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

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

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 segme...

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Bibliographic Details
Main Authors: Christos Karydas, Sandra Gewehr, Miltiadis Iatrou, George Iatrou, Spiros Mourelatos
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Journal of Imaging
Subjects:
olive tree
UAV
multiSPEC 4C
eBee
image segmentation
OBIA
CRI2
Online Access:https://www.mdpi.com/2313-433X/3/4/57
id doaj-491112b9405d43409cd77bd69c2c7c14
record_format Article
spelling doaj-491112b9405d43409cd77bd69c2c7c142020-11-24T21:48:55ZengMDPI AGJournal of Imaging2313-433X2017-12-01345710.3390/jimaging3040057jimaging3040057Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress MonitoringChristos Karydas0Sandra Gewehr1Miltiadis Iatrou2George Iatrou3Spiros Mourelatos4Ecodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, GreeceEcodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, GreeceAgroecosystem L.P., Nea Moudania, 2373 Chalkidiki, GreeceEcodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, GreeceEcodevelopment S.A., Filyro P.O. Box 2420, 57010 Thessaloniki, GreeceThe 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.https://www.mdpi.com/2313-433X/3/4/57olive treeUAVmultiSPEC 4CeBeeimage segmentationOBIACRI2
collection DOAJ
language English
format Article
sources DOAJ
author Christos Karydas
Sandra Gewehr
Miltiadis Iatrou
George Iatrou
Spiros Mourelatos
spellingShingle Christos Karydas
Sandra Gewehr
Miltiadis Iatrou
George Iatrou
Spiros Mourelatos
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
olive tree
UAV
multiSPEC 4C
eBee
image segmentation
OBIA
CRI2
author_facet Christos Karydas
Sandra Gewehr
Miltiadis Iatrou
George Iatrou
Spiros Mourelatos
author_sort Christos Karydas
title Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
title_short Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
title_full Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
title_fullStr Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
title_full_unstemmed Olive Plantation Mapping on a Sub-Tree Scale with Object-Based Image Analysis of Multispectral UAV Data; Operational Potential in Tree Stress Monitoring
title_sort olive plantation mapping on a sub-tree scale with object-based image analysis of multispectral uav data; operational potential in tree stress monitoring
publisher MDPI AG
series Journal of Imaging
issn 2313-433X
publishDate 2017-12-01
description 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.
topic olive tree
UAV
multiSPEC 4C
eBee
image segmentation
OBIA
CRI2
url https://www.mdpi.com/2313-433X/3/4/57
work_keys_str_mv AT christoskarydas oliveplantationmappingonasubtreescalewithobjectbasedimageanalysisofmultispectraluavdataoperationalpotentialintreestressmonitoring
AT sandragewehr oliveplantationmappingonasubtreescalewithobjectbasedimageanalysisofmultispectraluavdataoperationalpotentialintreestressmonitoring
AT miltiadisiatrou oliveplantationmappingonasubtreescalewithobjectbasedimageanalysisofmultispectraluavdataoperationalpotentialintreestressmonitoring
AT georgeiatrou oliveplantationmappingonasubtreescalewithobjectbasedimageanalysisofmultispectraluavdataoperationalpotentialintreestressmonitoring
AT spirosmourelatos oliveplantationmappingonasubtreescalewithobjectbasedimageanalysisofmultispectraluavdataoperationalpotentialintreestressmonitoring
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