Title of article :
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
Author/Authors :
Wilschut، نويسنده , , L.I. and Addink، نويسنده , , E.A. and Heesterbeek، نويسنده , , J.A.P. and Dubyanskiy، نويسنده , , V.M. and Davis، نويسنده , , S.A. and Laudisoit، نويسنده , , A. and M.Begon and Burdelov، نويسنده , , L.A. and Atshabar، نويسنده , , B.B. and de Jong، نويسنده , , S.M.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2013
Pages :
14
From page :
81
To page :
94
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. udy 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. 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. s were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producerʹs and userʹs 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. s 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.
Keywords :
Object-Based Image Analysis , Landscape epidemiology , Stratification , Yersinia pestis , Great gerbil , zoonosis , Vector-borne disease , Desert environment
Journal title :
International Journal of Applied Earth Observation and Geoinformation
Serial Year :
2013
Journal title :
International Journal of Applied Earth Observation and Geoinformation
Record number :
2379317
Link To Document :
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