Steen Magnussen , Paul Boudewyn, Mike Wulder, David Seemann

Predictions of forest inventory cover type proportions using Landsat TM

Magnussen S., Boudewyn P., Wulder M., Seemann D. (2000). Predictions of forest inventory cover type proportions using Landsat TM. Silva Fennica vol. 34 no. 4 article id 618. https://doi.org/10.14214/sf.618

Abstract

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.

Keywords
neural net; maximum likelihood classification; agreement of predictions

Received 10 March 2000 Accepted 18 September 2000 Published 31 December 2000

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