Trends and projections from annual forest inventory plots and coarsened exact matching
キーワード:
Forest inventory、 Moving average、 Inventory projection、 FIA data要旨
The coarsened exact matching (CEM) method is used to match annual forest inventory plots awaiting remeasurement with plots that have already been remeasured. This results in a model-free approach for short term inventory projections. CEM has many desirable properties relative to other matching methods and is easy to apply within a SQL database. The combination of short term projections with a 3 or 5 year moving window is suggested for providing trend estimates that include the current year and a few years into the future. The default projection represents business as usual. A method to bias the plot matching to generate desired scenarios is also developed. These ideas and methods are demonstrated with several applications to forest inventory data. Scenarios are generated where increasing future harvest levels are stochastically controlled to demonstrate this capability with operational data.参考文献
Bechtold, W. A., and P.L. Patterson. Editors. 2005. The enhanced forest inventory and analysis program - national sampling design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p.
Dehejia, R.H., and S. Wahba. 2002. Propensity score-matching methods for nonexperimental causal studies. Rev. Econ. Stat. 84(1): 151-161.
Fernandez, C.A., and R. Astrup. 2012. Empirical harvest models and their use in regional business-as-usual scenarios of timber supply and carbon stock development. Scand. J. For. Res. 27:379-392.
Iacus, S.M., G. King, and G. Porro. 2011a. Multivariate matching methods that are Monotonic Imbalance Bounding. J. Am. Stat. Assoc. 106(493): 345-361.
Iacus, S.M., G. King, and G. Porro. 2011b. Causal Inference without Balance Checking: Coarsened Exact Matching. Polit. Anal. doi: 10.1093.
King, G., R. Nielsen, C. Coberley, J.E. Pope, and A. Wells. 2011. Comparative Effectiveness of Matching Methods for Causal Inference. Working Paper, 2011. Available online at http://j.mp/jCpWmk; last accessed Feb. 25, 2013.
McRoberts, R.E. 2001. Imputation and model-based updating techniques for annual forest inventories. For. Sci. 47: 322-330.
R Development Core Team 2010. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, Available online at http://www.R-project.org.
Reams, G.A., and J.M. McCollum. 2000. The use of multiple imputation in the southern annual forest inventory system. In Integrated Tools for Natural Resource Inventories, Proceedings of IUFRO Conference. Edited by M.H. Hansen and T. Burk. USDA For. Serv. Gen. Tech. Rep. NC-212. pp. 228-233.
Reams, G.A. and P.C. Van Deusen. 1999. The Southern Annual Forest Inventory System. J. Agric. Biol. Environ. Stat. 4(4): 346-360.
Roesch, F.A., J.R. Steinman, and M.T. Thompson. 2003. Annual forest inventory estimates based on the moving average. P. 21-30 in Proc. third annual forest inventory and analysis symp. October 17-19 2001, Traverse City, Michigan. McRoberts, R.E., G.A. Reams, P.C. Van Deusen, and J.W. Moser (eds.). Gen. Tech. Rep. NC-230, USDA For. Serv., North Central Res. Stn., St. Paul, MN. 208 p.
Rosenbaum, P.R., and D.B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70(1): 41-55.
Rubin, D.B. 1987. Multiple imputation for nonresponse in surveys. John Wiley & Sons, Inc., New York, New York, USA. 258 p.
Rubin, D.B. 2001. Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Serv. Outcomes Res. Methodol. 2:169-188.
Sande, I.G. 1983. Hot-deck imputation procedures. In: Madow WG, Olkin I, editors. Incomplete data in Sample Surveys, vol. 3. New York: Academic Press: 334-50.
Van Deusen, P.C. 1997. Annual forest inventory statistical concepts with emphasis on multiple imputation. Can. J. For. Res. 27: 379-384.
Van Deusen, P.C. 1999. Modeling trends with annual survey data. Can. J. For. Res. 29: 1824-1828.
Van Deusen, P.C. 2010. Carbon sequestration potential of forest land: Management for products and bioenergy versus preservation. Biomass Bioenergy 34: 1687-1694.
Wear, D.N. 2011. Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS-141. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 41 p.
Woudenberg, S.W., B.L. Conkling, B.M. O'Conell, E.B. LaPoint, J.A. Turner, and K.L. Waddell. 2010. The Forest Inventory and Analysis Database: Database description and users manual version 4.0 for Phase 2. USDA For. Serv. Gen. Tech. Rep. RMRS-245. 336 p.
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