"Improving forest type discrimination with mixed lifeform classes using" by J. Rogan, N. Bumbarger et al.
 

Geography

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

Document Type

Article

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. © 2010 CASI.

Publication Title

Canadian Journal of Remote Sensing

Publication Date

12-1-2010

Volume

36

Issue

6

First Page

699

Last Page

708

ISSN

0703-8992

DOI

10.5589/m11-009

Keywords

forests, forestry, Massachusetts

Plum Print visual indicator of research metrics
PlumX Metrics
  • Citations
    • Citation Indexes: 15
  • Usage
    • Abstract Views: 1
  • Captures
    • Readers: 20
see details

Share

COinS