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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 February 2023) | Viewed by 17451

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Special Issue Editors

Dr. Andy Hardy

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Guest Editor

Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK
Interests: remote sensing; GIS; hydrological/environmental modeling; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Dr. Michelle Stanton

E-Mail Website
Guest Editor

Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, UK
Interests: spatial epidemiology; GIS; remote sensing; vector-borne diseases

Special Issue Information

Dear Colleagues,

Infectious diseases continue to be a burden across the tropics posing a significant risk to the health and wellbeing of billions of people. Diseases such as malaria remain the leading causes of death in Africa, whereas others have a significant effect on the health of people across the planet, such as the Western Nile virus, dengue fever, schistosomiasis, and Zika virus, to name but a few.

Remote sensing has the potential to provide game-changing resources to those that need it, including but not limited to early warning systems, vector habitat mapping, and human population and infrastructure mapping, providing vital public health information for control program managers and implementers at regional, national, and even continental scales.

This Special Issue will highlight new and exciting remote sensing methods for tackling infectious diseases, including UAV technology, very high resolution (<1m) land cover mapping, geospatial modeling of disease, and medium and broad-scale EO satellite data for mapping disease risk in the tropics.

Review papers are welcome, as well as papers presenting novel research activities.

Dr. Andy Hardy
Dr. Michelle Stanton
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

Disease risk mapping
Spatial modeling
Unmanned aerial vehicles
Habitat mapping
Public health
Entomology
Epidemiology

Published Papers (4 papers)

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Research

19 pages, 29140 KiB

Open AccessArticle

Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance

by Fedra Trujillano, Gabriel Jimenez Garay, Hugo Alatrista-Salas, Isabel Byrne, Miguel Nunez-del-Prado, Kallista Chan, Edgar Manrique, Emilia Johnson, Nombre Apollinaire, Pierre Kouame Kouakou, Welbeck A. Oumbouke, Alfred B. Tiono, Moussa W. Guelbeogo, Jo Lines, Gabriel Carrasco-Escobar and Kimberly Fornace

Remote Sens. 2023, 15(11), 2775; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112775 - 26 May 2023

Cited by 3 | Viewed by 2032

Abstract

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs. Full article

(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)

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15 pages, 10460 KiB

Open AccessArticle

Satellite Imagery-Based Identification of High-Risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood

by Juan Qiu, Dongfeng Han, Rendong Li, Ying Xiao, Hong Zhu, Jing Xia, Jie Jiang, Yifei Han, Qihui Shao, Yi Yan and Xiaodong Li

Remote Sens. 2022, 14(15), 3707; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153707 - 02 Aug 2022

Viewed by 1787

Abstract

Snail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all inundation areas carry snails and may have overestimated snail spread areas. Furthermore, these studies only used a single environmental factor to estimate the snail survival risk probability, failing to analyze multiple variables, to accurately distinguish the snail survival risk in the snail spread areas. This paper proposes a systematic framework for early monitoring of snail diffusion to accurately map snail spread areas from remote sensing imagery and enhance snail survival risk probability estimation based on the snail spread map. In particular, the flooded areas are extracted using the Sentinel-1 Dual-Polarized Water Index based on synthetic aperture radar images to map all-weather flooding areas. These flood maps are used to extract snail spread areas, based on the assumption that only inundation areas that spatially interacted with (i.e., are close to) the previous snail distribution regions before flooding are identified as snail spread areas, in order to reduce the misclassification in snail spread area identification. A multiple logistic regression model is built to analyze how various types of snail-related environmental factors, including the normalized difference vegetation index (NDVI), wetness, river and channel density, and landscape fractal dimension impact snail survival, and estimate its risk probabilities in snail spread area. An experiment was conducted in Jianghan Plain, China, where snails are predominantly linearly distributed along the tributaries and water channels of the middle and lower reaches of the Yangtze River. The proposed method could accurately map floods under clouds, and a total area of 231.5 km² was identified as the snail spread area. The snail survival risk probabilities were thus estimated. The proposed method showed a more refined snail spread area and a more reliable degree of snail survival risk compared with those of previous studies. Thus, it is an efficient way to accurately map all-weather snail spread and survival risk probabilities, which is helpful for schistosomiasis interruption. Full article

(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)

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16 pages, 2699 KiB

Open AccessArticle

Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

by Zac Yung-Chun Liu, Andrew J. Chamberlin, Krti Tallam, Isabel J. Jones, Lance L. Lamore, John Bauer, Mariano Bresciani, Caitlin M. Wolfe, Renato Casagrandi, Lorenzo Mari, Marino Gatto, Abdou Ka Diongue, Lamine Toure, Jason R. Rohr, Gilles Riveau, Nicolas Jouanard, Chelsea L. Wood, Susanne H. Sokolow, Lisa Mandle, Gretchen Daily, Eric F. Lambin and Giulio A. De Leo Show full author list Hide full author list

Remote Sens. 2022, 14(6), 1345; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061345 - 10 Mar 2022

Cited by 18 | Viewed by 7794

Abstract

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination. Full article

(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)

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18 pages, 6290 KiB

Open AccessArticle

Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

by Andy Hardy, Gregory Oakes, Juma Hassan and Yussuf Yussuf

Remote Sens. 2022, 14(2), 317; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020317 - 11 Jan 2022

Cited by 8 | Viewed by 4309

Abstract

Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes. Full article

(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)

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