Improving forest type discrimination with mixed lifeform classes using fuzzy classification thresholds informed by field observations

J. Rogan, N. Bumbarger, D. Kulakowski, Zachary Christman, D. M. Runfola, S. Blanchard

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents a three-stage methodology to mitigate uncertainty in forest lifeform classification using a case study in the mixed hardwood-conifer forest of Massachusetts, USA. First, two fuzzy membership surfaces representing the proportion of conifer and hardwood lifeform dominance were created using a supervised multilayer perceptron neural network algorithm. Second, an index of lifeform membership was generated using a ratio of the membership surfaces of conifer and hardwood forest. Lastly, this index was thresholded using field measurements of forest lifeform proportion to delineate pure conifer, mixed conifer-hardwood, and pure hardwood categories. This methodology produced a map of forest lifeform with 94% overall accuracy (kappa 0.88 for hardwood, 0.97 for conifer, and 0.97 for mixed), an improvement of 10% over a map generated using a top-down method using mixed forest training sites. Per-class accuracies increased approximately 5% for both the pure hardwood class, 26% for the pure conifer class, and 16% for the mixed class. The improvement in map accuracy was due to improved spectral discrimination of lifeforms, which results in a more geographically plausible map.

Original languageEnglish (US)
Pages (from-to)699-708
Number of pages10
JournalCanadian Journal of Remote Sensing
Volume36
Issue number6
DOIs
StatePublished - Dec 1 2010

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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