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

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.

[1]  Joanne V. Hall,et al.  Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion , 2019, Remote Sensing of Environment.

[2]  Md Shafiur Rahman,et al.  Progress towards universal health coverage in Myanmar: a national and subnational assessment. , 2018, The Lancet. Global health.

[3]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[4]  Assaf Anyamba,et al.  The relationship between mosquito abundance and rice field density in the Republic of Korea , 2010, International journal of health geographics.

[5]  J. Kaewkungwal,et al.  Asymptomatic and sub-microscopic malaria infection in Kayah State, eastern Myanmar , 2017, Malaria Journal.

[6]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[7]  R. Levins,et al.  Impact of deforestation and agricultural development on anopheline ecology and malaria epidemiology. , 2007, The American journal of tropical medicine and hygiene.

[8]  Pierre Soille,et al.  Automated global delineation of human settlements from 40 years of Landsat satellite data archives , 2019, Big Earth Data.

[9]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[11]  G. S. Bhunia,et al.  Localization of kala-azar in the endemic region of Bihar, India based on land use/land cover assessment at different scales. , 2012, Geospatial health.

[12]  K. Thimasarn,et al.  Malaria in tree crop plantations in south-eastern and western provinces of Thailand. , 1999, The Southeast Asian journal of tropical medicine and public health.

[13]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[14]  C. Woodcock,et al.  Theory and methods for accuracy assessment of thematic maps using fuzzy sets , 1994 .

[15]  Shui-sen Zhou,et al.  Receptivity to malaria in the China–Myanmar border in Yingjiang County, Yunnan Province, China , 2017, Malaria Journal.

[16]  W. Takken,et al.  Identifying the most productive breeding sites for malaria mosquitoes in The Gambia , 2009, Malaria Journal.

[17]  Roger F. Auch,et al.  Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database , 2020, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[18]  C. Milesi,et al.  Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? , 2012 .

[19]  David J. Ganz,et al.  Primitives as building blocks for constructing land cover maps , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[20]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[21]  A. Ullah Rohingya Refugees to Bangladesh: Historical Exclusions and Contemporary Marginalization , 2011 .

[22]  Julie A. Silva,et al.  Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys , 2020, GeoHealth.

[23]  C. Linard,et al.  Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts , 2010, International journal of health geographics.

[24]  Chengquan Huang,et al.  Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery , 2017, Remote. Sens..

[25]  S. Tong,et al.  Socio-demographic, ecological factors and dengue infection trends in Australia , 2017, PloS one.

[26]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[27]  Durrell D. Kapan,et al.  Spatially disaggregated disease transmission risk: land cover, land use and risk of dengue transmission on the island of Oahu , 2011, Tropical medicine & international health : TM & IH.

[28]  D. Campbell-Lendrum,et al.  Predicting Geographic Variation in Cutaneous Leishmaniasis, Colombia , 2004, Emerging infectious diseases.

[29]  Timothy F. Leslie,et al.  Spatial Associations Between Land Use and Infectious Disease: Zika Virus in Colombia , 2020, International journal of environmental research and public health.

[30]  N. Anstey,et al.  Malaria incidence in Myanmar 2005–2014: steady but fragile progress towards elimination , 2016, Malaria Journal.

[31]  Sucharita Gopal,et al.  Fuzzy set theory and thematic maps: accuracy assessment and area estimation , 2000, Int. J. Geogr. Inf. Sci..