Year of Award
Doctor of Philosophy (PhD)
Forest and Conservation Science
Department or School/College
W.A. Franke College of Forestry and Conservation
C. Kenneth Brewer, Jon Graham, Christopher Keyes, Carl Seielstad
bias estimation, forest inventory, forest regeneration, monitoring trends in burn severity, small area estimation, wildland fire
University of Montana
Wildfire activity in the western United States is expanding and concern for the declining extent of postfire tree cover in many western forests is mounting. Accurate estimates of postfire seedling, sapling, and large tree density following wildfire are critical for postfire forest management planning and monitoring forest dynamics. National forest inventory programs, such as the US Forest Service Forest Inventory and Analysis (FIA) program, can provide vegetation data for direct spatiotemporal domain estimation of postfire tree density, but sample observations within domains of administrative utility are often few to none. This research investigates indirect domain estimators, which borrow sample data from outside the domain to increase precision of domain estimates. Domains consist of National Forest System (NFS) lands burned in a particular US state and over a particular burn period, at varied times–since–burn. On the basis of estimated standard error, a strategy for augmenting domain samples with observations proximate in time proves superior to a strategy that borrows observations proximate in space when using FIA sample data alone. However, estimators of the mean squared error (MSE) of indirect domain estimators prove frequently negative and too highly variable for operational utility in this context. Relationships are therefore explored between observations of postfire tree density and a broad suite of geospatial explanatory variables in efforts to reveal trends and identify candidate auxiliary variables for model–assisted domain estimation. Algorithmic and parametric modeling techniques identify a multispectral satellite-based tree cover product and climate variables as the most important predictors. Yet poor overall performance suggests that a single model of tree regeneration throughout the entire western US is not feasible. Finally, model–assisted small area estimators are compared in a design–based inferential framework. In particular, k nearest neighbor–based (kNN) and linear regression–based small area estimators are developed and compared on the basis of domain–level standard error in domains spanning burned NFS lands within individual US states and decades. A kNN–based technique using only spatial coordinates as predictors yields the lowest standard errors at the domain level, indicating that none of the model–assisted approaches investigated here could be leveraged to better effect than to simply average the nearest observations in space, irrespective of time–since–disturbance.
Gaines, George Chilton III, "SMALL AREA ESTIMATION OF POSTFIRE TREE DENSITY IN THE WESTERN UNITED STATES USING AN ANNUALIZED FOREST INVENTORY" (2022). Graduate Student Theses, Dissertations, & Professional Papers. 11819.
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