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Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Author

Listed:
  • Courage Kamusoko

    (Asia Air Survey (AAS) Co., Ltd., Kanagawa 215-0004, Japan)

  • Jonah Gamba

    (TOPS Systems Corp., Tsukuba 305-0032, Japan)

  • Hitomi Murakami

    (Department of Computer and Information Science, Faculty of Science and Technology, Seikei University, Tokyo 180-8633, Japan)

Abstract

Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT), random forests (RF), and support vector machines (SVM) in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I) variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

Suggested Citation

  • Courage Kamusoko & Jonah Gamba & Hitomi Murakami, 2014. "Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers," Land, MDPI, vol. 3(2), pages 1-17, June.
    • Handle: RePEc:gam:jlands:v:3:y:2014:i:2:p:524-540:d:37343
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    References listed on IDEAS

    as
    1. Gambiza, J. & Bond, W. & Frost, P. G. H. & Higgins, S., 2000. "SPECIAL SECTION: LAND USE OPTIONS IN DRY TROPICAL WOODLAND ECOSYSTEMS IN ZIMBABWE: A simulation model of miombo woodland dynamics under different management regimes," Ecological Economics, Elsevier, vol. 33(3), pages 353-368, June.
    2. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
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