The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. In Random Forests modeling, the most important variables were those derived from lidar.
Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. Models from Random Forests and linear regression were competitive with one another neither approach produced substantially better models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. 49(1):12-35.Īirborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. The equations developed here are used to compute the biomass estimates used by the model FORCARB to develop the U.S. This analysis represents the first major effort to compile and analyze all available biomass literature in a consistent national-scale framework. The comparison also shows that differences in equation forms and species groupings may cause differences at small scales depending on tree size and forest species composition. species suggests general agreement (☓0%) between biomass estimates. A comparison with recent equations used to develop large-scale biomass estimates from U.S. Equations for predicting biomass of tree components were developed as proportions of total aboveground biomass for hardwood and softwood groups. We then implemented a modified meta-analysis based on the published equations to develop a set of consistent, national-scale aboveground biomass regression equations for U.S. We compiled all available diameter-based allometric regression equations for estimating total aboveground and component biomass, defined in dry weight terms, for trees in the United States. However, the literature is inconsistent and incomplete with respect to large-scale forest C estimation. Estimates of national-scale forest carbon (C) stocks and fluxes are typically based on allometric regression equations developed using dimensional analysis techniques.
0 kommentar(er)
