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Forest data visualization and land mapping using support vector machines and decision trees

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Abstract

Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different types of trees and in turn substantiate a part of the country’s economy. This paper uses visualization and machine learning (ML) processes to classify the forest land on the terrain dataset composed of the advanced spaceborne thermal emission and reflection radiometer (ASTER) imaging instrument to get the insight of the cumulated data by using Box Plot and Heat Map. The accuracy obtained by utilizing different machine learning techniques like Support Vector Machine (SVM) gives 95.4%, Logistic Regression (LR) gives 94.5%, K-Nearest Neighbor (K-NN) gives 93.7%, Decision Tree (DT) with 89.5%, Stochastic Gradient Descendent (SGD) with 92.4% and CN2 Rule Induction (RI) gives 85.3% are allied which gives appreciable results in forest mapping substantiated the same with confusion matrix and ROC. We also obtained the DT and rules for the considered dataset.

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Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, India

    Sujatha Radhakrishnan & Aarthy Seshadri Lakshminarayanan

  2. Department of Information Technology, Lord Buddha Educational Foundation (LBEF), Kathmandu, Nepal

    Jyotir Moy Chatterjee

  3. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    D. Jude Hemanth

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  1. Sujatha Radhakrishnan
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  2. Aarthy Seshadri Lakshminarayanan
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  3. Jyotir Moy Chatterjee
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  4. D. Jude Hemanth
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Correspondence to D. Jude Hemanth.

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Radhakrishnan, S., Lakshminarayanan, A.S., Chatterjee, J.M. et al. Forest data visualization and land mapping using support vector machines and decision trees. Earth Sci Inform 13, 1119–1137 (2020). https://doi.org/10.1007/s12145-020-00492-3

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  • DOI: https://doi.org/10.1007/s12145-020-00492-3

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