PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Remote Sensing Technology”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed1996

Estimation of larval production in Sanjay Lake and its surrounding ponds in Delhi, India using remote sensing technology.

A feasibility study to use remote sensing techniques for estimation of mosquito production in Sanjay lake in east Delhi was carried out. Besides the Sanjay lake, larval production for 12 surrounding remote sensing identifiable ponds was also estimated. Inspite of some limitations the technique is very useful for rapid mapping of major breeding sites, recording temporal changes and estimation of larval production in a cost effective manner in terms of survey cost and time.

Animals↗
PubMed2003

[Ecological surveillance on breeding ground for Oncomelania hupensis snails in the areas prevalent with islet-type schistosomiasis using remote sensing technology].

OBJECTIVE: To probe rational indices suitable for ecological surveillance on breeding ground for Oncomelania hupensis snails in areas prevalent with islet-type schisitosomiasis using remote sensing technology. METHODS: Three adjacent islets, prevalent with islet-type schistosomiasis, along the Yangtze River within the boundaries of Dongzhi County, Anhui Province were selected as study field for remote sensing analysis. Multi-spectral data were composed and non-supervisedly classified in computer with Idisi software for remote sensing analysis. Values of the normalized difference vegetation index (NDVI), green vegetation index (GVI), bright index (BI), which reflect the greenness and brightness of landscape, were also calculated. Finally, all the results were comprehensively analyzed, combined with data from the field investigation. RESULTS: NDVI, GVI and BI could depict characteristics of the landscape quantitatively. Values of NDVI, BI and GVI were varied in different types of landscapes, and 95% confidence interval of these values suitable for breeding of snails was 0.0522 approximately 0.3566, 2.4162 approximately 28.2672 and 29.3404 approximately 40.3135, respectively. Classification of NDVI showed that type 5 anf type 6 were main breeding ground for snails, and type 4 with values of NDVI from 0 to 0.1 was potential areas for snail propagation. Classification of GVI showed that types 5, 6 and 7 were main breeding ground for snails, and also type 4 with values of GVI from 2 to 10 was potential areas for snail propagation. Both NDVI and GVI showed type 2 and type 3 were temporarily not suitable for snail breeding. CONCLUSION: Index figures of NDVI and GVI re-formed by reasonable classification could reflect not only breeding ground for snails and range of the areas for snail propagation in islets, but also their evolving rules, i.e., status of new marshland formation and vegetation growth.

Animals↗
PubMed2002

Climatic and demographic determinants of American visceral leishmaniasis in northeastern Brazil using remote sensing technology for environmental categorization of rain and region influences on leishmaniasis.

Remote sensing (RS) permits evaluation of spatial and temporal variables that can be used for vector-borne disease models. A Landsat Thematic Mapper scene covering Canindé, Ceará in northeastern Brazil (September 25, 1986) was spectrally enhanced and classified using ERDAS (Atlanta, GA) Imagine for 873 4-km2 areas. The population and number of cases of American visceral leishmaniasis (AVL) were determined for each 4-km2 area. Relative risk (RR) ratios were calculated for climate, demographic, and case data recorded for 17 years by the Municipality of Conidé. The RR of AVL for a child less than 10 years old from the foothills relative to non-foothill residency was 4.0 (95% confidence limit = 3.5, 4.5). The RR of AVL in children was 9.1 during a time when the three-year rolling rain average (current year plus two previous year's precipitation) was between 40 and 60 cm relative to rain greater than 100 cm. The results suggest that features detected by RS techniques combined with climatic variables can be used to determine the risk of AVL in northeastern Brazil.

Adult↗
PubMed2004

[Geographical Information Systems and remote sensing technologies in parasitological epidemiology].

Parasites have natural habitats in the same way as a species: they are found in focal areas where the spatial distribution of the parasite, host, vector and required environmental conditions coincide. The spatial distribution of parasites is, therefore, a function of the interaction between abiotic and biotic environmental factors. The boundaries of distributions are not strictly fixed in space and time and may fluctuate with climate and other components of the environment or anthropical factors. Geographic Information Systems (GIS) and remote sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of disease. GIS can be used to complement conventional ecological monitoring and modelling techniques, and provide means to portray complex relationships in the ecology of disease. In addition, the use of GIS and RS to identify environmental features allows determination of risk factors and delimitation of areas at risk, permitting more rational allocation of resources for cost-effective control. Since 1996, GIS have been used in our territorial cross-sectional and longitudinal parasitological surveys in order to experiment new applications to plan sampling protocols and to display quickly, clearly, and analytically the spatial and/or temporal distribution of parasitological data. The use of GIS allowed us to draw the following types of descriptive parasitological maps: distribution maps, distribution maps with proportioned peaks, choroplethic maps with proportioned peaks, point distribution maps and point distribution maps with proportioned peaks. In a recent study, GIS and RS technologies have been used also to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines and to develop a preliminary risk assessment model. A GIS was constructed using RS and landscape feature data together with paramphistome positive survey records from 197 georeferenced ovine farms with animals pasturing in an area of the southern Italian Apennines. The GIS for the study area was constructed utilizing the following environmental variables: Normalized Difference Vegetation Index (NDVI), land cover, elevation, slope, aspect, and total length of rivers. In addition, data regarding the presence of watercourses smaller than rivers, namely, streams, springs and brooks were recorded in the field. All these variables were then calculated for "buffer zones" consisting of the areas included in a circle of 3 Km diameter centred on 197 farms. The environmental data obtained were analyzed by univariate and multivariate statistical analyses using the paramphistome farm coprological status (positive/negative) as the dependent variable. A multivariate stepwise discriminant analysis model was developed that included moors and heathland, sclerophyllus and coniferous forest vegetation, autumn-winter NDVI and presence of streams, springs and brooks on pasture. The variables entered in the model are consistent with the environmental requirements of paramphistomes and their snail intermediate host. In particular, the land cover types entered in the model in this area are indicators of marginal uncultivable and sloping zones where typically there is the presence of water (permanently or temporarily). In addition, since NDVI can be used as an indicator of regional thermal-moisture regime, the distribution of farms positive for paramphistomosis corresponding to relatively high values of winter NDVI indicated the presence of adequate moisture and temperatures favourable to the rumen fluke and the snails. In conclusion, GIS and RS are useful to define the habitats of parasites, especially for those with strong environmental determinants, and to produce forecasting maps requested for the planning and the monitoring of control strategies on small and large scale.

Animals↗
PubMed2005

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk.

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

Aedes↗
PubMed2004

[A review on research of implement application for crop simulation model in regional scale by using remote sensing technology].

When applying crop simulation model developed from plot-scale level to regional scale, some challenges such as acquirement of some broader spatial data and regionalization of parameters will arise, while satellite can monitor crop growth status by remote sensing. Therefore, regional application of crop simulation model will be implement by remote sensing technique. In this paper, some research advances are reviewed, which include the methods of biophysical parameters retrieved from remote sensing data for crop model, the approach of biomass obtained by remote sensing data directly, and the means of matching temporal and spatial resolution between remote sensing data and crop model input. Three solutions (forcing, recalibration and validation) to the application of crop model in region scale by using remote sensing technique were emphasized, and the current problem and future research orientation in this filed were discussed in brief.

Computer Simulation↗
PubMed1998

Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies.

Remote sensing and geographic information system (GIS) technologies were used to discriminate between 130 villages, in the Nile Delta, at high and low risk for filariasis, as defined by microfilarial prevalence. Landsat Thematic Mapper (TM) data were digitally processed to generate a map of landcover as well as spectral indices such as NDVI and moisture index. A Tasseled Cap transformation was also carried out on the TM data which produced three more indices: brightness, greenness and wetness. GIS functions were used to extract information on landcover and spectral indices within one km buffers around the study villages. The relationship between satellite data and prevalence was investigated using discriminant analysis. The analysis indicated that the most important landscape elements associated with prevalence were water and marginal vegetation, while wetness and moisture index were the most important indices. Discriminant functions generated for these variables were able to correctly predict 80% and 74% of high and low prevalence villages, respectively, with an overall accuracy of 77%. The present approach provides a promising tool for regional filariasis surveillance and helps direct control efforts.

Animals↗
PubMed2005

Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination.

Wetland ecosystems of California are located in highly populated areas and subject to high levels of contamination. Monitoring of wetlands to assess degrees of pollution damage requires periodic retrieval of information over large areas, which can be effectively accomplished by rapidly evolving remote sensing technologies. The biophysical principles of remote sensing of vegetation under stress need to be understood in order to correctly interpret the information obtained at the scale of canopies. To determine the potential to remotely characterize and monitor pollution, plants of Salicornia virginica, a major component of wetland communities in California, were treated with two metals and two crude oil types to study their sensitivity to pollutants and how this impacted their reflectance characteristics. Several growth and physiological parameters, as well as shoot reflectance were measured and correlated with symptoms and contamination levels. Significant differences between treatments were found in at least some of the measured parameters in all pollutants. Reflectance was sensitive to early stress levels only for cadmium and the lightweight petroleum. Pollutants that differ in their way of action also had different plant reflectance signatures. The high degree of correlation between reflectance features and stress indicators highlights the potential of using remote sensing to assess the type and degree of pollution damage.

Automation↗
PubMed2006

Spectral and landscape characterization of filarious and non-filarious villages in Egypt.

