ANISETTE: tracking mosquitoes vectors of diseases

An innovative project

The ANISETTE project – Inter-Site Analysis: Evaluation of Remote Sensing as a predictive tool for the surveillance and control of diseases caused by mosquito – has just been launched. With CNES funding, this project aims to measure the interoperability of methods combining remote sensing and spatial modelling to predict the dynamics of mosquito vectors and associated diseases. The aim is to identify the most suitable Earth observation images to predict areas conducive to the development of different mosquito vector species – including Aedes (vectors of dengue fever, Rift Valley fever) and Anopheles (vectors of malaria). These analyses will be carried out on various geographical sites: South America (Brazil, the West Indies, French Guiana), Europe (France), the Indian Ocean (Madagascar, Reunion Island), South and South-East Asia (India, Thailand, Cambodia) and Oceania (New Caledonia).

anisette.cirad.fr

ANISETTE is a continuation of regular and long-standing collaboration between the teams of the Institut de Recherche pour le Développement (UMR Espace Dev) and CIRAD (UMR TETIS, UMR ASTRE), within the Maison de la Télédétection in Montpellier and the “Remote-sensing, Environment, Health” group, now the Theia “Risks Associated with Infectious Diseases” Scientific Expertise Centre (SEC), and in partnership with researchers from the UMR IDEES. It brings together various French research teams working on remote sensing applied to diseases vectored by mosquitoes. 


Annelise Tran
Cirad | Tetis
@A.Tran
Contributions

More on this theme

More news

Welcome to Sentinel-2C, but why not keep Sentinel-2A operational too?

The Sentinel-2C satellite was launched by ESA and the Copernicus programme on 4 September 2024 on a VEGA launcher. As soon as it is operational, nominally following an estimated 3-month […]

The 2nd Workshop dedicated to high-resolution thermal observation gets underway

Save already the date for the coming 2nd French-Indian Workshp dedicated to HR thermal observation.

clASpy_T : a 3D point cloud classification tool for estuary monitoring

The THEIA portfolio of Lidar data processing tools has been extended with a new software tool for creating 3D point cloud classification models using machine learning: cLASpy_T cLASpy_T aims to […]

Search