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Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems

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

Deadline for manuscript submissions: closed (15 October 2020) | Viewed by 60706

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Special Issue Editor

Dr. Vincent Herbreteau

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Guest Editor

French National Research institute for Sustainable Development (IRD) in the UMR 228 ESPACE-DEV Research Unit, France
Interests: health geography; infectious diseases; spatial analysis; environmental modeling; remote sensing

Special Issue Information

Dear Colleagues,

Understanding and predicting the occurrence of diseases is a major challenge for health services in order to improve control and limit consequences, whether in humans, animals, or environmental health. It is now well documented that, in addition to the intrinsic determinants of individuals or those related to the causal agents of disease, interactions with the environment and society are major factors in the vulnerability of individuals, both in their exposure to disease and in the consequences of poor health conditions. Thus, geographical approaches have increasingly been used to provide a broader perspective on the environmental, economic, or social conditions that determine the health status of populations.

The use of remote sensing for health studies has increased almost exponentially since the early 1970s. The main applications are the use of low spatial resolution indices to describe temporal epidemiological series. Nevertheless, many studies have also shown the great potential of higher resolution images (spatial and radiometric) to measure locally the conditions favorable to the development of diseases. Today, remote sensing has a major role to play in the development of environmental monitoring to assist in the early detection of epidemics, which is a major challenge for health management.

This Special Issue aims to gather original articles and reviews showing practical applications of remote sensing in different areas of human, animal, or environmental health. In particular, it aims to show innovative uses of satellite images at different scales, whether locally with very high-resolution images, over larger areas at medium or low resolution, or as part of temporal monitoring. Critical approaches to these tools are also encouraged to better guide opportunities for integrating remote sensing into future health projects.

Dr. Vincent Herbreteau
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
spatial epidemiology
health geography
environmental modeling
disease surveillance
early-warning systems

Published Papers (11 papers)

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Research

Jump to: Review

19 pages, 5673 KiB

Open AccessArticle

Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis

by Leon Biscornet, Christophe Révillion, Sylvaine Jégo, Erwan Lagadec, Yann Gomard, Gildas Le Minter, Gérard Rocamora, Vanina Guernier-Cambert, Julien Mélade, Koussay Dellagi, Pablo Tortosa and Vincent Herbreteau

Remote Sens. 2021, 13(2), 325; https://doi.org/10.3390/rs13020325 - 19 Jan 2021

Cited by 7 | Viewed by 3650

Abstract

Leptospirosis, an environmental infectious disease of bacterial origin, is the infectious disease with the highest associated mortality in Seychelles. In small island territories, the occurrence of the disease is spatially heterogeneous and a better understanding of the environmental factors that contribute to the presence of the bacteria would help implement targeted control. The present study aimed at identifying the main environmental parameters correlated with animal reservoirs distribution and Leptospira infection in order to delineate habitats with highest prevalence. We used a previously published dataset produced from a large collection of rodents trapped during the dry and wet seasons in most habitats of Mahé, the main island of Seychelles. A land use/land cover analysis was realized in order to describe the various environments using SPOT-5 images by remote sensing (object-based image analysis). At each sampling site, landscape indices were calculated and combined with other geographical parameters together with rainfall records to be used in a multivariate statistical analysis. Several environmental factors were found to be associated with the carriage of leptospires in Rattus rattus and Rattus norvegicus, namely low elevations, fragmented landscapes, the proximity of urbanized areas, an increased distance from forests and, above all, increased precipitation in the three months preceding trapping. The analysis indicated that Leptospira renal carriage could be predicted using the species identification and a description of landscape fragmentation and rainfall, with infection prevalence being positively correlated with these two environmental variables. This model may help decision makers in implementing policies affecting urban landscapes and/or in balancing conservation efforts when designing pest control strategies that should also aim at reducing human contact with Leptospira-laden rats while limiting their impact on the autochthonous fauna. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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20 pages, 4045 KiB

Open AccessArticle

Changing Patterns of Malaria in Grande Comore after a Drastic Decline: Importance of Fine-Scale Spatial Analysis to Inform Future Control Actions

by Artadji Attoumane, Rahamatou Silai, Affane Bacar, Eric Cardinale, Gwenaëlle Pennober and Vincent Herbreteau

Remote Sens. 2020, 12(24), 4082; https://doi.org/10.3390/rs12244082 - 13 Dec 2020

