Social and Behavioral Sciences Commons^™

Mapping Selective Logging In Mixed Deciduous Forest: A Comparison Of Machine Learning Algorithms, Christopher D. Lippitt, John Rogan, Zhe Li, J. Ronald Eastman, Trevor G. Jones

Geography

This study assesses the performance of five Machine Learning Algorithms (MLAS) in a chronically modified mixed deciduous forest in Massachusetts (USA) in terms of their ability to detect selective timber logging and to cope with deficient reference datasets. Multitemporal Landsat Enhanced Thematic Mapper-plus (ETM+) imagery is used to assess the performance of three Anificial Neural Networks - Multi-Layer Perceptron, ARTMAP, Self-Organizing Map, and two Classification Tree splitting algorithms: gini and entropy rules, MLA performance evaluations are based on susceptibility to reduced training set size, noise, and variations in the training set, as well as the operability/transparency of the classification process. …