Carlos Ramirez Scott Conway Kama Kennedy Tanya Kohler Jinxun Liu Shengli Huang 2016 <p><span>High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (</span><i>i</i><span>) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (</span><i>ii</i><span>) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (</span><i>iii</i><span>) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an </span><i>R</i>²<span> of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an </span><i>R</i>²<span> of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m</span>³<span>·ha</span>⁻¹<span>·year</span>⁻¹<span>. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.</span></p> application/pdf 10.1139/cjfr-2016-0209 en NRC Research Press Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA article