Thibault Catry

Wetlands and Malaria in the Amazon: Guidelines for the Use of Synthetic Aperture Radar Remote-Sensing

The prevention and control of mosquito-borne diseases, such as malaria, are important health issues in tropical areas. Malaria transmission is a multi-scale process strongly controlled by environmental factors, and the use of remote-sensing data is suitable for the characterization of its spatial and temporal dynamics. Synthetic aperture radar (SAR) is well-adapted to tropical areas, since it is capable of imaging independent of light and weather conditions. In this study, we highlight the contribution of SAR sensors in the assessment of the relationship between vectors, malaria and the environment in the Amazon region. More specifically, we focus on the SAR-based characterization of potential breeding sites of mosquito larvae, such as man-made water collections and natural wetlands, providing guidelines for the use of SAR capabilities and techniques in order to optimize vector control and malaria surveillance. In light of these guidelines, we propose a framework for the production of spatialized indicators and malaria risk maps based on the combination of SAR, entomological and epidemiological data to support malaria risk prevention and control actions in the field.

Mapping soil typologies using geomorphologic features extracted from dem and SAR data: A environmental factor affecting malaria transmission in the Amazon

Soil typologies are characterized by different distribution and circulation of water, associated with specific water chemical properties, and thus potentially affects the distribution and density of malaria vectors in the Amazon. Based on a conceptual model of tropical soil evolution and distribution, the curvature of watershed slopes is a key indicator of soil typology. The average curvature of each subwatershed in our study area was identified using 30 m resolution SRTM data. PALSAR image with a spatial resolution of 12.5 m was used to implement a land cover map, including water and non-water surfaces. The non-water surface was then integrated with the average curvature of subwatersheds for soil typologies mapping. An indirect approach of soil typologies prediction was proposed, which is complementary with the classical soil classification methods.

Multi-sensor data fusion for identifying malaria environmental features

Malaria remains a persistent health problem in the cross-border area between French Guiana and Brazil. Various factors like environmental changes, population movements, etc. significantly influence the malaria transmission. In such context, optical and SAR data were used for producing a precise land cover map, including the resting and breeding sites of malaria vectors and human habitats. This study is a foundation for quantifying the degree of exposure of human populations to malaria vectors and thus for estimating and monitoring the malaria transmission risk.