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Remote Sensing
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Remote Sensing for Public Health
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Remote Sensing for Public Health

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 6276

Special Issue Editor

Prof. Dr. Hatim Sharif

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: hydrometeorology; hydrologic modeling and forecasting; environmental applications of remote sensing; natural hazards; public health; water quality modeling; transportation safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Public health aims to improve the quality of life through the prevention and control of disease. This is done through the surveillance of the environment, disease cases, and health indicators, and through the promotion of healthy behaviors. On the other hand, scientists use remote sensing to study the earth's biotic and abiotic components. Modern public health techniques require multidisciplinary teams of professionals working in these areas. Information gleaned from remotely sensed data is playing an increasingly important role in improving the health and well-being of people around the globe. For example, remote sensing data and techniques are used for measuring and predicting the quality of water (e.g. phytoplankton and red tides) and air (pollutant concentrations) and the biogeochemistry of various materials in the soil. In addition, they are also used to observe and understand environmental factors that control the onset and spread of many diseases such as influenza, cholera, dengue, and malaria. The decreased cost and product latency of advanced computational power and communication protocols (e.g., Internet of Things), their increased spatial, spectral, or temporal resolutions, and the new spatial modeling capabilities of geographic information systems are expanding the application of remote sensing beyond the research community into operational disease surveillance, control, and forecasting.

This Special Issue of Remote Sensing solicits papers that present innovative remote sensing applications and related geospatial modeling techniques to support the monitoring and forecasting of public health and the integration of historical and real-time health data with remote sensing data in disease surveillance, risk mapping, and remote sensing-based models of disease transmission and risk.

Prof. Dr. Hatim Sharif
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • human and animal health
  • disease modeling
  • air quality
  • water quality
  • risk mapping
  • infectious diseases
  • vector- and water-borne disease
  • monitoring terrestrial habitats of disease vectors

Published Papers (2 papers)

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18 pages, 3661 KiB  
Article
RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations
by Zeqiang Chen, Jin Luo, Nengcheng Chen, Ren Xu and Gaoyun Shen
Remote Sens. 2019, 11(13), 1585; https://doi.org/10.3390/rs11131585 - 4 Jul 2019
Cited by 9 | Viewed by 3148
Abstract
The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods [...] Read more.
The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods are time consuming when applied to large floodplains, high-resolution DEMs are not always available, and remote sensing imagery cannot be used alone to predict inundation. In this article, a new model for the highly accurate and rapid simulation of floodplains, called “RFim” (real-time inundation model), is proposed to simulate the real-time flooded area. The model combines remote sensing images with in situ data to find the relationship between the inundation extent and water level. The new approach takes advantage of remote sensing images, which have wide spatial coverage and high resolution, and in situ observations, which have continuous temporal coverage and are easily accessible. This approach has been applied in the study area of East Dongting Lake, representing a large floodplain, for inundation simulation at a 30 m resolution. Compared with the submerged extent from observations, the accuracy of the simulation could be more than 90% (the lowest is 93%, and the highest is 96%). Hence, the approach proposed in this study is reliable for predicting the flood extent. Moreover, an inundation simulation for all of 2013 was performed with daily water level observation data. With an increasing number of Earth observation satellites operating in space and high-resolution mappers deployed on satellites, it will be much easier to acquire large quantities of images with very high resolutions. Therefore, the use of RFim to perform inundation simulations with high accuracy and high spatial resolutions in the future is promising because the simulation model is built on remote sensing imagery and gauging station data. Full article
(This article belongs to the Special Issue Remote Sensing for Public Health)
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10 pages, 1267 KiB  
Letter
Forest Connectivity, Host Assemblage Characteristics of Local and Neighboring Counties, and Temperature Jointly Shape the Spatial Expansion of Lyme Disease in United States
by Yingying X. G. Wang, Kevin D. Matson, Yanjie Xu, Herbert H. T. Prins, Zheng Y. X. Huang and Willem F. de Boer
Remote Sens. 2019, 11(20), 2354; https://doi.org/10.3390/rs11202354 - 11 Oct 2019
Cited by 3 | Viewed by 2665
Abstract
Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the [...] Read more.
Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors. Full article
(This article belongs to the Special Issue Remote Sensing for Public Health)
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