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dc.contributor.author
Marzialetti, Pablo  
dc.contributor.author
Giovanni, Laneve  
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Santilli, Giancarlo  
dc.contributor.author
Huan, Wenjiang  
dc.contributor.author
Zappacosta, Diego Carlos  
dc.date.available
2022-05-06T15:18:27Z  
dc.date.issued
2019  
dc.identifier.citation
Maxent model application for tree pest monitoring; International Symposium on Geoscience and Remote Sensing; Japón; 2019; 6664-6666  
dc.identifier.isbn
978-1-5386-9154-0  
dc.identifier.uri
http://hdl.handle.net/11336/156795  
dc.description.abstract
Tree pests can cause rapid and widespread damage, reducing the economic value of plants, production, in the case of fruit trees, and their role in mitigating climate change. There are several diseases that affect trees, including, for example, pine tree nematode (PWN), trunk fungal diseases, or Xylella fastidiosa (Xf).Mapping of diseased plants based on visual or automatic analysis of remote sensing data could be a useful support for in situ investigation planning. However, there is a clear need for better modeling methods to elaborate potential critical scenarios in order to early detect diseases (e.g. Xf) in host plants.Maxent (Maximum Entropy) has proved powerful when modeling species with available scarce presence-only occurrence data. The purpose is to predict potential distributions or explore expanding distributions. In this work we applied the Maxent model comparing local modeling results with worldwide cases towards a more comprehensive analysis of potential pest risk zones.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Electrical and Electronics Engineers  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
REMOTE SENSING  
dc.subject
XYLELLA FASTIDIOSA  
dc.subject
OLIVES  
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TREE PEST  
dc.subject.classification
Ciencias de la Información y Bioinformática  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Maxent model application for tree pest monitoring  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.type
info:eu-repo/semantics/conferenceObject  
dc.type
info:ar-repo/semantics/documento de conferencia  
dc.date.updated
2022-03-16T20:55:01Z  
dc.journal.pagination
6664-6666  
dc.journal.pais
Japón  
dc.journal.ciudad
Pacifico Yokohama  
dc.description.fil
Fil: Marzialetti, Pablo. Università degli Studi di Roma "La Sapienza"; Italia  
dc.description.fil
Fil: Giovanni, Laneve. Università di Roma; Italia  
dc.description.fil
Fil: Santilli, Giancarlo. Universidade do Brasília; Brasil  
dc.description.fil
Fil: Huan, Wenjiang. Chinese Academy of Sciences; República de China  
dc.description.fil
Fil: Zappacosta, Diego Carlos. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://igarss2019.org/  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8898056  
dc.conicet.rol
Autor  
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Autor  
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Autor  
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Autor  
dc.coverage
Internacional  
dc.type.subtype
Simposio  
dc.description.nombreEvento
International Symposium on Geoscience and Remote Sensing  
dc.date.evento
2019-07-28  
dc.description.paisEvento
Japón  
dc.type.publicacion
Book  
dc.description.institucionOrganizadora
Institute of Electrical and Electronics Engineers  
dc.description.institucionOrganizadora
The Geoscience and Remote Sensing Society  
dc.source.libro
International Symposium on Geoscience and Remote Sensing  
dc.date.eventoHasta
2019-08-02  
dc.type
Simposio