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Forest Change Detection Applying Landsat Thematic Mapper Difference Features: A Comparison of Different Classifiers in Boreal Forest Conditions

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This article addresses the problem of detecting forest changes via multitemporal Landsat Thematic Mapper (TM) imagery. A stand-level classification approach is selected, where, for each stand, a total of 35 statistical differences is extracted from Landsat TM images. Three forest stand-change classes are considered: 1) no change, 2) moderate change, and 3) considerable change. The classification results are reported by using the following classifiers: K-nearest-neighbor, Maximum Likelihood classifier with Gaussian- and Kernel-based class probability density estimation, Classification and Regression Trees, Multilayer Perceptron (MLP) early stop committee, and the MLP with weight decay training. Two Bayesian learning methods for MLP are also used: The Evidence Framework of MacKay, and Hybrid Monte Carlo (HMC) method following Neal. The best overall correct classification result (88.1%) is obtained by MLP trained with HMC and the Automatic Relevance Detection approach, but the variation of the performance of the classifiers is rather small. FOR. SCI. 50(5):579–588.

Keywords: Forest change detection; Landsat TM; comparison of classifiers; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources

Document Type: Regular Article

Affiliations: 1: Laboratory of Computational Engineering Helsinki University of Technology P.O. Box 9203 Finland FIN-02015 Hut Phone: +358-9-4514829;, Fax: +358-9-4514830, Email: 2: Finnish Forest Research Institute Unioninkatu 40 A Helsinki Finland FIN-00170 Phone: +358-102112057, Email:

Publication date: October 1, 2004

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