Filarial disease is endemic in Egypt in some villages of Nile Delta governorates where it is transmitted by Culex pipiens female. GIS functions are used to identify environmental indicators of high-risk village as indicated by mosquito density, human infection rate, vector species composition, mean life expectancy "e(o)" & environmental variables (geology, hydrology, soil types, etc) as well as meteorological factors (temperature, RH and rainfall) in relation to filaria transmission risk. Remote-sensing technology was used to distinguish between the two studied villages as high and non-infected, as defined by microfilarial prevalence. The results indicate that filaria transmission risk is higher at an area characterized by highly productive aquifers, silt clay soil, receiving little amount of rain with low relative humidity (RH). The results indicate that the most important landscape elements associated with prevalence are water and different vegetation. This work showed that the integration between GIS and remote sensing technologies to analyze and identify the environmental factors, associated with the disease, did not only allow mapping icurrent spatial patterns, but also predicting its distribution under expected future developmental and environmental changes.

Animals↗
PubMed2005

The value of site-specific information and the environment: technology adoption and pesticide use under uncertainty.

Remote sensing technology offers an opportunity to significantly increase the amount of site-specific information about field characteristics such as pest populations. Coupled with variable rate application technologies, this added information has the potential to provide environmental benefits through reduced pesticide applications. However, producers face a complicated adoption decision because output prices and crop yields are uncertain. A model is developed to examine the potential value of remote sensing information to pesticide applications in an option-value framework under uncertainty. Simulations suggest that remote sensing information could decrease pesticide use, but uncertainty and irreversibility are likely to limit technological adoption by farmers. Potential cost-share subsidies are discussed.

Agriculture↗
PubMed2001

Validation of satellite data for quality assurance in lake monitoring applications.

The operational application of remote sensing technologies to lake water quality monitoring requires products derived from remote sensing to be quantitatively self-consistent and have a certified accuracy. Fundamental elements in this quality assurance framework are sensor radiometric calibration and atmospheric correction models, which are briefly discussed in the paper. In order to evaluate the accuracy of present operational techniques to retrieve basic parameters from satellite data, such as water-leaving radiance and reflectance, an experiment was organised in the frame of SAtellite remote sensing for Lake MONitoring (SALMON), a European Union co-funded research project. A series of ship-based radiometric and atmospheric measuring campaigns were conducted on Lake Iseo and Lake Garda (Italy) together with limnological sampling. Four Landsat-5 Thematic Mapper (TM) scenes were acquired during different seasons and simultaneous in situ measurements were made. After the radiometric calibration procedure, satellite digital images were processed by applying two entirely image-based atmospheric correction models. These models account for the effects of both additive scattering and multiplicative transmittance effects in the atmosphere on the at-satellite measured signal. The results achieved using these procedures were evaluated by comparing satellite-based estimates with in situ measurements of water reflectance. The root mean square difference between Landsat TM-derived reflectance values and ground measurements was close to 0.010 reflectance for each TM spectral band. Such image-based correction models, requiring no in situ field measurements during the satellite overpass, constitute a valid method of lake water monitoring.

Atmosphere↗
PubMed2004

Remote sensing of the urban heat island and its changes in Xiamen City of SE China.

World-wide urbanization has significantly modified the landscape, which has important climatic implications across all scales due to the simultaneous removal of natural land cover and introduction of urban materials. This resulted in a phenomenon known as an urban heat island (UHI). A study on the UHI in Xiamen of China was carried out using remote sensing technology. Satellite thermal infrared images were used to determine surface radiant temperatures. Thermal remote sensing data were obtained from band 6 of two Landsat TM/ETM+ images of 1989 and 2000 to observe the UHI changes over 11-year period. The thermal infrared bands were processed through several image enhancement technologies. This generated two 3-dimension-perspective images of Xiamen's urban heat island in 1989 and 2000, respectively, and revealed heat characteristics and spatial distribution features of the UHI. To find out the change of the UHI between 1989 and 2000, the two thermal images were first normalized and scaled to seven grades to reduce seasonal difference and then overlaid to produce a difference image by subtracting corresponding pixels. The difference image showed an evident development of the urban heat island in the 11 years. This change was due largely to the urban expansion with a consequent alteration in the ratio of sensible heat flux to latent heat flux. To quantitatively compare UHI, an index called Urban-Heat-Island Ratio Index (URI) was created. It can reveal the intensity of the UHI within the urban area. The calculation of the index was based on the ratio of UHI area to urban area. The greater the index, the more intense the UHI was. The calculation of the index for the Xiamen City indicated that the ratio of UHI area to urban area in 2000 was less than that in 1989. High temperatures in several areas in 1989 were reduced or just disappeared, such as those in old downtown area and Gulangyu Island. For the potential mitigation of the UHI in Xiamen, a long-term heat island reduction strategy of planting shade trees and using light-colored, highly reflective roof and paving materials should be included in the plans of the city planers, environmental managers and other decision-makers to improve the overall urban environment in the future.

