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


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…

Canadian Journal of Remote Sensing