Next Article in Journal
Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island
Previous Article in Journal
The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(7), 6604-6619; doi:10.3390/rs6076604

Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data

1
Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Ghent, Belgium
2
Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, 9000 Ghent, Belgium
3
Avia-GIS, Risschotlei 33, 2980 Zoersel, Belgium
*
Author to whom correspondence should be addressed.
Received: 17 February 2014 / Revised: 24 June 2014 / Accepted: 14 July 2014 / Published: 18 July 2014
View Full-Text   |   Download PDF [1606 KB, uploaded 18 July 2014]   |  

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. View Full-Text
Keywords: species distribution modelling; bluetongue; MODIS; WorldClim; random forests; variable importance species distribution modelling; bluetongue; MODIS; WorldClim; random forests; variable importance
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Van Doninck, J.; De Baets, B.; Peters, J.; Hendrickx, G.; Ducheyne, E.; Verhoest, N.E. Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data. Remote Sens. 2014, 6, 6604-6619.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top