Global Earth Observation Systems of Systems (GEOSS) are bringing vital societal benefits to people around the globe. In this research article, we engage undergraduate students in the exciting area of space exploration to improve the health of millions of people globally. The goal of the proposed research is to place students in a learning environment where they will develop their problem solving skills in the context of a world crisis (e.g., malaria). Malaria remains one of the greatest threats to public health, particularly in developing countries. The World Health Organization has estimated that over one million die of Malaria each year, with more than 80% of these found in Sub-Saharan Africa. The mosquitoes transmit malaria. They breed in the areas of shallow surface water that are suitable to the mosquito and parasite development. These environmental factors can be detected with satellite imagery, which provide high spatial and temporal coverage of the earth's surface. We investigate on moisture, thermal and vegetation stress indicators developed from NOAA operational environmental satellite data. Using these indicators and collected epidemiological data, it is possible to produce a forecast system that can predict the risk of malaria for a particular geographical area with up to four months lead time. This valuable lead time information provides an opportunity for decision makers to deploy the necessary preventive measures (spraying, treated net distribution, storing medications and etc) in threatened areas with maximum effectiveness. The main objective of the proposed research is to study the effect of ecology on human health and application of NOAA satellite data for early detection of malaria.
Malaria and dengue fever are the two most common mosquito-transmitted diseases, leading to millions of serious illnesses and deaths each year. Because the mosquito vectors are sensitive to environmental conditions such as temperature, precipitation, and humidity, it is possible to map areas currently or imminently at high risk for disease outbreaks using satellite remote sensing. In this paper we propose the development of an operational geospatial system for malaria and dengue fever early warning; this can be done by bringing together geographic information system (GIS) tools, artificial neural networks (ANN) for efficient pattern recognition, the best available ground-based epidemiological and vector ecology data, and current satellite remote sensing capabilities.
We use Vegetation Health Indices (VHI) derived from visible and infrared radiances measured by satellite-mounted Advanced Very High Resolution Radiometers (AVHRR) and available weekly at 4-km resolution as one predictor of malaria and dengue fever risk in Bangladesh. As a study area, we focus on Bangladesh where malaria and dengue fever are serious public health threats. The technology developed will, however, be largely portable to other countries in the world and applicable to other disease threats. A malaria and dengue fever early warning system will be a boon to international public health, enabling resources to be focused where they will do the most good for stopping pandemics, and will be an invaluable decision support tool for national security assessment and potential troop deployment in regions susceptible to disease outbreaks.
The proposed approach in this article applies an efficient and novel statistical technique to accurately describe radiometric data measured by Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration’s (NOAA) Polar Orbiting Environmental Satellites (POES). The corrected data set will then be applied to improve the strength of NOAA Global Vegetation Index (GVI) data set for the 1982- 2003 period produced from AVHRR. The GVI is used extensively for studying and monitoring land surface, atmosphere and recently for analyzing climate and environmental changes. The POES AVHRR data, though useful, cannot be directly used in climate change studies because of the orbital drift in the NOAA satellites over the lifetime of the satellites. This orbital drift causes inaccuracies in AVHRR data sets for some satellites. The main goal is achieved by implementing a statistical technique that uses an Empirical Distribution Function (EDF) to produce error free long-term time-series for GVI data sets. This technique permits the representation of any global ecosystem from desert to tropical forest and to correct deviations in satellite data that are due to orbital drifts and AVHRR sensor degradations. The primary focus of this research is to generate error free satellite data by applying the EDF technique for climatological research.
Malaria transmission in many part of the world specifically in Bangladesh and southern African countries is unstable
and epidemic. An estimate of over a million cases is reported annually. Malaria is heterogeneous, potentially due to
variations in ecological settings, socio-economic status, land cover, and agricultural practices. Malaria control only
relies on treatment and supply of bed networks. Drug resistance to these diseases is widespread. Vector control is
minimal. Malaria control in those countries faces many formidable challenges such as inadequate accessibility to
effective treatment, lack of trained manpower, inaccessibility of endemic areas, poverty, lack of education, poor
health infrastructure and low health budgets. Health facilities for malaria management are limited, surveillance is
inadequate, and vector control is insufficient. Control can only be successful if the right methods are used at the
right time in the right place. This paper aims to improve malaria control by developing malaria risk maps and risk
models using satellite remote sensing data by identifying, assessing, and mapping determinants of malaria associated
with environmental, socio-economic, malaria control, and agricultural factors.
This paper apply an statistical technique to correct radiometric data measured by Advanced Very High Resolution
Radiometers(AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting
Environmental Satellites(POES). This paper study Normalized Difference Vegetation Index (NDVI) stability in the
NOAA/NESDIS Global Vegetation Index (GVI) data for the period 1982-2003. AVHRR weekly data for the five NOAA
afternoon satellites NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 are used for the China dataset, for it
includes a wide variety or different ecosystems represented globally. GVI has found wide use for studying and
monitoring land surface, atmosphere, and recently for analyzing climate and environmental changes. Unfortunately the
POES AVHRR data, though informative, can not be directly used in climate change studies because of the orbital drift in
the NOAA satellites over these satellites' life time. This orbital drift introduces errors in AVHRR data sets for some
satellites. To correct this error of satellite data, this paper implements Empirical Distribution Function (EDF) which is a
statistical technique to generate error free long-term time-series for GVI data sets. We can use the same methodology
globally to create vegetation index to improve the climatology.