Examination of imputation methods to estimate status and change of forest attributes from paneled inventory data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/np193d125

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  • The Forest Inventory and Analysis (FIA) program conducts an annual inventory throughout the United States. In the western United States, 10% of all plots (one panel) are measured annually, and a moving average is used for estimating current condition and change of forest attributes while alternative methods are sought in all regions of the United States. This dissertation explored alternatives to the moving average in the Pacific Northwest using Current Vegetation Survey data collected in Oregon and Washington. Several nearest neighbor imputation methods were examined for their suitability to update plot-level forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha) to the current point in time. The results were compared to estimates obtained using a moving average and a weighted moving average. In terms of bias and accuracy, the weighted moving average performed better than the moving average. When the most recent measurements of the variables of interest were used as ancillary data, randomForest imputation outperformed both the moving average and the weighted moving average. For estimating current basal area/ha, stems/ha, volume/ha, and biomass/ha, tree-level imputation outperformed plot-level imputation. The difference in bias and accuracy between tree- and plot-level imputation was more pronounced when the variables of interest were summarized by species groups. Nearest neighbor imputation methods were also investigated for estimating mean annual change in selected forest attributes. The imputed mean annual change was used to update unmeasured panels to the current point in time. In terms of bias and accuracy, the resulting estimates of current basal area/ha, stems/ha, volume/ha, and biomass/ha outperformed the results obtained using plot-level imputation. Information on hard to estimate forest attributes such as cavity tree and snag abundance are important for wildlife management plans. Using FIA data collected in Washington, Oregon, and California, nearest neighbor imputation approaches and negative binomial regression models were examined for their suitability in estimating cavity tree and snag abundance. The negative binomial models were preferred to the nearest neighbor imputation approaches.
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