A contribution towards simplifying area-wide tsetse surveys using medium resolution meteorological satellite data

Area of Science:

• Veterinary Entomology
• Geographic Information Systems (GIS)
• Remote Sensing

Background:

• A Geographic Information System (GIS) integrating tsetse fly, trypanosomiasis, animal production, agriculture, and land use data has been developed in Togo.
• Previous work established digital tsetse distribution and abundance maps aligned with local agro-ecological conditions.

Purpose of the Study:

• To generate tsetse distribution and abundance maps using remotely sensed data and limited field data.
• To evaluate the potential of satellite data and multivariate analysis for predicting tsetse distribution and abundance.

Main Methods:

• Application of discriminant analysis models using contemporary tsetse data.
• Integration of low-resolution remotely sensed data from NOAA and Meteosat platforms.
• Comparison of satellite-derived predictions with field data for validation and synergy.

Main Results:

• Satellite data combined with multivariate analysis effectively predict tsetse distribution and abundance.
• Strategic incorporation of satellite imagery can significantly minimize the need for extensive field data collection.
• The number of predictor variables must be carefully selected based on training data and distribution homogeneity.

Conclusions:

• Remotely sensed data offers a promising approach for mapping tsetse fly populations.
• Combining satellite predictions with field data can enhance accuracy and efficiency in tsetse surveillance.
• Optimized field surveys, informed by satellite data, can improve the planning of integrated trypanosomiasis control operations.