dc.creatorMarzialetti, Pablo
dc.creatorGiovanni, Laneve
dc.creatorSantilli, Giancarlo
dc.creatorHuan, Wenjiang
dc.creatorZappacosta, Diego Carlos
dc.date.accessioned2022-05-06T15:18:27Z
dc.date.accessioned2022-10-15T12:28:38Z
dc.date.available2022-05-06T15:18:27Z
dc.date.available2022-10-15T12:28:38Z
dc.date.created2022-05-06T15:18:27Z
dc.date.issued2019
dc.identifierMaxent model application for tree pest monitoring; International Symposium on Geoscience and Remote Sensing; Japón; 2019; 6664-6666
dc.identifier978-1-5386-9154-0
dc.identifierhttp://hdl.handle.net/11336/156795
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4386055
dc.description.abstractTree 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.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://igarss2019.org/
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/8898056
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceInternational Symposium on Geoscience and Remote Sensing
dc.subjectREMOTE SENSING
dc.subjectXYLELLA FASTIDIOSA
dc.subjectOLIVES
dc.subjectTREE PEST
dc.titleMaxent model application for tree pest monitoring
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.typeinfo:ar-repo/semantics/documento de conferencia


Este ítem pertenece a la siguiente institución