Volume II-3/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 333-337, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-333-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 333-337, 2015
https://doi.org/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

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 D. Rocchini et al.
  • 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.