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dc.contributor.author
Forstmaier, Andreas
dc.contributor.author
Shekhar, Ankit
dc.contributor.author
Chen, Jia
dc.date.accessioned
2020-08-03T13:26:24Z
dc.date.available
2020-08-03T02:48:36Z
dc.date.available
2020-08-03T13:26:24Z
dc.date.issued
2020-07
dc.identifier.issn
2072-4292
dc.identifier.other
10.3390/rs12142176
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/429609
dc.identifier.doi
10.3929/ethz-b-000429609
dc.description.abstract
Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Eucalyptus
en_US
dc.subject
Map
en_US
dc.subject
Natura 2000
en_US
dc.subject
Invasive species
en_US
dc.subject
Portugal
en_US
dc.subject
Artificial neural networks
en_US
dc.subject
Sentinel 2
en_US
dc.title
Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-07-08
ethz.journal.title
Remote Sensing
ethz.journal.volume
12
en_US
ethz.journal.issue
14
en_US
ethz.journal.abbreviated
Remote Sens.
ethz.pages.start
2176
en_US
ethz.size
19 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03648 - Buchmann, Nina / Buchmann, Nina
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03648 - Buchmann, Nina / Buchmann, Nina
ethz.date.deposited
2020-08-03T02:48:42Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-08-03T13:26:51Z
ethz.rosetta.lastUpdated
2022-03-29T02:44:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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