Next Article in Journal
Analysis of Precise Orbit Predictions for a HY-2A Satellite with Three Atmospheric Density Models Based on Dynamic Method
Next Article in Special Issue
Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats
Previous Article in Journal
Editorial for Special Issue: “Remote Sensing of Forest Cover Change”
Article

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

1
Department of Geography, Edge Hill University, St. Helens Road, Ormskirk, Lancashire L39 4QP, UK
2
Centre for Ecology and Hydrology, Library Avenue, Lancaster Environment Centre, Bailrigg, Lancaster LA1 4AP, UK
3
Department of Chrono-Environment, University of Bourgogne Franche-Comte/CNRS, La Bouloie, 25030 Besançon CEDEX, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 39; https://doi.org/10.3390/rs11010039
Received: 29 November 2018 / Revised: 18 December 2018 / Accepted: 21 December 2018 / Published: 27 December 2018
(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. View Full-Text
Keywords: Echinococcus multilocularis; Ellobius tancrei; land cover; Microtus gregalis; random forests; SAR; Sentinel; spatial epidemiology; time-series Echinococcus multilocularis; Ellobius tancrei; land cover; Microtus gregalis; random forests; SAR; Sentinel; spatial epidemiology; time-series
Show Figures

Graphical abstract

MDPI and ACS Style

Marston, C.; Giraudoux, P. On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping. Remote Sens. 2019, 11, 39. https://doi.org/10.3390/rs11010039

AMA Style

Marston C, Giraudoux P. On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping. Remote Sensing. 2019; 11(1):39. https://doi.org/10.3390/rs11010039

Chicago/Turabian Style

Marston, Christopher, and Patrick Giraudoux. 2019. "On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping" Remote Sensing 11, no. 1: 39. https://doi.org/10.3390/rs11010039

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop