SATELLITE MODELS FOR GLOBAL ENVIRONMENTAL CHANGE IN THE NASA HEALTH AND AIR QUALITY PROGRAMS

Abstract:

Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. Health and Air Quality providers and researchers are effective by the global environmental changes that are occurring and they need environmental data to study and understand the geographic, environmental, and meteorological differences in disease.

This presentation maintains a diverse constellation of Earth observing research satellites and sponsors research in developing satellite data applications across a wide spectrum of areas including environmental health; infectious disease; air quality standards, policies, and regulations; and the impact of climate change on health and air quality. Successfully providing predictions with the accuracy and specificity required by decision makers will require advancements over current capabilities in a number of interrelated areas. These areas include observations, modeling systems, forecast development, application integration, and the research to operations transition process. This presentation will highlight many projects on which NASA satellites have been a primary partner with local, state, Federal, and international operational agencies over the past twelve years in these areas. Domestic and International officials have increasingly recognized links between environment and health.

Health providers and researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. The presentation is directly related to Earth Observing systems and Global Health Surveillance and will present research results of the remote sensing environmental observations of earth and health applications, which can contribute to the health research.

As part of NASA approach and methodology they have used Earth Observation Systems and Applications for Health Models to provide a method for bridging gaps of environmental, spatial, and temporal data for tracking disease.