A Disease Control-Oriented Land Cover Land Use Map for Myanmar

Dong Chen, Varada Shevade, Allison Baer, Jiaying He, Amanda Hoffman-Hall, Qing Ying, Yao Li and Tatiana V. Loboda
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Dong Chen: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Varada Shevade: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Allison Baer: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Jiaying He: Department of Earth System Science, Tsinghua University, Beijing 100084, China
Amanda Hoffman-Hall: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Qing Ying: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Yao Li: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Tatiana V. Loboda: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

Data, 2021, vol. 6, issue 6, 1-15

Abstract: Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.

Keywords: remote sensing; public health; infectious disease; malaria; Myanmar; land cover/land use map; landsat (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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