Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA

Abstract

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 ( i ) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, ( ii ) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and ( iii ) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R 2 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 R 2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m 3 ·ha −1 ·year −1 . 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.