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Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Abstract

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.

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Acknowledgments

A study of this nature would not have been possible without the hundreds of volunteers who contributed their data to the red-listed plant species database. M. J. Bailey helped with correction of the English text. Terhi Ryttäri helped in aggregating the species data for this study. Different parts of this research were funded by the Academy of Finland (project grant 116544) and the EC FP6 Integrated Projects ALARM (GOCE-CT-2003-506675) (Settele et al. 2005), ECOCHANGE (GOCE-2006-036866), and EU FP7 project SCALES (project #226852).

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Correspondence to Miia Parviainen.

Appendix

Appendix

See Tables 6, 7, and 8.

Table 6 List of different habitats included in the five main habitat categories delimited for the study species
Table 7 List of five Landsat 7 ETM+ images used in the study
Table 8 Summary of the response shapes between the 28 red-listed vascular plant species and each environmental variable in the hybrid GAM models

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Parviainen, M., Zimmermann, N.E., Heikkinen, R.K. et al. Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species. Biodivers Conserv 22, 1731–1754 (2013). https://doi.org/10.1007/s10531-013-0509-1

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Keywords

  • Endangered plant species
  • GAM
  • High-latitude landscape
  • Landsat ETM+
  • Predictive modelling
  • Productivity
  • Remote sensing