Modelling Bluetongue vectors occurence using GIS and Remote Sensing techniques. Martins H., Nunes T., Boinas F.

Transcription

1 Modelling Bluetongue vectors occurence using GIS and Remote Sensing techniques Martins H., Nunes T., Boinas F.

2 Introduction Objectives Spatial modelling of Culicoides imicola occurence Compare modelling techniques (discriminant analysis vs logistic regression)

3 Materials and Methods Base data for modelling Temperature Animal density - Entomological data - Climatic and - environmental data - Animal population data Temperature Humidity Larvae Eggs

4 Materials and Methods Entomological data - Ouput variable - Entomological surveillance program May 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

5 Material and Methods Climatic and Environmental data WorldClim package 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 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

6 Material and Methods Climatic and Environmental data - 30 remotely sensed variables - NOAA satellite, AVHRR sensor NDVI LST Temporal encapsulation through Fourier trasnformation MIR Maximum Minimum Average Amplitude Phase Anual, bi-anual, tri-anual cycles

7 Material and Methods Climatic and Environmental data - SRTM sensor Elevation Slope Slope Aspect extension

8 Material and Methods Animal population data - Bovines (SNIRB, DGV) - Small ruminants (SNIRA, DGV) - Kernel density Bovines Small Ruminants Ruminants

9 Material and Methods Conceptual Framework

10 Results Discriminant analysis - Significant variables Mean Temperature of warmest quarter (-) Minimum LST (+) Maximum MIR (+) Tri-annual LST amplitude (+) - Accuracy assessment Se = 76,6% Sp = 75,3% Global Accuracy = 75,8% Absence Presence

11 Results Logistic Regression - Significant variables Mean Temperature of warmest quarter (-) Precipitation of wettest quarter (-) Minimum NDVI (+) 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

12 Results BTV1 and 4 outbreaks (2006, 2007) Probability Absence Presence Methodology Class Temp. Warmest quarter Min. LST Slope Min. NDVI Discriminant analysis Logistic regression Absent 20,1 2778,4 7,3 1246,6 Present 23,5 2837,8 3,4 1345,7 Absent 20,5 1946,9 4,8 919,1 Present 23,6 2839,0 2,9 1264,8

13 Conclusions Logistic regression models were more robust and accurate Temperature variables were significant in both 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)

14 Work in progress

15 Acknowledgements Entomological Laboratory Staff National Veterinary Authority Thank you!