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 R2 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 R2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m3·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.

Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests

“…Researchers often integrate field inventory data with coarseresolution imagery through imputation models to map detailed forest attributes over large areas (Beaudoin et al 2014;Blackard et al 2008;Huang et al 2017;Wilson et al 2012;Zhang et al 2014). One widely used approach is k nearest neighbor (kNN) imputation (Tomppo et al 2008;Zald et al 2014), which uses a set of predictor variables (x) to determine a number (k) of most similar reference observations (nearest neighbors or NN) to derive response variables (y) for the target pixel (Crookston and Finley 2008;McRoberts 2012;Ohmann et al 2011).…”

Quantifying Spatiotemporal Post‐Disturbance Recovery Using Field Inventory, Tree Growth, and Remote Sensing

“…For soil, productivity classes were compiled from the Soil Survey Geographic (SSURGO) database. These data sets were described in Huang, Ramirez, Conway, et al (2016) and Huang et al (2018).…”

“…As outlined in previous studies (Huang, Ramirez, Conway, et al, 2016;Huang et al, 2018), remote sensing images must be sensitive and correlated to both pre-and postdisturbance forest conditions. Different types of disturbances can result in considerable variation in terms of internal patterns of mortality and the amount of biological material that can contribute to ecosystem recovery (Foster et al, 1998).…”

Prediction of the Site Index for a Subtropical Broad-Leaved Forest on Okinawa Island Using Topographic Factor

“…Moreover, several studies have reported site indices for prediction and mapping based on a limited number of factors that can be easily derived from remote sensing data, such as Landsat (Günlü et al, 2008;Huang et al, 2017) Detection And Ranging (LiDAR) (Laamrani et al, 2014). Spatially continuous maps are needed for tactical planning and operations for forest management;…”

“…therefore, high-resolution site index maps are very powerful tools for local forest management (Huang et al, 2017). In Japan, it has become possible to estimate the site index using topographic factors derived from DEM, especially for conifer plantations of trees, such as the Japanese larch (Larix kaempferi) (Mitsuda et al, 2001), Japanese cedar (Cryptomeria japonica) (Zushi, 2006;Mitsuda et al, 2007).…”