Cited by 3 | Viewed by 3784

Abstract

Malaria has long been endemic in the Union of Comoros reaching an incidence of 15,045 cases for 100,000 inhabitants in 2010 (103,670 cases). Since then, strengthened control actions based on the distribution of Long-Lasting Insecticidal mosquito Nets and mass treatment have reduced malaria to a low level. However, it persists more specifically in Grande Comore, where 82% of cases were diagnosed between 2010 and 2016. This situation remains a challenge for health authorities seeking to eliminate malaria, by targeting transmission sites more precisely. In this context, this study aimed at mapping malaria at the finest scale, in order to describe its spatial distribution and identify possible environmental indicators. The National Malaria Control Program provided the 2016 data, the only year that could be mapped at the level of localities. This mapping revealed spatial autocorrelation between localities, especially in the east of the island with a major cluster around Itsinkoudi (using the Kulldorff’s spatial scan test). Secondary clusters showed that malaria remains present throughout the island in both rural and urban areas. We also analyzed satellite images (SPOT 5) with remote sensing techniques (Object-Based Image Analysis) to look for environmental indicators. Landscape analysis shows that malaria incidence is correlated across the island with low altitudes, and a larger proportion of grasslands or a fewer proportion of forested areas nearby (at less than 1km around villages). More locally in the east, malaria is linked to larger shrub areas. These relationships could be associated with the fact that lower altitude localities are more interconnected, such facilitating malaria transmission. In 2016, malaria persists in Grande Comore, showing new patterns with more cases in the eastern part of the island and the possibility of high incidences during the dry season. Precise mapping of epidemiological data and landscape analysis allow the identification of clusters and active transmission foci. They are important tools for health surveillance in order to optimize control actions on key transmission locations. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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45 pages, 8095 KiB

Open AccessArticle

Toward an Early Warning System for Health Issues Related to Particulate Matter Exposure in Brazil: The Feasibility of Using Global PM_2.5 Concentration Forecast Products

by Emmanuel Roux, Eliane Ignotti, Nelson Bègue, Hassan Bencherif, Thibault Catry, Nadine Dessay, Renata Gracie, Helen Gurgel, Sandra de Sousa Hacon, Mônica de A. F. M. Magalhães, Antônio Miguel Vieira Monteiro, Christophe Revillion, Daniel Antunes Maciel Villela, Diego Xavier and Christovam Barcellos

Remote Sens. 2020, 12(24), 4074; https://doi.org/10.3390/rs12244074 - 12 Dec 2020

Cited by 3 | Viewed by 2853

Abstract

_{2.5}

severely affects human health. Remotely sensed (RS) data can be used to estimate PM

_{2.5}

concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM

_{2.5}

concentration forecast products derived from models assimilating RS data have [...] Read more.

_{2.5}

severely affects human health. Remotely sensed (RS) data can be used to estimate PM

_{2.5}

concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM

_{2.5}

concentration forecast products derived from models assimilating RS data have not yet been exploited to generate early alerts for respiratory problems in Brazil. We investigated the feasibility of building such an early warning system. For this, PM

_{2.5}

concentrations on a 4-day horizon forecast were provided by the Copernicus Atmosphere Monitoring Service (CAMS) and compared with the number of severe acute respiratory disease (SARD) cases. Confounding effects of the meteorological conditions were considered by selecting the best linear regression models in terms of Akaike Information Criterion (AIC), with meteorological features and their two-way interactions as explanatory variables and PM

_{2.5}

concentrations and SARD cases, taken separately, as response variables. Pearson and Spearman correlation coefficients were then computed between the residuals of the models for PM

_{2.5}

concentration and SARD cases. The results show a clear tendency to positive correlations between PM

_{2.5}

and SARD in all regions of Brazil but the South one, with Spearman’s correlation coefficient reaching 0.52 (p < 0.01). Positive significant correlations were also found in the South region by previously correcting the effects of viral infections on the SARD case dynamics. The possibility of using CAMS global PM

_{2.5}

concentration forecast products to build an early warning system for pollution-related effects on human health in Brazil was therefore established. Further investigations should be performed to determine alert threshold(s) and possibly build combined risk indicators involving other risk factors for human respiratory diseases. This is of particular interest in Brazil, where the COVID-19 pandemic and biomass burning are occurring concomitantly, to help minimize the effects of PM emissions and implement mitigation actions within populations. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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21 pages, 52364 KiB

