Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest

Abstract

This research investigated three machine learning approaches - decision trees, random forest, and support vector machines - to classify local forest communities at the Huntington Wildlife Forest (HWF), located in the central Adirondack Mountains of New York State, and to identify forest type change over a 20-year period using multi-temporal Landsat satellite Thematic Mapper (TM) data. Because some forest species are sensitive to topographic characteristics, three terrain correction methods - C correction, statistical-empirical (SE) correction, and Variable Empirical Coefficient Algorithm (VECA) - were utilized to account for the topographic effects. Results show that the topographic correction slightly improved the classification accuracy although the improvement was not significant based on the McNemar test. Random forest and support vector machines produced higher classification accuracies than decision trees. Besides, random forest- and support vector machine-based multi-temporal classifications better reflected the forest type change seen in the reference data. In addition, topographic features such as elevation and aspect played important roles in characterizing the forest type changes.