Mapping Chestnut Stands Using Bi-Temporal VHR Data

DC FieldValueLanguage
dc.contributor.authorMarchetti, Francesca
dc.contributor.authorWaske, Bjoern
dc.contributor.authorArbelo, Manuel
dc.contributor.authorMoreno-Ruiz, Jose A.
dc.contributor.authorAlonso-Benito, Alfonso
dc.date.accessioned2021-12-23T16:14:12Z-
dc.date.available2021-12-23T16:14:12Z-
dc.date.issued2019
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/10959-
dc.description.abstractThis study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife.
dc.description.sponsorshipUniversidad de La Laguna; Universidad de Almeria [2018/0001440, 2019/006]; Ministerio de Ciencia, Innovacion y Universidades (MCIU); Agencia Estatal de Investigacion (AEI); Fondo Europeo de Desarrollo Regional (FEDER)European Commission [RTI2018-099171-B-I00]; The Universidad de La Laguna and the Universidad de Almeria funded this work through the bridge projects 2018/0001440 and 2019/006, granted in the 2018 and 2019 calls. This study was also partially funded by the Ministerio de Ciencia, Innovacion y Universidades (MCIU), the Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) through the project RTI2018-099171-B-I00.
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofREMOTE SENSING
dc.subjectACCURACY
dc.subjectAREA
dc.subjectbi-temporal image
dc.subjectCanary Islands
dc.subjectEnvironmental Sciences
dc.subjectEnvironmental Sciences & Ecology
dc.subjectextended morphological profiles
dc.subjectGeology
dc.subjectGeosciences, Multidisciplinary
dc.subjectHYPERSPECTRAL DATA
dc.subjectImaging Science & Photographic Technology
dc.subjectLIDAR
dc.subjectQUANTITY DISAGREEMENT
dc.subjectrandom forest
dc.subjectRANDOM FOREST CLASSIFIER
dc.subjectRemote Sensing
dc.subjectREMOTE-SENSING DATA
dc.subjectSPATIAL CLASSIFICATION
dc.subjectTREE SPECIES CLASSIFICATION
dc.subjectWorldView
dc.subjectWORLDVIEW-2 IMAGERY
dc.titleMapping Chestnut Stands Using Bi-Temporal VHR Data
dc.typejournal article
dc.identifier.doi10.3390/rs11212560
dc.identifier.isiISI:000504716700101
dc.description.volume11
dc.description.issue21
dc.contributor.orcid0000-0002-6853-4442
dc.contributor.researcheridAAY-4509-2020
dc.contributor.researcheridF-4128-2016
dc.identifier.eissn20724292
dc.publisher.placeST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
dcterms.isPartOf.abbreviationRemote Sens.
dcterms.oaStatusgold, Green Published
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidWaBj345-
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