The goal of foster is to streamline the modeling of the relationship between satellite imagery time series or any other environmental information, such as terrain elevation, with forest structural attributes derived from 3D point cloud data and their subsequent imputation over the broader landscape.
The package is organized around functions for data preprocessing, stratified random sample selection, spectral index calculation, time series summary metrics calculation, k-NN predictive model development and their accuracy assessment, and finally response variable (i.e. forest attributes) imputation. The following figure shows a diagram explaining the general processing flow a user might encounter when imputing forest attributes from selected predictors that may include time series of multispectral satellite images and topographic variables.
You can install the development version of foster from GitHub with:
A vignette is available to give an overview of FOSTER and run through an example of ALS metrics imputation.
Note that if using the development version of FOSTER, the vignette to be build during installation to access it with browseVignette("foster"). It will take a few minutes to build the vignette.