Remote Sensing and Human Health: New Sensors and New Opportunities

Beck, Louisa R.; Lobitz, B.; Wood, B. L.

doi:10.3201/eid0603.000301

Cited by 297 publications

(207 citation statements)

References 26 publications

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“…Low spatial resolution satellite sensors can provide surrogates of meteorological data through specific channel values or the calculation of indices related to temperature, humidity and vegetation. These methods have been developed for different vectorborne diseases [8,41,42] and take benefit from the area-wide availability of the data and their repetitiveness over time. The accuracy of these predictions on the presence/absence or abundance of C. imicola is variable (from R 2 = 0.53 [3,4] to kappa = 0.91 [46]): understandably, they are more accurate in the regions from which entomological training data sets come from.…”

Section: Introductionmentioning

confidence: 99%

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

et al. 2007

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-The recent and rapid spread in the Mediterranean Basin of bluetongue, a viral disease of ruminants transmitted by some species of Culicoides (biting midges), highlights the necessity of determining the conditions of its emergence. This study uses high spatial resolution satellite imagery and methods from landscape ecology science to identify environmental parameters related to bluetongue occurrence in Corsica, a French Mediterranean island where the disease occurred for the first time in 2000. A set of environmental variables recorded in the neighborhood of 80 sheep farms were related to case occurrence through a logistic regression model computed within three subsequent buffer distances of 0.5, 1 and 2 km. The results reveal the role of landscape metrics, particularly those characterizing land-use units such as prairies and woodlands, as well as farm type, latitude and sunshine to explain the presence of bluetongue. Internal and external validation both indicate that the best results are obtained with the 1 km buffer size model (area under Receiver Operating Characteristic curve = 0.9 for internal validation and 0.81 for external validation). The results show that high spatial resolution remote sensing (i.e. 10 m pixels) and landscape ecology approaches contribute to improving the understanding of bluetongue epidemiology.epidemiology / bluetongue / remote sensing / geographic information systems / landscape ecology

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Section: Introductionmentioning

confidence: 99%

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

et al. 2007

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“…The relationship between environmental exposure to risk agents and health conditions have been studied with the aid of geotechnologies, such as remote sensing, which provide important information for surveillance, monitoring and mapping the risk of several diseases (Barcellos & Bastos 1996, Beck et al 2000.…”

mentioning

confidence: 99%

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

Oliveira¹,

Silva

Fernandes³

et al. 2012

Mem. Inst. Oswaldo Cruz

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The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies

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“…Geospatial analysis technologies such as remote sensing (RS) and geographical information systems (GIS) provide tools for gathering and analyzing these spatial data across a wide spectrum of spatial scales. RS has been applied to a variety of landscape epidemiological studies (Beck et al, 2000), and has been especially effective for analysis of a number of vector-borne and zoonotic diseases with environmental co-factors. Some of these diseases include malaria (Mushinzimana et al, 2006), Lyme disease (Brownstein et al, 2005), Chagas disease , West Nile fever (Rogers et al, 2002), hantavirus pulmonary syndrome (Glass et al, 2000), Ebola Hemorrhagic fever (Pinzon et al, 2004), and Rift Valley fever (Linthicum et al, 1987;Martin et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

2007

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Abstract. Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studies of infectious disease dynamics. For this application, it was necessary to develop a scheme that could yield both land cover and land use data from the same classification. Hypothesizing that automated discrimination between classes would be more accurate using an object-based method compared to a per-pixel method, we used a single Landsat Enhanced Thematic Mapper+ (ETM+) image to classify land cover into eight classes using both per-pixel and object-based classification algorithms. Our results show that the objectbased method achieves 84% overall accuracy, compared to only 43% using the per-pixel method. Producer's and user's accuracies for the object-based map were higher for every class compared to the per-pixel classification. The Kappa statistic was also significantly higher for the object-based classification. These results show the importance of using image information from domains beyond the spectral domain, and also illustrate the importance of object-based techniques for remote sensing applications in epidemiological studies.

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Remote Sensing and Human Health: New Sensors and New Opportunities

Cited by 297 publications

References 26 publications

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

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