EXPLICITLY ACCOUNTING FOR UNCERTAINTY IN CROWDSOURCED DATA FOR SPECIES DISTRIBUTION MODELLING - D. Rocchini; A. Comber; C. X. Garzon-Lopez; M. Neteler; A. M. Barbosa; M. Marcantonio; Q. Groom; C. da Costa Fonte; G. M. Foody (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015)
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.