Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS,Landsat 7 ETM+, SRTM and multiple Random Forests |
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| Institution: | 1. Utrecht University, Department of Physical Geography, Heidelberglaan 2, PO Box 80115, 3508 TC Utrecht, The Netherlands;2. Utrecht University, Faculty of Veterinary Medicine, Yalelaan 7, 3584 CL Utrecht, The Netherlands;3. Stavropol Plague Control Research Institute, Sovetskaya 13-15, Stavropol 355035, Russian Federation;4. Anti-Plague Institute, M. Aikimbayev''s Kazakh Science Centre for Quarantine and Zoonotic Diseases, 14 Kapalskaya Street, Almaty 050074, Kazakhstan;5. RMIT University, School of Mathematical and Geospatial Sciences, Melbourne, Victoria 3000, Australia;6. University of Liverpool, Institute of Integrative Biology, Crown Street, Liverpool, UK;7. University of Antwerp, Department of Biology, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium |
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| Abstract: | Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape.The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation.In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit.Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively.In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague. |
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| Keywords: | Object-based image analysis Stratification Landscape epidemiology Vector-borne disease Zoonosis Yersinia pestis Great gerbil Desert environment |
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