Special Issue "Remote Sensing Applications to Human Health"

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

Deadline for manuscript submissions: closed (30 June 2017).

Special Issue Editors

Prof. Bing Xu
E-Mail Website
Guest Editor
Center for Earth System Science, Tsinghua University, MengMinWei Hall, Beijing 100084, China
Interests: remote sensing; climate change; infectious disease; human health; ecological modeling; epidemiology; pathogen evolution
Prof. Nils Chr. Stenseth
E-Mail Website
Guest Editor
Centre for Ecological and Evolutionary Synthesis, University of Oslo, Oslo, Norway
Interests: population biology; large-scale ecological and evolutionary patterns; effects of climate variation; terrestrial, marine and freshwater systems; vector-borne infectious diseases
Dr. Raul Zurita-Milla
E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Interests: spatio-temporal analysis; time series, process modelling and integration of remote sensing and GIS for applications in phenology, agriculture, land use/land cover, epidemiology and public health

Special Issue Information

Dear Colleagues,

Satellite remote sensing has been continuously providing spatially and temporally consistent Earth observation data, applied for health research, over the past four decades. Association studies have since been explored between remote sensing derived environmental data and pathogen activity, host abundance, and disease outbreaks and transmission. Incorporating longitudinal series of remotely sensed data into models has played an important role in parameterizing the spatio-temporal process of disease transmission. Remote sensing has also allowed us to extrapolate from what we know about a few smaller locations to a much larger region.

New satellites will offer even higher-resolution imagery with more robust algorithms to process the data. We expect to dramatically expand our ability to view and understand Earth’s land, water, and air. Our social and economic systems are exerting significant impacts on our natural environment, resulting in global environmental problems, such as climate change, ozone layer depletion and air pollution. The changing environment, in return, is having significant consequences on human society, such as rising temperatures and sea level, and increased frequency in natural disasters and human disease. Remote sensing provides various ways of modelling this nature-human interaction system, has been used to answer questions as how this changing environment has impacted our health, and how we humans respond and adapt to this changing environment.

Remote sensing has benefited macro-scale studies and often up to the planetary level, while human health is related to individuals in micro-scale studies and often down to the molecular level. How remote sensing can be linked, applied and contributed to human health research is a challenge. It requires novel minds and inter-disciplinary approach to revolutionize the unique area and tackle such linkages and issues.

We invite you to submit your recent research on remote sensing applications to human health, particularly addressing the following topics:

  • monitoring terrestrial habitats of disease vectors
  • impact of climate change on infectious disease outbreaks and transmission
  • global warming and non-infectious diseases
  • pollutants and health hazards
  • urbanization and health consequences
  • disease modelling incorporating remotely sensed data
  • response and adaptation to health impact
  • GIS and big data computation for health research
  • health care access and health policy stipulation

Prof. Bing Xu
Prof. Nils Chr. Stenseth
Dr. Raul Zurita-Milla
Guest Editors

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 papers will be 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 2000 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.

Published Papers (8 papers)