China↗
PubMed2000

Environmental information systems for the control of arthropod vectors of disease.

Over the last decade, remote sensing technologies and geographical information systems have moved from the research arena into the hands of vector control specialists. This review explains remote sensing approaches and spatial information technologies used for investigations of arthropod pests and vectors of diseases affecting humans and livestock. Relevant applications are summarized with examples of studies on African horse sickness vector Culicoides midges (Diptera: Ceratopogonidae), malaria vector Anopheles and arbovirus vector culicine mosquitoes (Diptera: Culicidae), leishmaniasis vector Phlebotomus sandflies (Diptera: Psychodidae), trypanosomiasis vector tsetse (Diptera: Glossinidae), loaiasis vector Chrysops (Diptera: Tabanidae), Lyme disease vector Ixodes and other ticks (Acari: Ixodidae). Methods and their uses are tabulated and discussed with recommendations for efficiency, caution and progress in this burgeoning field.

Arthropod Vectors↗
PubMed2000

Education, outreach and the future of remote sensing in human health.

The human health community has been slow to adopt remote sensing technology for research, surveillance, or control activities. This chapter presents a brief history of the National Aeronautics and Space Administration's experiences in the use of remotely sensed data for health applications, and explores some of the obstacles, both real and perceived, that have slowed the transfer of this technology to the health community. These obstacles include the lack of awareness, which must be overcome through outreach and proper training in remote sensing, and inadequate spatial, spectral and temporal data resolutions, which are being addressed as new sensor systems are launched and currently overlooked (and underutilized) sensors are newly discovered by the health community. A basic training outline is presented, along with general considerations for selecting training candidates. The chapter concludes with a brief discussion of some current and future sensors that show promise for health applications.

Communicable Disease Control↗
PubMed2006

Water quality monitoring using remote sensing in support of the EU water framework directive (WFD): a case study in the Gulf of Finland.

Water quality monitoring using remote sensing has been studied in Finland for many years. But there are still few discussions on water quality monitoring using remote sensing technology in support of water policy and legislation in Finland under the WFD. In this study, we present water quality monitoring using remote sensing in the Gulf of Finland, and focus on the spatial distribution of water quality information from satellite-based observations in support of water policy by a case study of nitrate concentrations in surface waters. In addition, we briefly describe instruments using a system of river basin districts (RBD), highlighting the importance of integrated water resources and river-basin management in the WFD, and discuss the role of water quality monitoring using remote sensing in the implementation of water policy in Finland under the WFD.

Environmental Monitoring↗
PubMed2005

Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing.

An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 microm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 microg/m3 with a corresponding average PM2.5 concentration of 13.8 microg/m3. When PM2.5 concentrations greater than 40 microg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.

Air Movements↗
PubMed2003

Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya.

BACKGROUND: Remote sensing technology provides detailed spectral and thermal images of the earth's surface from which surrogate ecological indicators of complex processes can be measured. METHODS: Remote sensing data were overlaid onto georeferenced entomological and human ecological data randomly sampled during April and May 2001 in the cities of Kisumu (population asymptotically equal to 320,000) and Malindi (population asymptotically equal to 81,000), Kenya. Grid cells of 270 meters x 270 meters were used to generate spatial sampling units for each city for the collection of entomological and human ecological field-based data. Multispectral Thermal Imager (MTI) satellite data in the visible spectrum at five meter resolution were acquired for Kisumu and Malindi during February and March 2001, respectively. The MTI data were fit and aggregated to the 270 meter x 270 meter grid cells used in field-based sampling using a geographic information system. The normalized difference vegetation index (NDVI) was calculated and scaled from MTI data for selected grid cells. Regression analysis was used to assess associations between NDVI values and entomological and human ecological variables at the grid cell level. RESULTS: Multivariate linear regression showed that as household density increased, mean grid cell NDVI decreased (global F-test = 9.81, df 3,72, P-value = <0.01; adjusted R2 = 0.26). Given household density, the number of potential anopheline larval habitats per grid cell also increased with increasing values of mean grid cell NDVI (global F-test = 14.29, df 3,36, P-value = <0.01; adjusted R2 = 0.51). CONCLUSIONS: NDVI values obtained from MTI data were successfully overlaid onto georeferenced entomological and human ecological data spatially sampled at a scale of 270 meters x 270 meters. Results demonstrate that NDVI at such a scale was sufficient to describe variations in entomological and human ecological parameters across both cities.

Journal Article↗