ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 333-337, 2015
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/333/2015/
doi:10.5194/isprsannals-II-3-W5-333-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
20 Aug 2015
EXPLICITLY ACCOUNTING FOR UNCERTAINTY IN CROWDSOURCED DATA FOR SPECIES DISTRIBUTION MODELLING
D. Rocchini1, A. Comber2, C. X. Garzon-Lopez1, M. Neteler1, A. M. Barbosa3, M. Marcantonio1, Q. Groom4, C. da Costa Fonte5, and G. M. Foody6 1Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 S Michele allAdige, TN, Italy
2The School of Geography, University of Leeds Leeds, LS2 9JT, UK
3Centro de Investigacao em Biodiversidade e Recursos Geneticos (CIBIO), InBIO Research Network in Biodiversity and Evolutionary Biology, University of Evora, 7004-516 Evora, Portugal
4Information Technology and Botany - Botanic Garden Meise, Brussels, Belgium
5Universidade de Coimbra, Coimbra, Portugal
6School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK
Keywords: Ecosystems, Fuzzy Sets, Sampling Bias, Sampling Effort, Semantic Problems in Species Determination, Species Distribution Models, Uncertainty 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.
Conference paper (PDF, 3575 KB)


Citation: Rocchini, D., Comber, A., Garzon-Lopez, C. X., Neteler, M., Barbosa, A. M., Marcantonio, M., Groom, Q., da Costa Fonte, C., and Foody, G. M.: EXPLICITLY ACCOUNTING FOR UNCERTAINTY IN CROWDSOURCED DATA FOR SPECIES DISTRIBUTION MODELLING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 333-337, doi:10.5194/isprsannals-II-3-W5-333-2015, 2015.

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