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Special Issue "Remote Sensing in Public Health"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2011)

Special Issue Editors

Guest Editor
Dr. Patrick Kinney

Department of Environmental Health Sciences Mailman School of Public Health, Columbia University, 60 Haven Ave., B-1 New York, NY 10032, USA
E-Mail
Interests: intersection of global environmental change; human health; and policy; with an emphasis on the public health impacts of climate change and air pollution
Guest Editor
Dr. Pietro Ceccato

International Research Institute for Climate and Society, The Earth Institute, Columbia University, Lamont Campus 61, Route 9W, Palisades, NY 10964, USA
E-Mail
Phone: +1 (845) 680-4425
Interests: Create new methods to monitor Earth observation parameters from remotely sensed data with emphasis on vegetation, temperature, precipitation, surface moisture, and fire activity; Integrate climate and environmental monitoring information into applications for Human Health, Agriculture, Food Security, Natural Disaster and Pest Management

Special Issue Information

Dear Colleagues,

Good health is one of the primary aspirations of human social development. As a consequence, health indicators are key components of the human development indices by which we measure progress toward sustainable development. Certain diseases and causes of ill health are associated with particular environmental and climate conditions. Changes in the natural environment and climate can thus compromise human and animal health. Droughts may lead to malnutrition, dust storms and smog can cause respiratory illnesses, and algal blooms contaminate seafood. Emerging and re-emerging infectious diseases can spread whenever ecosystems change.

Spatial information derived from remotely-sensed data or models is playing an increasingly important role in understanding the relationship between health and environmental factors, in addition to locating and forecasting disease outbreaks. Remote Sensing and associated spatial modeling techniques hold particular potential for efficient monitoring and forecasting of human and animal diseases; developing policies and implementing interventions aimed at better controlling these diseases.

This special issue of Remote Sensing solicits papers that present innovative Remote Sensing applications and related spatial modeling techniques to support monitoring and forecasting human and animal health in order to support efforts to better manage those factors that risk to compromise it.

Dr. Pietro Ceccato
Dr. Patrick Kinney
Guest Editors

Keywords

  • remote sensing
  • earth observation
  • human and animal health
  • aeroallergens
  • air quality
  • infectious diseases
  • emerging and re-emerging diseases
  • early warning systems

Published Papers (3 papers)

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Research

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Open AccessArticle Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat
Remote Sens. 2011, 3(11), 2364-2383; doi:10.3390/rs3112364
Received: 29 August 2011 / Revised: 12 October 2011 / Accepted: 19 October 2011 / Published: 7 November 2011
Cited by 21 | PDF Full-text (4849 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light
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Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues. Full article
(This article belongs to the Special Issue Remote Sensing in Public Health)
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Open AccessArticle Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study
Remote Sens. 2011, 3(9), 1805-1816; doi:10.3390/rs3091805
Received: 8 July 2011 / Revised: 16 August 2011 / Accepted: 18 August 2011 / Published: 25 August 2011
Cited by 7 | PDF Full-text (407 KB) | HTML Full-text | XML Full-text
Abstract
Exposure to pesticides has been associated with increased risk of many adverse health effects. To understand the relationships between pesticide exposure and health outcomes, epidemiologists need information on where pesticides are applied in the environment. California maintains one of the most comprehensive pesticide
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Exposure to pesticides has been associated with increased risk of many adverse health effects. To understand the relationships between pesticide exposure and health outcomes, epidemiologists need information on where pesticides are applied in the environment. California maintains one of the most comprehensive pesticide use reporting systems in the world, yet the data are only recorded at a coarse geographic scale of approximately 2.6 km2 area. A method is presented that uses Landsat image time series to downscale California pesticide use data to the crop field-level. The approach is demonstrated using paraquat applied to vineyard and cotton fields. Full article
(This article belongs to the Special Issue Remote Sensing in Public Health)

Review

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Open AccessReview Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis
Remote Sens. 2011, 3(11), 2321-2345; doi:10.3390/rs3112321
Received: 15 August 2011 / Revised: 7 October 2011 / Accepted: 20 October 2011 / Published: 27 October 2011
Cited by 3 | PDF Full-text (512 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous
[...] Read more.
The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or “OBIA”) can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work. Full article
(This article belongs to the Special Issue Remote Sensing in Public Health)

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