Decision Support Model & Visualization for Assessing Environmental Phenomena, Ecosystem Services, Policy Consequences, and Satellite Design


Space Enabled

USGS & Space Enabled

Reid, Jack B., Danielle Wood. "Decision Support Model and Visualization for Assessing Environmental Phenomena, Ecosystem Services, Policy Consequences, and Satellite Design Using Earth Observation Data." AIAA 2020-4181 Session: Model-Based Engineering: Technologies and Methodologies II Published Online: 2 Nov 2020.


With the increasing availability of Earth Observation (EO) data has come a commensurate rise in EO applications. To address the dual needs of processing data for applications and designing missions with applications in mind, we present a multi-disciplinary, integrated modeling framework to advance environmental management, policymaking, and observation platform design. This core modeling framework is called Environment-Vulnerability-Decision-Technology (EVDT). EVDT is not the model itself, it is the framework that guides the creation of an integrated model customized to each application with a specific set of stakeholders in mind and designed using Systems Architecture. The EVDT framework can be customized to build integrated models specific to a certain application. Individual models in the framework include certain core models (Environment, Vulnerability, Decision, Technology) and optional models such as Public Health which are added when needed for a specific application. The Environment Model uses earth science methods to estimate the state of environmental phenomena; The Vulnerability Model captures societal impact of environmental changes including ecosystem services; the Decision Model captures human behavior and policy consequences; and the Technology Model provides tools to design earth observation systems or select among earth observation technologies such as satellites, airborne sensors, and in-situ sensors. The intent in developing this framework and its applications is to lead to the development of a standard to facilitate the re-use of models and the design of future remote observation systems. The presented prototype specifically focuses on two case studies. The first case study considers the dynamics of human and environmental behavior related to the mangrove forests in the Guaratiba area of Rio de Janeiro. The second case study considers the relevancy of this framework in approaching coronavirus-related public health decisions in Rio de Janeiro, Santiago, and elsewhere.

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