McNellie, Megan; Oliver, Ian; Gibbons, Philip
Most predictive models rely on 'the known' to infer 'the unknown'. Geo-referenced, on-ground observational data are the 'point of truth' upon which many vegetation models are built. We focus on some of the enigmatic errors that we have uncovered when using vegetation plot data. Using a case study, we sourced 9362 sites to examine the prevalence of spatial errors. We found that an incorrect datum was recorded for 5% of sites; less than 2% of sites were duplicated and up to 34% of sites were...[Show more]
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