Species distribution models represent an important approach to map the spread of plant and animal species over space (and time). As all the statistical modelling techniques related to data from the field, they are prone to uncertainty. In this study we explicitly dealt with uncertainty deriving from field data sampling; in particular we propose i) methods to map sampling effort bias and ii) methods to map semantic bias.
Rocchini, D.; Comber, A.; Garzon Lopez, C.X.; Neteler, M.G.; Barbosa, A.M.; Marcantonio, M.; Groom, Q.; da Costa Fonte, C.; Foody, G.M. (2015). Explicitly accounting for uncertainty in crowdsourced data for species distribution modelling. In: 9th International Symposium on Spatial Data Quality (ISSDQ 2015), Montpellier, la Grande Motte, France, 29-30 September 2015. url: http://www.isprs-geospatialweek2015.org/workshops/issdq/index.html handle: http://hdl.handle.net/10449/25399
Explicitly accounting for uncertainty in crowdsourced data for species distribution modelling
Rocchini, Duccio;Garzon Lopez, Carol Ximena;Neteler, Markus Georg;Marcantonio, Matteo;
2015-01-01
Abstract
Species distribution models represent an important approach to map the spread of plant and animal species over space (and time). As all the statistical modelling techniques related to data from the field, they are prone to uncertainty. In this study we explicitly dealt with uncertainty deriving from field data sampling; in particular we propose i) methods to map sampling effort bias and ii) methods to map semantic bias.
| File | Dimensione | Formato | |
|---|---|---|---|
|
rocchini_etal_ISPRS.pdf
solo utenti autorizzati
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
2.03 MB
Formato
Adobe PDF
|
2.03 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
