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dc.contributor.author
Andreo, Verónica Carolina
dc.contributor.author
Belgiu, Mariana
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Brito Hoyos, Diana Marcela
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Osei, Frank
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Provensal, María Cecilia
dc.contributor.author
Stein, Alfred
dc.date.available
2020-05-18T20:19:04Z
dc.date.issued
2019-05
dc.identifier.citation
Andreo, Verónica Carolina; Belgiu, Mariana; Brito Hoyos, Diana Marcela; Osei, Frank; Provensal, María Cecilia; et al.; Rodents and satellites: Predicting mice abundance and distribution with Sentinel-2 data; Elsevier Science; Ecological Informatics; 51; 5-2019; 157-167
dc.identifier.issn
1574-9541
dc.identifier.uri
http://hdl.handle.net/11336/105417
dc.description.abstract
Remote sensing data is widely used in numerous ecological applications. The Sentinel-2 satellites (S2 A and B), recently launched by the European Spatial Agency´s (ESA), provide at present the best revisit time, spatial and spectral resolution among the freely available remote sensing optical data. In this study, we explored the potential of S2 enhanced spectral and spatial resolution to explain and predict mice abundances and distribution in border habitats of agroecosystems. We compared the predictive ability of different vegetation and water indices derived from S2 and Landsat 8 (L8) imagery. Our analyses revealed that the best predictor of mice abundance was L8-derived Enhanced Vegetation Index (EVI). S2-based indices, however, outperformed those computed from L8 bands for indices estimated simultaneously to mice trappings and for mice distribution models. Furthermore, indices including S2 red-edge bands were the best predictors of the distribution of the two most common rodent species in the ensemble. The findings of this study can be used as guidelines when selecting the sensors and vegetation variables to be included in more complex models aimed at predicting the distribution and risk of various vector-borne diseases, and especially rodents in other agricultural landscapes.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
AGROECOSYSTEMS
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DISEASE ECOLOGY
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MICE ABUNDANCE
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RED-EDGE BANDS
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REMOTE SENSING
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VEGETATION INDICES
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Ecología
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Ciencias Biológicas
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CIENCIAS NATURALES Y EXACTAS
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Sensores Remotos
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Ingeniería del Medio Ambiente
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INGENIERÍAS Y TECNOLOGÍAS
dc.title
Rodents and satellites: Predicting mice abundance and distribution with Sentinel-2 data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-05-11T13:54:00Z
dc.identifier.eissn
1574-9541
dc.journal.volume
51
dc.journal.pagination
157-167
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Andreo, Verónica Carolina. University Of Twente; Países Bajos. Ministerio de Salud. Instituto Nacional de Medicina Tropical; Argentina. Universidad Nacional del Nordeste; Argentina
dc.description.fil
Fil: Belgiu, Mariana. University Of Twente; Países Bajos
dc.description.fil
Fil: Brito Hoyos, Diana Marcela. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Osei, Frank. University Of Twente; Países Bajos
dc.description.fil
Fil: Provensal, María Cecilia. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales; Argentina
dc.description.fil
Fil: Stein, Alfred. University Of Twente; Países Bajos
dc.journal.title
Ecological Informatics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.ecoinf.2019.03.001
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/journal/ecological-informatics/vol/51/suppl/C
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