We assess accuracy of GNN models in a number of ways, at both the local and regional scales. In general, GNN vegetation maps are appropriate for landscape to regionalscale analyses but are insufficiently accurate for local or standlevel applications.
The following examples are from the 2006 GNN model covering Western Washington for the Northwest Forest Plan project.
Observed vs. Predicted Plot AttributesWe create scatterplots to compare the observed plot values against predicted (modeled) values for each plot used in the GNN model. The observed value comes directly from the plot data, whereas the predicted value comes from the GNN prediction for the plot location. We use a modified leaveoneout crossvalidation approach, based on a pixel's second nearestneighbor. We develop our models with all plots, but in determining accuracy, we don't allow a plot to assign itself as a neighbor at the plot location. This approach yields similar accuracy assessment results as a true crossvalidation approach, but probably slightly underestimates the true accuracy of the distributed (firstnearestneighbor) map. 
Species Kappa CoefficientsOne accuracy measure we use for the species distribution variables is based on Cohen's kappa coefficient, which is a statistical measure of reliability that accounts for agreement occurring by chance. The equation for kappa is: kappa = (Pr(a)  Pr(e))/(1.0  Pr(e)) where Pr(a) is the relative observed agreement among raters, and Pr(e) is the probability that agreement is due to chance. 
We create an error matrix for vegetation class assignments at plot locations. Cell values are model plot counts. Dark gray cells represent plots where the observed class matches the predicted class and are included in the percent correct. Light gray cells represent cases where the observed and predicted differ slightly (within +/ one class) based on canopy cover, hardwood proportion or average stand diameter, and are included in the percent "fuzzy" correct.
Observed Class 
Predicted Class  
Sparse  Open  Blf Sm 
Blf Md/Lg 
Mix Sm 
Mix Md 
Mix Lg 
Con Sm 
Con Md 
Con Lg 
Con VLg 
Total  % Correct 
% FCorrect 

Sparse  12  33  2  0  4  1  0  5  3  0  0  60  20.0  75.0 
Open  111  49  2  0  9  5  0  39  6  1  0  122  40.2  90.2 
Blf  Sm  1  4  4  6  8  5  0  4  0  0  0  32  12.5  68.8 
Blf  Md/Lg  0  1  5  9  3  19  1  2  4  0  0  44  20.5  77.3 
Mix  Sm  0  3  1  5  19  13  0  16  7  0  0  64  29.7  81.3 
Mix  Md  0  1  2  6  17  28  2  7  23  4  0  90  31.3  84.4 
Mix  Lg  0  0  0  1  0  5  3  0  3  1  0  13  23.1  76.9 
Con  Sm  1  16  0  0  29  14  0  215  144  16  0  435  49.4  92.9 
Con  Md  0  6  0  0  7  25  1  69  410  119  13  650  63.1  95.8 
Con  Lg  0  0  0  0  0  4  1  7  130  148  48  338  43.8  96.7 
Con  VLg  0  0  1  0  0  0  0  1  22  91  33  148  22.3  83.8 
Total  25  113  17  27  96  119  8  365  752  380  94  1996  
% Correct  48.0  43.4  23.5  33.3  19.8  23.5  37.5  58.9  54.5  38.9  35.1  46.6  
% FCorrect  92.0  92.9  70.6  81.5  85.4  75.6  87.5  92.9  94.0  94.5  86.2  91.5 
Area Distributions from Regional Inventory Plots vs. GNNWe create histograms to compare the distributions of land area in different vegetation conditions as estimated from a regional, sample (plot) based inventory (FIA Annual plots) to model predictions from GNN (based on counts of 30m pixels). The stand height histogram to the left is just one example of an area histogram included in GNN accuracy assessment reports. The reports contain histograms for a variety of plot variables including tree basal area, canopy cover, quadratic mean diameter, snag volume, etc. 