Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles

Re Yang Lee*, Kuo Chen Chang, Deng Yuan Ou, Chia Hui Hsu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Conducting field research in Taiwan can be challenging because of the abundance of steep slopes. This study aimed to establish an automatic interpretation procedure applicable to exploring images of large-scale slope land taken using UAVs. The proposed method was compared with traditional field surveying and manual image interpretation techniques to determine the advantages and disadvantages of the proposed procedure in terms of efficiency. The object-based image analysis (OBIA) and texture features were first combined and the random forest (RF) classifier was then employed to interpret crop types. This study selected three sites of slope land and plains for experimentation. The obtained results indicated that the overall accuracy of the proposed classification method exceeded 91%, and the Kappa value was approximately 0.9 for all sites. In addition, interpretation of the proposed method was more efficient than that of the two traditional methods.

Original languageEnglish
Pages (from-to)1293-1310
Number of pages18
JournalGeocarto International
Volume35
Issue number12
DOIs
Publication statusPublished - 2020 Sep 9

Keywords

  • object-based image analysis
  • random forest
  • texture
  • Unmanned aerial vehicle

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

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