The nine countries sharing the Amazon forest accounted for 89% of all malaria cases reported in the Americas in 2008. Remote sensing can help identify the environmental determinants of malaria transmission and their temporo-spatial evolution. Seventeen studies characterizing land cover or land use features, and relating them to malaria in the Amazon subregion, were identified. These were reviewed in order to improve the understanding of the land cover/use class roles in malaria transmission. The indicators affecting the transmission risk were summarized in terms of temporal components, landscape fragmentation and anthropic pressure. This review helps to define a framework for future studies aiming to characterize and monitor malaria.

This study deals with the connections between vegetation cover and the annual and interannual climatic variability in Brazil. The analyses were based on the normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor for the Global Inventory Modeling and Monitoring Study (GIMMS) continental dataset and monthly mean outgoing longwave radiation (OLR) data. The statistical technique of principal component analysis (PCA) was used for analysing the data for the January 1982–December 1993 period. The first component reveals that NDVI data have potential for characterizing the main patterns of vegetation types in Brazil. It was also possible to characterize the annual and interannual climate-related variability of the vegetation types. Specifically, the second and third leading PCA are associated with the summer/winter and spring/autumn portions of the annual cycle while their interannual variations are modulated by El Niño Southern Oscillation (ENSO) phenomena. The fourth component showed that ‘non-climatic’ factors, such as slash burning events, do affect NDVI values mainly in central Brazil. The high correlation coefficients between NDVI and OLR eigenvectors, corresponding to the summer/winter (r=0.91) and spring/autumn (r=0.70) modes, suggest that the annual variability of the vegetation coverage in Brazilian territory is largely modulated by the prevailing climatic conditions.

Deforestation has been linked to a rise in malaria prevalence. In this paper, we studied longitudinally 20 spots, including forested and deforested portions of a temporary river in a malarigenous frontier zone. Larval habitat parameters influencing distribution ofAnopheles darlingi(Diptera: Culicidae) larvae were studied. We observed that larvae were clustered in forested-deforested transitions. For the first time in the literature, it was verified that parameters determining larval distribution varied from deforested to forested areas. The proximity to human dwellings was also a significant factor determining distribution, but larvae was most importantly associated with a previously undescribed parameter, the presence of small obstructions to river flow, such as tree trunks within the river channel, which caused pooling of water during the dry season (‘microdams’). In deforested areas, the most important factor determining distribution of larvae was shade (reduced luminance). Larvae were absent in the entire studied area during the wet season and present in most sites during the dry season. During the wet-dry transition, larvae were found sooner in areas with microdams, than in other areas, suggesting that flow obstruction prolongs the breeding season ofAn. darlingi.Adult mosquito densities and malaria incidence were higher during the dry season. Our data correlate well with the published literature, including the distribution of malaria cases near the forest fringes, and has permitted the creation of a model ofAn. darlingibreeding, where preference for sites with reduced luminance, human presence and microdams would interact to determine larval distribution.

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.