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

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.

Keywords:

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

Acknowledgements

The authors wish to express our appreciation to the Agriculture and Food Agency, Council of Agriculture, R.O.C. (under Grant number 106 AFA-1.4-P-01) for funding that supported this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Agriculture and Food Agency, Council of Agriculture, R.O.C. under Grant number 106 AFA-1.4-P-01.