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Using landsat time-series and lidar to inform aboveground carbon baseline estimation in MinnesotaAuthor(s): Ram K. Deo; Grant M. Domke; Matthew B. Russell; Christopher W. Woodall; Michael J. Falkowski
Source: In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 104-109.
Publication Series: General Technical Report (GTR)
Station: Pacific Northwest Research Station
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DescriptionLandsat data has long been used to support forest monitoring and management decisions despite the limited success of passive optical remote sensing for accurate estimation of structural attributes such as aboveground biomass. The archive of publicly available Landsat images dating back to the 1970s can be used to predict historic forest biomass dynamics. In addition, increasing regional scale availability and high sensitivity of LiDAR for biomass mapping also needs exploration of its utility in back-projection modeling. This study has combined recent national forest inventory (NFI) data (2007-2011) with the Landsat data from 1986-2011 and a regional LiDAR dataset acquired by the Minnesota Department of Natural Resources (DNR) to assess the potential of the remote sensing data in predicting aboveground forest biomass back to the 1990 baseline used in the United Nations Framework Convention on Climate Change reporting in the US. Since obtaining cloud-free Landsat images at required seasons for a regional or national study is unlikely, pixel level polynomial models were fitted to a suite of time-series predictors obtained from cloud-free Landsat data of a single scene in Minnesota such that each predictor represented only one growing season between 1986 and 2011. Similarly, selected LiDAR variables were back-projected using Landsat metrics as explanatory variables. The rational for this effort was to obtain a wall-to-wall inventory for any target year that does not have remote sensing data by combining a set of projected predictors and current NFI data. Several candidate models were developed to produce biomass maps for the year 2000 to compare the outputs with the extant map of National Biomass and Carbon Dataset (NBCD) circa 2000 and annual NFI plot measurements. We found that the model including back-projected LiDAR metrics did not significantly improve the prediction accuracy as compared to the model based only on projected Landsat metrics. As the polynomial-projected Landsat-based model provided accuracy similar to the NBCD model, the former may be used for reference mapping back to 1990.
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CitationDeo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Falkowski, Michael J. 2015. Using landsat time-series and lidar to inform aboveground carbon baseline estimation in Minnesota. In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 104-109.
- Integrating field plots, lidar, and landsat time series to provide temporally consistent annual estimates of biomass from 1990 to present
- Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US
- Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales
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