Pitfalls and possible solutions for using geo-referenced site data to inform vegetation models

Megan J. McNellie; Ian Oliver; Philip Gibbons

doi:10.1016/j.ecoinf.2015.05.012

Pitfalls and possible solutions for using geo-referenced site data to inform vegetation models

Megan J. McNellie^*, Ian Oliver, Philip Gibbons

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

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 located within 1000. m of each other. Whilst sites within a 1000. m neighbourhood are not necessarily errors, they do need to be considered within the context of using spatial environmental layers and predictive modelling. We offer solutions for identifying and managing spatial locations of point data to ensure that the information-rich resource held in data repositories is not compromised by unidentified spatial error.

Original language	English
Pages (from-to)	230-234
Number of pages	5
Journal	Ecological Informatics
Volume	30
DOIs	https://doi.org/10.1016/j.ecoinf.2015.05.012
Publication status	Published - 1 Nov 2015

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10.1016/j.ecoinf.2015.05.012

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abstract = "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 located within 1000. m of each other. Whilst sites within a 1000. m neighbourhood are not necessarily errors, they do need to be considered within the context of using spatial environmental layers and predictive modelling. We offer solutions for identifying and managing spatial locations of point data to ensure that the information-rich resource held in data repositories is not compromised by unidentified spatial error.",

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Pitfalls and possible solutions for using geo-referenced site data to inform vegetation models

Abstract

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