Open AccessArticle

Assessment of Urban Land Surface Temperature and Vertical City Associated with Dengue Incidences

by Kanchana Nakhapakorn, Warisara Sancharoen, Auemphorn Mutchimwong, Supet Jirakajohnkool, Rattapon Onchang, Chawarat Rotejanaprasert, Kraichat Tantrakarnapa and Richard Paul

Remote Sens. 2020, 12(22), 3802; https://doi.org/10.3390/rs12223802 - 19 Nov 2020

Cited by 8 | Viewed by 4054

Abstract

Rapid population and urban growth in Bangkok increases the need for vertical city development because of the limited territory. This might lead to increasing land surface temperatures (LST), which makes some urban areas significantly warmer and leads to hot spots known as urban heat islands. It is known that climatic factors, such as rainfall and temperature, influence increases in dengue incidences. Thus, this research uses spatial statistical analysis to consider the association of urban LST with dengue incidences. The LST calculation methods are based on LANDSAT imageries in 2009 and 2014. Pearson correlation and Bayesian hierarchical modeling were used for predicting dengue incidences. This study found the highest correlation between the density of high-rise buildings, which had a significant influence on LST, and dengue incidences. Both the number of high-rise buildings and the surface temperature of low-rise buildings increased dengue incidence between 2009 and 2014. Overall, it was found that for every increase of 1000 high-rise buildings, the dengue incidence increased 2.19 on average during that period. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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21 pages, 5809 KiB

Open AccessArticle

Studying Land Cover Changes in a Malaria-Endemic Cambodian District: Considerations and Constraints

by Anaïs Pepey, Marc Souris, Amélie Vantaux, Serge Morand, Dysoley Lek, Ivo Mueller, Benoit Witkowski and Vincent Herbreteau

Remote Sens. 2020, 12(18), 2972; https://doi.org/10.3390/rs12182972 - 12 Sep 2020

Cited by 7 | Viewed by 3368

Abstract

Malaria control is an evolving public health concern, especially in times of resistance to insecticides and to antimalarial drugs, as well as changing environmental conditions that are influencing its epidemiology. Most literature demonstrates an increased risk of malaria transmission in areas of active deforestation, but knowledge about the link between land cover evolution and malaria risk is still limited in some parts of the world. In this study, we discuss different methods used for analysing the interaction between deforestation and malaria, then highlight the constraints that can arise in areas where data is lacking. For instance, there is a gap in knowledge in Cambodia about components of transmission, notably missing detailed vector ecology or epidemiology data, in addition to incomplete prevalence data over time. Still, we illustrate the situation by investigating the evolution of land cover and the progression of deforestation within a malaria-endemic area of Cambodia. To do so, we investigated the area by processing high-resolution satellite imagery from 2018 (1.5 m in panchromatic mode and 6 m in multispectral mode) and produced a land use/land cover map, to complete and homogenise existing data from 1988 and from 1998 to 2008 (land use/land cover from high-resolution satellite imagery). From these classifications, we calculated different landscapes metrics to quantify evolution of deforestation, forest fragmentation and landscape diversity. Over the 30-year period, we observed that deforestation keeps expanding, as diversity and fragmentation indices globally increase. Based on these results and the available literature, we question the mechanisms that could be influencing the relationship between land cover and malaria incidence and suggest further analyses to help elucidate how deforestation can affect malaria dynamics. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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22 pages, 9465 KiB

Open AccessArticle

Remote Sensing and Multi-Criteria Evaluation for Malaria Risk Mapping to Support Indoor Residual Spraying Prioritization in the Central Highlands of Madagascar

by Hobiniaina Anthonio Rakotoarison, Mampionona Rasamimalala, Jean Marius Rakotondramanga, Brune Ramiranirina, Thierry Franchard, Laurent Kapesa, Jocelyn Razafindrakoto, Hélène Guis, Luciano Michaël Tantely, Romain Girod, Solofoarisoa Rakotoniaina, Laurence Baril, Patrice Piola and Fanjasoa Rakotomanana

Remote Sens. 2020, 12(10), 1585; https://doi.org/10.3390/rs12101585 - 16 May 2020

