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The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias
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