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Applications of Remote Sensing for One Health
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Applications of Remote Sensing for One Health

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8483

Special Issue Editors

Prof. Dr. Damien Arvor

E-Mail Website
Guest Editor
CNRS, UMR6554LETG, Université Rennes 2, Place du Recteur H. Le Moal, CEDEX, 35043 Rennes, France
Interests: climate change; land use and land cover change; agricultural frontiers; Amazon; agrosystems; forest dynamics; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinwei Dong

E-Mail Website
Guest Editor
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing of global change; land cover and land use change; integrated monitoring and assessment of terrestrial ecosystem
Special Issues, Collections and Topics in MDPI journals
Dr. Zhichao Li

E-Mail Website
Guest Editor
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: health geography; climate change; land use and cover changes; applications of machine learning and deep learning in disease risk modeling
Prof. Dr. Xiangming Xiao

E-Mail Website
Guest Editor
Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, 101 David L. Boren Blvd., Norman, OK 73019-5300, USA
Interests: applications of remote sensing and GIS in ecosystems science and natural resources; land use and cover changes; ecosystem service assessment; biogeochemistry of terrestrial ecosystems; ecosystem modeling at large spatial scales; integrated impact assessment of climate change; ecology and epidemiology of infectious diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The changes in climate and land use have increased the probability of pathogen spillover between wild animals, domestic animals, and humans, which then increase the risk of infectious disease transmission worldwide. The “One Health” approach, which aims to explore the interactions between the environment, animals, and humans, promotes multidisciplinary and collaborative research and practices at local, regional, and global scales. Satellite images and other ready-to-use geospatial datasets contain rich information on climate, land use and land cover, ecology, and population. Accompanied by innovative spatial and temporal methodologies in remote sensing and geographic information science, big geospatial data can be used to advance infectious disease research, including the identification of risk drivers, disease risk mapping, and early detection and warning of disease outbreaks toward defining better strategies for disease control and prevention.

This Special Issue, “Applications of Remote Sensing for One Health”, aims to capture recent advancements in the application of big geospatial data in One Health-related studies and promote the realization of the “One Health” concept from theory to practice. Potential topics include (but are not limited to) the following: (i) integration, fusion, and analyses of multi-source and heterogeneous datasets for characterizing the human–animal–environment systems at various spatial and temporal scales, for example, climate, land use, environment, socioeconomic, and population; (ii) the use of big geospatial data and various models and computing technologies (e.g., machine learning and deep learning) to explore the hidden interrelations between the environment, animal, and human systems.

Prof. Dr. Damien Arvor
Prof. Dr. Jinwei Dong
Dr. Zhichao Li
Prof. Dr. Xiangming Xiao
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

  • one health
  • zoonosis
  • environmental health
  • animal health
  • epidemiology
  • big geospatial data
  • data collection and integration
  • geospatial artificial intelligence
  • remote sensing

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Published Papers (3 papers)

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Research

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12 pages, 2586 KiB  
Article
Landscape Patterns and Topographic Features Affect Seasonal River Water Quality at Catchment and Buffer Scales
by Li Deng, Wanshu Li, Xiaojie Liu, Yazhu Wang and Lingqing Wang
Remote Sens. 2023, 15(5), 1438; https://doi.org/10.3390/rs15051438 - 3 Mar 2023
Cited by 9 | Viewed by 1818
Abstract
Effects of landscape patterns or topographic features on the river water environment have been broadly studied to control non-point source (NPS) pollution and to cut off potential pathways for pollutants to affect human health. However, spatio-temporal dynamics and scale effects with respect to [...] Read more.
Effects of landscape patterns or topographic features on the river water environment have been broadly studied to control non-point source (NPS) pollution and to cut off potential pathways for pollutants to affect human health. However, spatio-temporal dynamics and scale effects with respect to the impact of landscape patterns and topographic features on the aquatic environment over successive years have not been elucidated. In this study, water quality parameters and land cover data for three consecutive years mainly in Tangshan City, located in the northeast of the Haihe River Basin, China, were obtained to determine the associations between landscape patterns and topographic features with the water environment. Results indicated that seasonal differences in dissolved oxygen (DO) and total nitrogen (TN) were significant (p < 0.001), and spatial variation was generally observed for each water quality parameter. Redundancy analysis revealed that landscape patterns and topographic features have different impacts on the aquatic environment as seasonal spans and spatial scales change. Overall, the best explanatory variables explained an average of 58.6% of the variation in water quality at various spatial scales over the two seasons. Topographic features made a greater contribution to river water quality changes at the buffer scale; conversely, at the catchment scale, water quality changes stemmed primarily from differences in landscape composition and configuration. The landscape shape index of cropland (LSIcrop) was an important factor influencing seasonal river water quality changes at various spatial scales. These results suggest that considering landscape connectivity at distinct spatial scales could enhance the understanding of the alteration of hydrological processes across multiple topographic features, which in turn has an impact on seasonal river water. Full article
(This article belongs to the Special Issue Applications of Remote Sensing for One Health)
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18 pages, 7732 KiB  
Article
Runoff Responses of Various Driving Factors in a Typical Basin in Beijing-Tianjin-Hebei Area
by Zhaohui Feng, Siyang Liu, Yikai Guo and Xiaojie Liu
Remote Sens. 2023, 15(4), 1027; https://doi.org/10.3390/rs15041027 - 13 Feb 2023
Cited by 5 | Viewed by 1532
Abstract
Changes in land use and landscape caused by human activities, rapid socioeconomic development and climate change disturb the water cycle process and impact the runoff. This study analyzed the runoff responses to different driving factors in a typical basin in the Beijing-Tianjin-Hebei region [...] Read more.
Changes in land use and landscape caused by human activities, rapid socioeconomic development and climate change disturb the water cycle process and impact the runoff. This study analyzed the runoff responses to different driving factors in a typical basin in the Beijing-Tianjin-Hebei region of North China combined with methods such as geographically and temporally weighted regression, landscape pattern indexes and Budyko theory. The results indicated that the runoff and runoff depth were higher in the central and south part and were lower in the northwest of the basin. Furthermore, the average runoff increased at the later stage of the study period. Artificial surface and land use intensity exerted positive impacts on runoff and runoff depth in most areas. The complex and diverse landscape with a high shape index blocked runoff to some extent. Moreover, runoff depth would increase by 0.724 mm or decrease by 0.069 mm when the rainfall or potential evaporation increased by 1 mm. In addition, population density and the economic development in both rural as well as urban areas put a heavy burden on runoff and water resource in this basin. From above it could be concluded that the impacts on runoff due to environmental change brought by human activities could not be neglected though the runoff was also greatly affected by climate change. This study reflected the runoff responses to driving factors in a typical basin of North China, which will provide reference for water resource protection and give enlightenment to water management. Full article
(This article belongs to the Special Issue Applications of Remote Sensing for One Health)
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Review

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22 pages, 2934 KiB  
Review
Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
by Zhichao Li and Jinwei Dong
Remote Sens. 2022, 14(19), 5052; https://doi.org/10.3390/rs14195052 - 10 Oct 2022
Cited by 6 | Viewed by 3959
Abstract
With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in [...] Read more.
With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting. Full article
(This article belongs to the Special Issue Applications of Remote Sensing for One Health)
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