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Integrating Remote Sensing and GIS in Environmental Health Assessment

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3371

Special Issue Editors


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Guest Editor
Department of Environmental Sciences, College of the Coast and Environment, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: geographic information science; remote sensing; spatial analysis; environmental health; disaster resilience; sustainability
Special Issues, Collections and Topics in MDPI journals
Department of Geography, Texas A&M University, 3147 TAMU, College Station, TX 77843, USA
Interests: spatial modeling; geographic information science and technology (GIST); disaster resilience; coupled nature–human system modeling
Special Issues, Collections and Topics in MDPI journals
Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
Interests: geoinformatics; spatial computation and modeling of community resilience/sustainability; data science and statistics in land use; geo-simulation of human and environmental systems; GeoAI (artificial intelligence) frameworks; integrated geo-cyber-infrastructures; urban planning; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are calling for papers for a Special Issue on “Integrating Remote Sensing and GIS in Environmental Health Assessments”. Remote sensing and GIS applications in environmental health and health impact assessments have been around for many decades, such as for using satellite data to monitor the timing and spread of cholera, identifying high-risk areas of malaria and schistosomiasis, evaluating the health impacts from air pollution, estimating urban heat island health effects, and so on. With the increasing intensity of human activities and the complex threats from climate change, existing and emerging threats to human health due to environmental factors are on the rise. Meanwhile, recent advances in remote sensing and GIS technology, including improved satellites and other remote sensing devices and new algorithms such as GeoAI, propel the development of better detection, assessment, monitoring, and prediction models. It will be most fitting to devote this Special Issue to demonstrate the current state of knowledge and cutting-edge methods for environmental health assessments using integrated remote sensing and GIS technologies. This Special Issue will help identify the new challenges that we are facing and will face in the future, as well as inform policies to reduce health risks and impacts.

This forthcoming Special Issue invites manuscripts that examine a broad range of topics on environmental health and health risk assessments using integrating remote sensing and GIS technologies. Potential topics include, but are not limited to, the following:

  • State-of-the-art technologies and applications in health impact assessments using integrated remote sensing and GIS methods;
  • Detection, assessment, and prediction of existing and emerging diseases;
  • Land use and land cover change and their effects on environmental health;
  • Effects of air and water pollution and human activities, such as industrial facilities and deforestation, on environmental health;
  • Effects of natural disasters such as flooding, drought, and wildfires on environmental health;
  • Environmental justice and environmental health;
  • Health resilience under climate change;
  • Scale effects on modeling and prediction;
  • Integrating human mobility and use of wearable smart sensors for human health risk assessments;
  • Urban infrastructure, urban design, green space, urban heat islands, and health impact assessments;
  • Applications of artificial intelligence in environmental health assessments;
  • Use of nonconventional detection devices such as drones, Google Street images, GPS sensors, and social sensors to identify human exposure;
  • Mitigation and adaptation strategies derived from modeling and geo-simulation.

Prof. Dr. Nina Lam
Dr. Heng Cai
Dr. Kenan Li
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 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

  • environmental health risk assessment
  • land use/land cover change
  • health vulnerability, resilience, and sustainability
  • spatial modeling and simulation
  • spatial data fusion
  • human dynamic sensor technology
  • remote sensing image processing

Published Papers (2 papers)

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Research

22 pages, 30453 KiB  
Article
Modeling the Land Cover Change in Chesapeake Bay Area for Precision Conservation and Green Infrastructure Planning
by Xinge Zhang, Kenan Li, Yuewen Dai and Shujing Yi
Remote Sens. 2024, 16(3), 545; https://doi.org/10.3390/rs16030545 - 31 Jan 2024
Viewed by 748
Abstract
This study developed a precise land cover model to predict the shifts from pervious to impervious surfaces in the Chesapeake watershed. Utilizing 1 m resolution longitudinal land cover data from the Conservation Innovation Center (CIC), our model achieved impressive balanced accuracies: 98.96% for [...] Read more.
This study developed a precise land cover model to predict the shifts from pervious to impervious surfaces in the Chesapeake watershed. Utilizing 1 m resolution longitudinal land cover data from the Conservation Innovation Center (CIC), our model achieved impressive balanced accuracies: 98.96% for Portsmouth, 99.88% for Isle of Wight, and 95.76% for James City. Based on the analysis of feature importance, our model also assessed the influence of local socioeconomic and environmental factors, along with their spatial lags as represented by natural splines. These outcomes and findings are crucial for land use and environmental planners, providing them with tools to identify areas of urban expansion and to devise appropriate green infrastructure strategies, while also prioritizing land conservation. Additionally, our model offers insights into the socioeconomic and environmental drivers behind land cover changes. Its adaptability at the county level and reliance on widely available data make it a viable option for other municipalities within the Chesapeake basin to conduct similar analyses. As a proof-of-concept, this project underscores the potential of precision conservation in facilitating both land preservation and the advancement of green infrastructure planning, thus serving as a valuable resource for policymakers and planners in the region. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and GIS in Environmental Health Assessment)
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31 pages, 2818 KiB  
Article
Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy
by Tommaso Orusa, Annalisa Viani, Boineelo Moyo, Duke Cammareri and Enrico Borgogno-Mondino
Remote Sens. 2023, 15(9), 2348; https://doi.org/10.3390/rs15092348 - 29 Apr 2023
Cited by 15 | Viewed by 1870
Abstract
Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was [...] Read more.
Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries–Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and GIS in Environmental Health Assessment)
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