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J. Imaging 2017, 3(4), 57; https://doi.org/10.3390/jimaging3040057

Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring

1
Ecodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, Greece
2
Agroecosystem L.P., Nea Moudania, 2373 Chalkidiki, Greece
*
Author to whom correspondence should be addressed.
Received: 12 September 2017 / Revised: 26 November 2017 / Accepted: 30 November 2017 / Published: 4 December 2017
(This article belongs to the Special Issue Remote and Proximal Sensing Applications in Agriculture)
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Abstract

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. View Full-Text
Keywords: olive tree; UAV; multiSPEC 4C; eBee; image segmentation; OBIA; CRI2 olive tree; UAV; multiSPEC 4C; eBee; image segmentation; OBIA; CRI2
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Karydas, C.; Gewehr, S.; Iatrou, M.; Iatrou, G.; Mourelatos, S. Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring. J. Imaging 2017, 3, 57.

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