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

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.

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

“…Recent advances in satellite data availability and temporal, radiometric, and spatial resolution provided by the Sentinels, has made it possible to significantly improve the identification of small fragments in tropical forests [26,27]. Furthermore, methodological advances, such as time series analysis of optical satellite imagery or synergistic approaches that combine the advantages of optical and synthetic aperture radar (SAR) imagery, have shown to improve vegetation classification in tropical areas when detecting detailed vegetation characteristics [28][29][30][31][32]. Moreover, advances in machine learning algorithms, such as random forests, have improved models regarding forest distribution, types, and attributes [33,34].…”

Mapping small mammal optimal habitats using satellite-derived proxy variables and species distribution models

“…Relative abundance scores of small mammal presence (the number of intervals where presence indicators were observed) were produced for each species for each transect. In Sary Mogul, field surveys comprised 37 transects as described in [ 25 ]. Transect locations were separated by an average of 1.2 km to avoid spatial autocorrelation [ 12 ].…”

Assessing a century of maize and soybean polyculture for silage production

“…The partial dependence plots display, on average, the additive effect of changes of the predictor variable on the predicted variable when all other predictors are held constant and at their average. Partial dependence plots have been used to show the effect of climate variables on crop yields (Hoffman et al., 2020), rainfall and runoff modeling (Shortridge et al., 2015), and for mapping land cover characteristics of pathogen hosts (Marston & Giraudoux, 2018) among other applications. Two‐way partial dependence plots help with visualizing interactions (Saha et al., 2021).…”