This chapter looks into the spatiotemporal dimension of both animal tracking data sets and the dynamic environmental data that can be associated with them. Typically, these geographic layers derive from remote sensing measurements, commonly those collected by sensors deployed on earth-orbiting satellites, which can be updated on a monthly, weekly or even daily basis. The modelling potential for integrating these two levels of ecological complexity (animal movement and environmental variability) is huge and comes from the possibility to investigate processes as they build up, i.e. in a full dynamic framework. This chapter’s exercise will describe howto integrate dynamic environmental data in the spatial database and join to animal locations one of the most used indices for ecological productivity and phenology, the normalised difference vegetation index (NDVI) derived from MODIS

Basille, M.; Urbano, F.; Racine, P.; Capecchi, V.; Cagnacci, F. (2014). Tracking animals in a dynamic environment: remote sensing image time series. In: Spatial database for GPS wildlife tracking data: a practical guide to creating a data management system with PostgreSQL/PostGIS and R (editor(s) Urbano, F; Cagnacci, F.): Springer: 95-114. ISBN: 978-3-319-03742-4 handle: http://hdl.handle.net/10449/24228

Tracking animals in a dynamic environment: remote sensing image time series

Cagnacci, Francesca
2014-01-01

Abstract

This chapter looks into the spatiotemporal dimension of both animal tracking data sets and the dynamic environmental data that can be associated with them. Typically, these geographic layers derive from remote sensing measurements, commonly those collected by sensors deployed on earth-orbiting satellites, which can be updated on a monthly, weekly or even daily basis. The modelling potential for integrating these two levels of ecological complexity (animal movement and environmental variability) is huge and comes from the possibility to investigate processes as they build up, i.e. in a full dynamic framework. This chapter’s exercise will describe howto integrate dynamic environmental data in the spatial database and join to animal locations one of the most used indices for ecological productivity and phenology, the normalised difference vegetation index (NDVI) derived from MODIS
NDVI
Raster time series
Spatial database
Spatiotemporal intersection
Settore BIO/07 - ECOLOGIA
2014
978-3-319-03742-4
Basille, M.; Urbano, F.; Racine, P.; Capecchi, V.; Cagnacci, F. (2014). Tracking animals in a dynamic environment: remote sensing image time series. In: Spatial database for GPS wildlife tracking data: a practical guide to creating a data management system with PostgreSQL/PostGIS and R (editor(s) Urbano, F; Cagnacci, F.): Springer: 95-114. ISBN: 978-3-319-03742-4 handle: http://hdl.handle.net/10449/24228
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