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Open AccessArticle

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

1
Department of Land Resources & Environmental Sciences, Montana State University, PO Box 173120, Bozeman, MT 59717, USA
2
USDA Forest Service, Rocky Mountain Research Station, 800 E. Beckwith, Missoula, MT 59801, USA
3
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR 97331, USA
4
USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
*
Author to whom correspondence should be addressed.
Forests 2018, 9(4), 157; https://doi.org/10.3390/f9040157
Received: 15 February 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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. View Full-Text
Keywords: remote sensing; forest structure classification; Landsat satellite imagery; time series; change vector analysis; temporal continuity remote sensing; forest structure classification; Landsat satellite imagery; time series; change vector analysis; temporal continuity
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Savage, S.L.; Lawrence, R.L.; Squires, J.R.; Holbrook, J.D.; Olson, L.E.; Braaten, J.D.; Cohen, W.B. Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager. Forests 2018, 9, 157.

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