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Open AccessArticle
Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities
Remote Sens. 2017, 9(12), 1266; https://doi.org/10.3390/rs9121266 - 07 Dec 2017
Cited by 10
Abstract
Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by [...] Read more.
Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China
Remote Sens. 2017, 9(10), 1076; https://doi.org/10.3390/rs9101076 - 22 Oct 2017
Cited by 4
Abstract
Striped field mice (Apodemus agrarius) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease [...] Read more.
Striped field mice (Apodemus agrarius) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease risk. Thus, a higher population density of A. agrarius in an area might indicate a higher risk for an HFRS outbreak. Here, we surveyed the A. agrarius population density between 2005 and 2012 on the Weihe Plain, Shaanxi Province, China, and used this monitoring data to examine the relationships between the dynamics of A. agrarius populations and environmental conditions of crop-land, represented by remote sensing based indicators. These included the normalized difference vegetation index, leaf area index, fraction of photosynthetically active radiation absorbed by vegetation, net photosynthesis (PsnNet), gross primary productivity, and land surface temperature. Structural equation modeling (SEM) was applied to detect the possible causal relationship between PsnNet, A. agrarius population density and HFRS risk. The results showed that A. agrarius was the most frequently captured species with a capture rate of 0.9 individuals per hundred trap-nights, during 96 months of trapping in the study area. The risk of HFRS was highly associated with the abundance of A. agrarius, with a 1–5-month lag. The breeding season of A. agrarius was also found to coincide with agricultural activity and seasons with high PsnNet. The SEM indicated that PsnNet had an indirect positive effect on HFRS incidence via rodents. In conclusion, the remote sensing-based environmental indicator, PsnNet, was highly correlated with HTNV reservoir population dynamics with a 3-month lag (r = 0.46, p < 0.01), and may serve as a predictor of potential HFRS outbreaks. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis
Remote Sens. 2017, 9(10), 1018; https://doi.org/10.3390/rs9101018 - 30 Sep 2017
Cited by 9
Abstract
Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify [...] Read more.
Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify important environmental determinants of the disease and predict its outbreaks. A novel artificial neural network (ANN) model was developed, using annual county-level numbers of HB cases and data on 37 environmental variables, potentially associated with HB in Inner Mongolia, China. Data from 2006 to 2008 were used to train, validate and test the model, while data for 2009–2010 were used to assess the model’s performance. The Enhanced Vegetation Index was identified as the most important predictor of HB incidence, followed by land surface temperature and other temperature- and precipitation-related variables. The suitable ecological niche of HB was modeled based on these predictors. Model estimates were found to be in good agreement with reported numbers of HB cases in both the model development and assessment phases. The study suggests that HB outbreaks may be predicted, with a reasonable degree of accuracy, using the ANN model and environmental variables obtained from satellite data. The study deepened the understanding of environmental determinants of HB and advanced the methodology for prediction of climate-sensitive infectious disease outbreaks. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
Mapping and Assessment of PM10 and O3 Removal by Woody Vegetation at Urban and Regional Level
Remote Sens. 2017, 9(8), 791; https://doi.org/10.3390/rs9080791 - 01 Aug 2017
Cited by 11
Abstract
This study is the follow up of the URBAN-MAES pilot implemented in the framework of the EnRoute project. The study aims at mapping and assessing the process of particulate matter (PM10) and tropospheric ozone (O3) removal by various forest [...] Read more.
This study is the follow up of the URBAN-MAES pilot implemented in the framework of the EnRoute project. The study aims at mapping and assessing the process of particulate matter (PM10) and tropospheric ozone (O3) removal by various forest and shrub ecosystems. Different policy levels and environmental contexts were considered, namely the Metropolitan city of Rome and, at a wider level, the Latium region. The approach involves characterization of the main land cover and ecosystems using Sentinel-2 images, enabling a detailed assessment of Ecosystem Service (ES), and monetary valuation based on externality values. The results showed spatial variations in the pattern of PM10 and O3 removal inside the Municipality and in the more rural Latium hinterland, reflecting the spatial dynamics of the two pollutants. Evergreen species displayed higher PM10 removal efficiency, whereas deciduous species showed higher O3 absorption in both rural and urban areas. The overall pollution removal accounted for 5123 and 19,074 Mg of PM10 and O3, respectively, with a relative monetary benefit of 161 and 149 Million Euro for PM10 and O3, respectively. Our results provide spatially explicit evidence that may assist policymakers in land-oriented decisions towards improving Green Infrastructure and maximizing ES provision at different governance levels. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling
Remote Sens. 2017, 9(6), 609; https://doi.org/10.3390/rs9060609 - 15 Jun 2017
Cited by 2
Abstract
The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern [...] Read more.
The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern United States into southeastern Ontario. Several factors such as climate change, changes in host abundance, host and vector migration, or possibly a combination of these factors likely contribute to the emergence of Lyme disease cases in eastern Ontario. This study first determined areas of warming using time series remotely sensed temperature data within Ontario, then analyzed possible spatial-temporal changes in Lyme disease risk in eastern Ontario from 2000 to 2013 due to climate change using tick population modeling. The outputs of the model were validated by using tick surveillance data from 2002 to 2012. Our results indicated areas in Ontario where Lyme disease risk changed from unsustainable to sustainable for sustaining Ixodes scapularis (black-legged tick) populations. This study provides evidence that climate change has facilitated the northward expansion of black-legged tick populations’ geographic range over the past decade. The results demonstrate that remote sensing data can be used to increase the spatial detail for Lyme disease risk mapping and provide risk maps for better awareness of possible Lyme disease cases. Further studies are required to determine the contribution of host migration and abundance on changes in eastern Ontario’s Lyme disease risk. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees
Remote Sens. 2017, 9(4), 328; https://doi.org/10.3390/rs9040328 - 30 Mar 2017
Cited by 10
Abstract
Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the [...] Read more.
Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle
Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China
Remote Sens. 2017, 9(3), 221; https://doi.org/10.3390/rs9030221 - 01 Mar 2017
Cited by 24
Abstract
Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM [...] Read more.
Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. Through assembling techniques, including linear mixed effect regressions with a spatial-varying coefficient, a maximum likelihood estimator and the spatiotemporal Kriging together, we develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1° over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.0 μg/m3 and R2 = 0.72) and outperforms either AOD-derived PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-derived PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides spatiotemporally complete predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.7 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81 across China during 2014. Fused estimates will be publicly available for future health-related studies. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessTechnical Note
Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data
Remote Sens. 2017, 9(10), 971; https://doi.org/10.3390/rs9100971 - 21 Sep 2017
Cited by 4
Abstract
Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning [...] Read more.
Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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