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Modelling the spatial distribution of Culicoides imicola: climatic versus remote sensing data

Jasper Van doninck UGent, Bernard De Baets UGent, Jan Peters UGent, Guy Hendrickx, Els Ducheyne and Niko Verhoest UGent (2014) REMOTE SENSING. 6(7). p.6604-6619
abstract
Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables' importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species' presence and changing environment.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
bluetongue, MODIS, species distribution modelling, WorldClim, random forests, variable importance, BLUETONGUE VIRUS VECTORS, ENTOMOLOGICAL SURVEILLANCE, SATELLITE IMAGERY, RANDOM FORESTS, BITING MIDGES, BREEDING SITES, ABUNDANCE, CERATOPOGONIDAE, UNCERTAINTY, PREDICTION
journal title
REMOTE SENSING
Remote Sens.
volume
6
issue
7
pages
6604 - 6619
Web of Science type
Article
Web of Science id
000340038700033
JCR category
REMOTE SENSING
JCR impact factor
3.18 (2014)
JCR rank
5/28 (2014)
JCR quartile
1 (2014)
ISSN
2072-4292
DOI
10.3390/rs6076604
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
5796706
handle
http://hdl.handle.net/1854/LU-5796706
date created
2015-01-06 14:35:24
date last changed
2016-12-19 15:40:03
@article{5796706,
  abstract     = {Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88\%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables' importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species' presence and changing environment.},
  author       = {Van doninck, Jasper and De Baets, Bernard and Peters, Jan and Hendrickx, Guy and Ducheyne, Els and Verhoest, Niko},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keyword      = {bluetongue,MODIS,species distribution modelling,WorldClim,random forests,variable importance,BLUETONGUE VIRUS VECTORS,ENTOMOLOGICAL SURVEILLANCE,SATELLITE IMAGERY,RANDOM FORESTS,BITING MIDGES,BREEDING SITES,ABUNDANCE,CERATOPOGONIDAE,UNCERTAINTY,PREDICTION},
  language     = {eng},
  number       = {7},
  pages        = {6604--6619},
  title        = {Modelling the spatial distribution of Culicoides imicola: climatic versus remote sensing data},
  url          = {http://dx.doi.org/10.3390/rs6076604},
  volume       = {6},
  year         = {2014},
}

Chicago
Van doninckJasper, Bernard De Baets, Jan Peters, Guy Hendrickx, Els Ducheyne, and Niko Verhoest. 2014. “Modelling the Spatial Distribution of Culicoides Imicola: Climatic Versus Remote Sensing Data.” Remote Sensing 6 (7): 6604–6619.
APA
Van doninckJasper, De Baets, B., Peters, J., Hendrickx, G., Ducheyne, E., & Verhoest, N. (2014). Modelling the spatial distribution of Culicoides imicola: climatic versus remote sensing data. REMOTE SENSING, 6(7), 6604–6619.
Vancouver
1.
Van doninckJasper, De Baets B, Peters J, Hendrickx G, Ducheyne E, Verhoest N. Modelling the spatial distribution of Culicoides imicola: climatic versus remote sensing data. REMOTE SENSING. 2014;6(7):6604–19.
MLA
Van doninckJasper, Bernard De Baets, Jan Peters, et al. “Modelling the Spatial Distribution of Culicoides Imicola: Climatic Versus Remote Sensing Data.” REMOTE SENSING 6.7 (2014): 6604–6619. Print.