%0 Research article
%T Predictions of forest inventory cover type proportions using Landsat TM
%A Magnussen, Steen
%A Boudewyn, Paul
%A Wulder, Mike
%A Seemann, David
%D 2000
%J Silva Fennica
%V 34
%N 4
%R doi:10.14214/sf.618
%U https://silvafennica.fi/article/618
%X The feasibility of generating via Landsat TM data current estimates of  cover type proportions for areas lacking this information in the  national forest inventory was explored by a case study in New Brunswick.  A recent forest management inventory covering 4196 km2 in south-eastern  New Brunswick (the test area) and a coregistered Landsat TM scene was  used to develop predictive models of 12 cover type proportions in an  adjacent 4525 km2 region (the validation area). Four prediction models  were considered, one using a maximum likelihood classifier (MLC), and  three using the proportions of 30 TM clusters as predictors. The MLC was  superior for non-vegetated cover types while a neural net or a  prorating of cluster proportions was chosen for predicting vegetated  cover types. Most predictions generated for national inventory  photo-plots of 2 x 2 km were closer to the most recent inventory results  than estimates extrapolated from the test area. Agreement between  predictions and current inventory results varied considerably among  cover types with model-based predictions outperforming, on average, the  simple spatial extensions by about 14%. In this region, an 11-year-old  forest inventory for the validation area provided estimates that in half  the cases were closer to current inventory estimates than predictions  using the optimal Landsat TM model. A strong temporal correlation of  photo-plot-level cover type proportions made old-values more consistent  than predictions using the optimal Landsat TM model in all but three  cases. Prorating of cluster proportions holds promise for large-scale  multi-sensor predictions of forest inventory cover types.