The Use of Multispectral (MSS) and Synthetic Aperture Radar (SAR) Microwave Remote Sensing Data to Study Environment Variables, Land Use / Land Cover Changes, and Recurrent Weather Condition for Forecast Malaria: A Systematic Review

Abstract

Malaria is endemic problem in the low and middle income countries, especially, sub-Saharan Africa, is caused by Plasmodium falciparum contributed on the major parts, and Plasmodium vivax parasites in the minor parts claim for millions of morbidity and mortality on the global level. Mainly due to the climate change, monsoon failure, declining agriculture crop production, population movements on poverty, mushroom growth of unplanned urbanization, landscape and land cover changes. Multispectral (MSS) satellite data and Synthetic Aperture Radar (SAR) imagery has been used for the replacement of conventional survey methods for the assessment of the problems. Remote sensing of environmental information has been used to study the variations of climate conditions; land use/ land cover changes and its impact on natural environmental transitions, assess breeding potentiality, and forecast malaria for the past 4 decades. It provides the reliable, picturesque, repetitive, precise, speed, and low cost comparatively. Remote sensing technology has been applied as alternative tool, a scientific method to develop spatial models for forecast malaria for lager areas; regional, national, and global scale. Malaria is prolonged public health challenging problem in Africa continent, tropical countries, and sub-tropical regions for several decades, it claims 2 million death tolls every year, especially, in the sub-Saharan Africa regions excessively tremendous problem, despite, all kinds control measures. The perceptions of spatial model for malaria prediction/ forecast malaria epidemics have been attracted by many researchers for past 4 decades. Therefore, present study is aimed to review relevant studies of the use of multispectral satellite data, and synthetic aperture radar imagery to analyze recurrent weather environment (temperature, precipitation, relative humidity, and saturation deficiency), land surface temperature (LST), sea surface temperature (SST), vector breeding potentiality, deforestation; land use/ land cover changes for forecast malaria.