In light of the profound global health impact of pandemics, the reliance on data-driven insights to understand disease outbreaks has never been more crucial. Malaria is a disease transmitted by mosquitoes that is endemic to specific regions and causes severe illness and death to millions each year. The sensitivity of mosquito vectors to environmental factors like temperature, precipitation, and humidity enables the mapping of areas at high risk of disease outbreaks through satellite remote sensing. This study proposes the development of a practical geospatial system that can provide early warning for malaria. It combines Geographic Information System (GIS) tools, Artificial Neural Networks (ANN) for efficient pattern recognition, robust on-ground environmental data (including epidemiological and vector ecology data), and the capabilities of satellite remote sensing. The study employs Vegetation Health Indices (VHI) derived from satellite-mounted Advanced Very High-Resolution Radiometers (AVHRR) on a weekly basis with a 4-km resolution to predict malaria risk in Bangladesh. While the focus is on Bangladesh due to its significant malaria threat, the technology developed can be adapted for use in other countries and against different disease threats. Implementing an early malaria warning system would be a significant asset to global public health efforts. It would enable targeted resource allocation for pandemic containment and serve as a vital decision-making tool for national security assessments and potential troop deployments in disease-prone regions.
Considering the public health impact of a global pandemic, the reliance on data to understand disease outbreak is important now more than ever. Malaria is the most common mosquito-transmitted disease endemic to certain regions, leading to millions of serious illnesses and deaths each year. Because 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 the authors propose the development of an operational geospatial system for malaria 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 groundbased environmental data such as epidemiological and vector ecology data, and current satellite remote sensing capabilities. The authors 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 risk in Bangladesh. As a study area, we focus on Bangladesh where malaria is a serious public health threat. The technology developed will, however, be largely portable to other countries in the world and applicable to other disease threats. A malaria 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.
Dengue is the 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 Dengue 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 dengue fever risk in Bangladesh. As a study area, we focus on Bangladesh where 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 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.
With the growing use of the Vegetation Index in many remote sensing applications, it was imperative to examine the Brightness Temperature (BT) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to decrease the long-term inaccuracy of the BT data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data is a consequence of orbit degradation, and from the circuit drifts over the life of a satellite. Degradation of BT over time and shifts of BT between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for six particular years, four of which were consecutive, are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for paired years for the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 were assumed to be standard because the crossing time of the satellite over the equator maximized the value of the coefficients. These years were considered the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the EDF method and compared with the standard years. These normalized values were then utilized to estimate new BT time series that show significant improvement of BT data for the affected years so that the dataset is useful for environment monitoring.
The main objective of this report is to examine the Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to decrease the long-term inaccuracy of the NDVI data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data is a consequence of orbit degradation, and from the circuit drifts over the life of a satellite. Degradation of NDVI over time and shifts of NDVI between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for six particular years, four of which were consecutive, are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for paired years for the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 were assumed to be standard because the crossing time of the satellite over the equator (between 13:30 and 15:00 hours) maximized the value of the coefficients. These years were considered the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the EDF method and compared with the standard years. These normalized values were then utilized to estimate new NDVI time series that show significant improvement of NDVI data for the affected years so that the dataset is useful in climate studies.
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.
KEYWORDS: Satellites, Remote sensing, Environmental sensing, Geographic information systems, Vegetation, Agriculture, Data modeling, Surveillance, Data corrections, Control systems
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.
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. It allows one to represent
any global ecosystem from desert to tropical forest and to correct deviations in satellite data due to orbit degradation. The
corrected datasets can be used as proxy to study climate change, epidemic analysis, and drought prediction etc.
The proposed paper apply novel statistical approach 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 investigates Normalized Difference Vegetation Index (NDVI)
stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003. AVHRR weekly data for the five
NOAA afternoon satellites for the China dataset is studied, for it includes a wide variety of different ecosystems
represented globally. It was found that data for the years 1988, 1992, 1993, 1994, 1995 and 2000 are not stable enough
compared to other years because of satellite orbit drift, and AVHRR sensor degradation. It is assumed that data from
NOAA-7 (1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990), NOAA-14 (1996, 1997), and NOAA-16 (2001,
2002) to be standard because these satellites equator crossing time fall within 1330 and 1500, and hence maximizing the
value of coefficients. The crux of the proposed correction procedure consists of dividing standard years data sets into two
subsets. The subset 1 (standard data correction sets) is used for correcting unstable years and then corrected data for this
years compared with the standard data in the subset 2 (standard data validation sets). In this paper, we apply empirical
distribution function (EDF) to correct this deficiency of data for the affected years. It allows one to represent any global
ecosystem from desert to tropical forest and to correct deviations in satellite data due to satellite technical problems. The
corrected data set can be used for climatological research.
This paper investigates Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global
Vegetation Index (GVI) data during 1982-2003. Advanced Very High Resolution Radiometer (AVHRR) weekly data for
the five NOAA afternoon satellites for the China dataset is studied, for it includes a wide variety of different ecosystems
represented globally. It was found that data for the years 1988, 1992, 1993, 1994, 1995 and 2000 are not stable enough
compared to other years because of satellite orbit drift, and AVHRR sensor degradation. It is assumed that data from
NOAA-7 (1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990),
NOAA-14 (1996, 1997), and NOAA-16 (2001,
2002) to be standard because these satellite's equator crossing time fall within 1330 and 1500, and hence maximizing the
value of coefficients. The crux of the proposed correction procedure consists of dividing standard year's data sets into
two subsets. The subset 1 (standard data correction sets) is used for correcting unstable years and then corrected data for
this years compared with the standard data in the subset 2 (standard data validation sets). In this paper, we apply
empirical distribution function (EDF) to correct this deficiency of data for the affected years. We normalize or correct
NDVI data by the method of EDF compared with the standard. Using these normalized values, we estimate new NDVI
time series which provides NDVI data for these years that match in subset 2 that is used for data validation.
Empirical distribution functions were applied for removing long-term errors from BT data derived from AVHRR sensor
on NOAA environmental satellites. This paper investigates BT stability in the NOAA/NESDIS Global Vegetation Index
(GVI) data set during 1982-2003. This period includes five NOAA satellites. Degradation of BT over time for each
satellite was estimated for geographical location in China. The method of matching empirical distribution function
(EDF) improves the time relative stability of BT data for all satellites, especially NOAA-9, -11 and -14.
Empirical distribution functions were applied for improving stability of NDVI data for NOAA environmental satellites.
This paper investigates NDVI stability in the NOAA/NESDIS Global Vegetation Index (GVI) data set during 1982-
2003, in the period, which includes five NOAA series satellites. Degradation of NDVI over time and NDVI's shifts
between the satellites were estimated for geographical location in China. The method of matching empirical distribution
functions improves the time relative stability of NDVI data for all satellites, especially NOAA-9, -11 and -14.
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