The objective of this study was to develop a methodology for mapping olive plantations on a sub-tree scale. For this purpose, multispectral imagery of an almost 60-ha plantation in Greece was acquired with an Unmanned Aerial Vehicle. Objects smaller than the tree crown were produced with image segmentation. Three image features were indicated as optimum for discriminating olive trees from other objects in the plantation, in a rule-based classification algorithm. After limited manual corrections, the final output was validated by an overall accuracy of 93%. The overall processing chain can be considered as suitable for operational olive tree monitoring for potential stresses.
Karydas, C., Gewehr, S., Iatrou, M., Iatrou, G., & Mourelatos, S. (2017). Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring. Journal of Imaging, 3(4), 57. https://doi.org/10.3390/jimaging3040057