Cited by 9 | Viewed by 4300

Abstract

The National Malaria Control Program (NMCP) in Madagascar classifies Malagasy districts into two malaria situations: districts in the pre-elimination phase and districts in the control phase. Indoor residual spraying (IRS) is identified as the main intervention means to control malaria in the Central Highlands. However, it involves an important logistical mobilization and thus necessitates prioritization of interventions according to the magnitude of malaria risks. Our objectives were to map the malaria transmission risk and to develop a tool to support the Malagasy Ministry of Public Health (MoH) for selective IRS implementation. For the 2014–2016 period, different sources of remotely sensed data were used to update land cover information and substitute in situ climatic data. Spatial modeling was performed based on multi-criteria evaluation (MCE) to assess malaria risk. Models were mainly based on environment and climate. Three annual malaria risk maps were obtained for 2014, 2015, and 2016. Annual parasite incidence data were used to validate the results. In 2016, the validation of the model using a receiver operating characteristic (ROC) curve showed an accuracy of 0.736; 95% CI [0.669–0.803]. A free plugin for QGIS software was made available for NMCP decision makers to prioritize areas for IRS. An annual update of the model provides the basic information for decision making before each IRS campaign. In Madagascar and beyond, the availability of the free plugin for open-source software facilitates the transfer to the MoH and allows further application to other problems and contexts. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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24 pages, 5928 KiB

Open AccessEditor’s ChoiceArticle

Spatial Modeling of Mosquito Vectors for Rift Valley Fever Virus in Northern Senegal: Integrating Satellite-Derived Meteorological Estimates in Population Dynamics Models

by Annelise Tran, Assane Gueye Fall, Biram Biteye, Mamadou Ciss, Geoffrey Gimonneau, Mathieu Castets, Momar Talla Seck and Véronique Chevalier

Remote Sens. 2019, 11(9), 1024; https://doi.org/10.3390/rs11091024 - 30 Apr 2019

Cited by 9 | Viewed by 6899

Abstract

Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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18 pages, 3890 KiB

Open AccessArticle

Developing an Advanced PM_2.5 Exposure Model in Lima, Peru

by Bryan N. Vu, Odón Sánchez, Jianzhao Bi, Qingyang Xiao, Nadia N. Hansel, William Checkley, Gustavo F. Gonzales, Kyle Steenland and Yang Liu

Remote Sens. 2019, 11(6), 641; https://doi.org/10.3390/rs11060641 - 16 Mar 2019

Cited by 34 | Viewed by 6394

Abstract

It is well recognized that exposure to fine particulate matter (PM_2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM_2.5 measurements. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM_2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM_2.5 concentrations at a 1 km² spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R² (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m³). Mean PM_2.5 for ground measurements was 24.7 μg/m³ while mean estimated PM_2.5 was 24.9 μg/m³ in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m³ (Std.Dev. = 5.97 μg/m³), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM_2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM_2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM_2.5 measurements at 1 km² spatial resolution to support future epidemiological studies. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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25 pages, 10576 KiB

Open AccessArticle

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

by Andy Hardy, Georgina Ettritch, Dónall E. Cross, Pete Bunting, Francis Liywalii, Jacob Sakala, Andrew Silumesii, Douglas Singini, Mark Smith, Tom Willis and Chris J. Thomas

Remote Sens. 2019, 11(5), 593; https://doi.org/10.3390/rs11050593 - 12 Mar 2019

Cited by 63 | Viewed by 9930

Abstract

Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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25 pages, 9096 KiB

Open AccessEditor’s ChoiceArticle

On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping

by Christopher Marston and Patrick Giraudoux

Remote Sens. 2019, 11(1), 39; https://doi.org/10.3390/rs11010039 - 27 Dec 2018

Cited by 8 | Viewed by 4694

Abstract

(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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Review

Jump to: Research

82 pages, 4576 KiB

Open AccessFeature PaperReview

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

by Renaud Marti, Zhichao Li, Thibault Catry, Emmanuel Roux, Morgan Mangeas, Pascal Handschumacher, Jean Gaudart, Annelise Tran, Laurent Demagistri, Jean-François Faure, José Joaquín Carvajal, Bruna Drumond, Lei Xu, Vincent Herbreteau, Helen Gurgel, Nadine Dessay and Peng Gong

Remote Sens. 2020, 12(6), 932; https://doi.org/10.3390/rs12060932 - 13 Mar 2020

Cited by 24 | Viewed by 8799

Abstract

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models. Full article

(This article belongs to the Special Issue Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems)

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