Machine Learning Classification of Tree Cover Type and Application to Forest Management

Machine Learning Classification of Tree Cover Type and Application to Forest Management

Duncan MacMichael (University of Washington, Bothell, USA) and Dong Si (University of Washington, Bothell, USA)
DOI: 10.4018/IJMDEM.2018010101
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This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.
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This paper begins with an introduction to the problem background and research background, followed by a description of the dataset. Next, the methodology and approach for analyzing and preprocessing the dataset are presented. Experiments that were performed are then detailed, including techniques to improve accuracy and the method used for ensemble training in parallel. Finally, results are described for the baseline classifiers and the neural network. These results are compared to past work in this area before listing challenges, future work, and conclusions.

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