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Integrating geostatistics and remote sensing for mapping the spatial distribution of cattle hoofprints in relation to malaria vector control

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Globally, malaria is still a persistent health problem affecting more than 200 million people. With about 90% of malaria cases occurring in Sub-Saharan Africa, it becomes imperative to understand the environmental factors contributing to malaria vector proliferation. The cattle hoofprints are known to be some of the productive breeding sites for Anopheles (An.) arabiensis and An. fenestus in Southern and East African countries. Therefore, this study aimed at testing the potential of integrating field data and Sentinel-2 satellite imagery for mapping cattle hoofprint distribution in the Vhembe District, South Africa. The purpose was to improve the predictability of mosquito breeding sites in the study area by using field point dataset and Sentinel-2 data. Due to the difficulty of sampling all locations in the study area, the spatial interpolation was employed to create continuous surfaces of cattle hoofprints, using limited sampled point observations. The sampled point observations were then correlated with Sentinel-derived variables for predicting cattle hoofprints at unsampled locations. The ordinary Kriging (OK), co-Kriging (CK) and step-wise multiple linear regression (SMLR) were used due to their ability to incorporate both field point data and ancillary datasets. The CK was the best performing interpolation method, with R 2 = 0.69 for validation dataset (n = 33), compared to OK (R 2 = 0.57) and SMLR (R 2 = 0.25). The resulting co-Kriging semivariogram shows that the combination of field data and remote sensing dataset improves the prediction accuracy of cattle hoofprint distribution. Findings from this study demonstrated that the interpolation error for estimating cattle hoofprints/100 m2 can be minimized greatly by using CK (RMSE = 0.2; MAD = 0.04) than with both OK (RMSE = 2.39; MAD = 2.11) and SMLR (RMSE = 5.20; MAD = 4.55) methods. Furthermore, the results from this study indicate that there is a high number of cattle hoofprints in malaria-prone areas at the study site than in the malaria-free areas. Studies such as this provide the platform for developing an operational platform for long-term monitoring of areas susceptible to malaria, risks, and control management.
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Document Type: Research Article

Affiliations: 1: Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Hatfield, South Africa 2: New Partnership for Africa’s Development (NEPAD) Agency, Midrand, South Africa 3: South African National Space Agency (SANSA), Earth Observation Directorate, Pretoria, South Africa

Publication date: August 3, 2019

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