The use of multivariate statistics and imputation by GNN results in unique spatial modeling properties. Alternative model forms can be specified to optimize for different objectives and outcomes. For the NWFP mapping, we are providing GNN species-size models and associated map products.
Species-size model: Response variables used in model development are basal area by species and size-class. This model weighs information on both species composition and stand structure, and resulting maps are useful for applications where elements of both are important - especially where it is important to maintain the covariance among these vegetation elements (e.g., if tree lists are to be input into simulation models such as the Forest Vegetation Simulator).
Map products for each modeling region include the following:
To download GNN map products, visit the modeling regions and schedule page and then click on your region of interest.
Digital GNN imputation maps are provided as 30-m-resolution ArcGIS grids, where the grid value is a unique plot number that links to the plot database. Selected vegetation variables from the plot database are joined as items in the grid to facilitate viewing and exploratory spatial analysis. Metadata for the vegetation variables are included with the grids and in the plot database. Dates for maps developed from GNN species-size models are determined by the vintage of the satellite imagery used in their development.
Most users will want to download the "masked" versions of the GNN maps, where areas of nonforest land cover developed from ancillary data sources have been embedded in the GNN grids. The GNN models apply only to forest land (areas currently or with the potential to support at least 10% tree cover), because a consistent regional plot sample of nonforest areas is unavailable. Unmasked versions of the GNN maps are also available for download, for users who would like to apply a nonforest mask of their own choosing.
Digital map where the grid value is the distance to the nearest-neighbor plot that was imputed to the pixel. Distance is unit-less Euclidean distance in eight-dimensional gradient space for the first eight axes from canonical correspondence analysis (CCA), with axes weighted by their explanatory power in the model (eigenvalues), and converted to integer grids (see Ohmann and Gregory 2002). The nearest-neighbor distance gives a spatially explicit indication of plot support for the GNN model. Areas with shorter distances are more likely to have better prediction accuracy than those with longer distances.