Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images

Authors

  • Nobphadon Suksangpanya King Mongkut's Institute of Technology Ladkrabang https://orcid.org/
  • C. Supunyachotsakul Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

DOI:

https://doi.org/10.13052/jmm1550-4646.18317

Keywords:

Feature classification, satellite images, temporal, supervised classification, semi-automatic

Abstract

This work focuses on rotating crops in the forest conservation areas in the
northern region of Thailand which always cause false detection for forest
encroachment and deforestation. Therefore, this work establishes a database
of rotating crop areas in the northern region of Thailand and additionally
develops a semi-automatic classification approach to help facilitate the classification
process. LANDSAT images ranging from 1987 to 2018 are used
as the input data for classifying the rotating crop areas. The semi-automatic
classification approach is comprised of the automatic supervised classification
and the manual classification by visual interpretation, respectively. The
automatic and manual classification procedures are explained, and the results
are verified by using ground truth locations distributed over the study region
which gives 81.72% accuracy.

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Author Biography

C. Supunyachotsakul, Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

C. Supunyachotsakul received her B.Eng. degree in Survey Engineering from Chulalongkorn University, Thailand; and M.Sc. degrees in Photogrammetry and Geoinformatics from Stuttgart University of Applied Sciences, Germany; and M.S.E. and Ph.D. degrees in Civil Engineering from Purdue University, USA. Dr. Supunyachotsakul is currently a faculty member at Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand. Her research interest is in the areas of Photogrammetry, LIDAR technology, and 3D-point cloud data processing.

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Published

2022-02-04

How to Cite

Suksangpanya, N., & Supunyachotsakul, C. . (2022). Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images. Journal of Mobile Multimedia, 18(03), 807–820. https://doi.org/10.13052/jmm1550-4646.18317

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Section

Smart Innovative Technology for Future Industry and Multimedia Applications