DYNMAP - Dynamic predictive mapping using multi-sensor data fusion - demonstration for malaria vector habitat

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Context and objectives

Up-to-date information is crucial for the monitoring of environment-related processes. Unfortunately, despite the variety of satellite imagery, up-to-date detailed information is rarely available. Coarse and medium resolution provide cheap and frequent updates but the image is often not detailed enough. High resolution images are increasingly available but on small surfaces and to a prohibitive cost particularly for malaria control. Detailed information from land covers is generally available but often out of date due to the long process implied in developing such dataset for a whole country. The malaria control workers have thus to choose between cheap up-to-date data from coarse or medium spatial resolution, which is often not sufficient for the purpose, or detailed dataset produced by other services but that are often three to five years old and thus not relevant either for the purpose. Innovative methods are thus needed to provide more frequent updates and increase the opportunity of dynamic predictive mapping. Multi-sensor data fusion and downscaling techniques could offer an alternative. This research has thus four objectives: (1) develop a Bayesian Data Fusion method to merge data from various source and spatial resolution referring to the same date (2) adapt this approach to temporally non overlapping dataset (3) use the approach to map land surface descriptors of interest for malaria vector An. dirus s.l. (land cover, vegetation water content) (4) Delineate An. dirus s.l. distribution and dry season habitat using those descriptors.

Project outcome

  • A new data fusion method applicable not only to concomitant images of various resolutions but also to update detailed images using low to medium resolution imagery
  • Improved knowledge of relation between land descriptors and malaria vector in South East Asia
  • Verification of the hypothesis of receding habitat in the dry season for Anopheles dirus s.l. and association with land cover and relative humidity
  • Validation of leaf water content as indicator for approximation of relative humidity related to mosquito habitat
  • Pave the way towards a new family of products usable for various application
  • To help in improving the use of remote sensing product in the field of epidemiology deliverables
  • Scientific articles describing the achievements, limitation and applicability of the new developed methodology
  • The activities and results of the project are compiled in reports.
  • Map predicting the habitat of the vector in the dry season if the hypothesis is validated