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dc.contributor.author
Andreo, Verónica Carolina  
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Belgiu, Mariana  
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Brito Hoyos, Diana Marcela  
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Osei, Frank  
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Provensal, María Cecilia  
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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  
dc.subject.classification
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  
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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