Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics |
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| Institution: | 1. Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland;2. Biological Problems of the Cryolithozone, Russian Academy of Sciences, Siberian Division, 41 Lenin Prospekt, Yakutsk, Yakutia 677980, Russian Federation;1. School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA;2. Forestry Department, Michigan State University, East Lansing, MI 48824, USA;3. Code 618, Biospheric Sciences Branch, NASA/Goddard Space Flight Center, Greenbelt, MD 20742, USA;4. School of Forest Resources, University of Maine, Orono, ME 04469, USA;5. USDA Forest Service, Northern Research Station, Forest Inventory and Analysis Program, 1992 Folwell Avenue, Saint Paul, MN 55114, USA;1. Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;2. Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada;3. Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany |
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| Abstract: | Canada is dominated by forested ecosystems which are subject to various inventory and management practices, with more northern boreal forests subject to neither. Our objectives were to measure the capacity of temporal trajectory metrics for estimating selected forest attributes in a northern Canadian boreal forest context using Landsat imagery and investigate the importance of different types of temporal trajectory metrics. Results indicated that Wetness was the best Tasseled Cap (TC) component for aboveground biomass estimation (R2 = 50%, RMSE% = 56%), and the combination of simple and complex metrics from all TC components produced the highest R2 (62%) and lowest RMSE% (49%). Using a similar combination of variables, other forest attributes were estimated equally reliably with lower RMSE% values. The most important temporal trajectory metrics were simple and described TC component values at each point of change in the temporal trajectory, however the most important variables overall were environmental variables. |
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| Keywords: | Landsat Multitemporal LiDAR Forest Random forests Boreal Canada |
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