lucretia E. Olson, Justin D. Braaten, Warren B. Cohen
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Forests
Subjects:
Online Access:http://www.mdpi.com/1999-4907/9/4/157
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spelling doaj-8fa91285f98246dfa0538ab4e2a685192020-11-24T23:48:50ZengMDPI AGForests1999-49072018-03-019415710.3390/f9040157f9040157Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land ImagerShannon L. Savage0Rick L. Lawrence1John R. Squires2Joseph D. Holbrook3Lucretia E. Olson4Justin D. Braaten5Warren B. Cohen6Department of Land Resources & Environmental Sciences, Montana State University, PO Box 173120, Bozeman, MT 59717, USADepartment of Land Resources & Environmental Sciences, Montana State University, PO Box 173120, Bozeman, MT 59717, USAUSDA Forest Service, Rocky Mountain Research Station, 800 E. Beckwith, Missoula, MT 59801, USADepartment of Land Resources & Environmental Sciences, Montana State University, PO Box 173120, Bozeman, MT 59717, USAUSDA Forest Service, Rocky Mountain Research Station, 800 E. Beckwith, Missoula, MT 59801, USACollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR 97331, USAUSDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USAThere is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.http://www.mdpi.com/1999-4907/9/4/157remote sensingforest structure classificationLandsat satellite imagerytime serieschange vector analysistemporal continuity
collection DOAJ
language English
format Article
sources DOAJ
author Shannon L. Savage
Rick L. Lawrence
John R. Squires
Joseph D. Holbrook
Lucretia E. Olson
Justin D. Braaten
Warren B. Cohen
spellingShingle Shannon L. Savage
Rick L. Lawrence
John R. Squires
Joseph D. Holbrook
Lucretia E. Olson
Justin D. Braaten
Warren B. Cohen
Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
Forests
remote sensing
forest structure classification
Landsat satellite imagery
time series
change vector analysis
temporal continuity
author_facet Shannon L. Savage
Rick L. Lawrence
John R. Squires
Joseph D. Holbrook
Lucretia E. Olson
Justin D. Braaten
Warren B. Cohen
author_sort Shannon L. Savage
title Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
title_short Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
title_full Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
title_fullStr Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
title_full_unstemmed Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
title_sort shifts in forest structure in northwest montana from 1972 to 2015 using the landsat archive from multispectral scanner to operational land imager
publisher MDPI AG
series Forests
issn 1999-4907
publishDate 2018-03-01
description There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future.
topic remote sensing
forest structure classification
Landsat satellite imagery
time series
change vector analysis
temporal continuity
url http://www.mdpi.com/1999-4907/9/4/157
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