Modelling Bluetongue vectors occurence using GIS and Remote Sensing techniques
Martins H., Nunes T., Boinas F.
Introduction
� Objectives
� Spatial modelling of Culicoides imicola occurence
� Compare modelling techniques (discriminant analysis vs logistic regression)
Materials and Methods
� Base data for modelling
Temperature
- Entomological data
EggsLarvae
Animal density
Temperature
Humidity
- Entomological data
- Climatic and
- environmental data
- Animal population data
Materials and Methods
� Entomological data
- Ouput variable
- Entomological surveillance program� May 2005May 2005
� 3670 valid catches
� 216 sampling locations
- Data processing criteria� Maximum count of individual
� 2 valid catches (September – November)
� Treshold value = 10 specimens
� Boolean classification (73 neg, 47 pos)Absence Presence
Material and Methods
� Climatic and Environmental data
BIO1 = Annual Mean Temperature
BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO3 = Isothermality (P2/P7) (* 100)
BIO4 = Temperature Seasonality (standard deviation *100)
BIO5 = Max Temperature of Warmest Month
WorldClim package
BIO5 = Max Temperature of Warmest Month
BIO6 = Min Temperature of Coldest Month
BIO7 = Temperature Annual Range (P5-P6)
BIO8 = Mean Temperature of Wettest Quarter
BIO9 = Mean Temperature of Driest Quarter
BIO10 = Mean Temperature of Warmest Quarter
BIO11 = Mean Temperature of Coldest Quarter
BIO12 = Annual Precipitation
BIO13 = Precipitation of Wettest Month
BIO14 = Precipitation of Driest Month
BIO15 = Precipitation Seasonality (Coefficient of Variation)
BIO16 = Precipitation of Wettest Quarter
BIO17 = Precipitation of Driest Quarter
BIO18 = Precipitation of Warmest Quarter
BIO19 = Precipitation of Coldest Quarter
Material and Methods
� Climatic and Environmental data
- 30 remotely sensed variables
- NOAA satellite, AVHRR sensor� NDVI
LST Temporal encapsulation through Fourier trasnformation� LST
� MIR
Temporal encapsulation through Fourier trasnformation
MaximumMaximumMinimumMinimumAverageAverageAmplitudeAmplitudePhasePhase
MaximumMaximumMinimumMinimumAverageAverageAmplitudeAmplitudePhasePhase
Anual, bi-anual,
tri-anual cycles
Material and Methods
� Climatic and Environmental data
- SRTM sensor
Elevation Slope Slope AspectElevation Slope Slope extension
Aspect
Material and Methods
� Animal population data
- Bovines (SNIRB, DGV)
- Small ruminants (SNIRA, DGV)
- Kernel density
Bovines Small Ruminants Ruminants
Material and Methods
� Conceptual Framework
Results
� Discriminant analysis
- Significant variables� Mean Temperature of warmest quarter (-)
� Minimum LST (+)� Minimum LST (+)
� Maximum MIR (+)
� Tri-annual LST amplitude (+)
- Accuracy assessment� Se = 76,6%
� Sp = 75,3%
� Global Accuracy = 75,8%
Absence Presence
Results
� Logistic Regression- Significant variables
� Mean Temperature of warmest quarter (-)� Precipitation of wettest quarter (-)� Minimum NDVI (+)Slope (-)� Slope (-)
� Bi-annual MIR phase (-)� Tri-annual LST amplitude (+)� Minimum LST (+)� Annual MIR amplitude (+)� Mean temperature of driest quarter (+)
- Accuracy assessment� Se = 80,9%� Sp = 83,6%� Global Accuracy = 82,5%
Probability
Results
BTV1 and 4 outbreaks
(2006, 2007)
Absence Presence
Probability
(2006, 2007)
Methodology Class Temp. Warmest quarter
Min. LST
Slope Min. NDVI
Discriminant analysis
Absent 20,1 2778,4 7,3 1246,6
Present 23,5 2837,8 3,4 1345,7
Logistic regression
Absent 20,5 1946,9 4,8 919,1
Present 23,6 2839,0 2,9 1264,8
Conclusions
� Logistic regression models were more robust and accurate
� Temperature variables were significant in both modelsboth models
� Future developments:� Include other remote sensing variables
� Use higher resolution images (spatial, temporal and spectral)
� Repeat methodologies with time-dependent dataset
� Other methodologies (variable selection, modelling)
Work in progress
Acknowledgements
� Entomological Laboratory Staff
� National Veterinary Authority
Thank you!