Reid, Jack B., and Danielle Wood. “Interactive Model for Assessing Mangrove Health, Ecosystem Services, Policy Consequences, and Satellite Design in Rio de Janeiro Using Earth Observation Data.” 71st International Astronautical Congress (IAC): Cyberspace Edition, International Astronautical Federation, 2020. https://dspace.mit.edu/handle/1721.1/129598.

Abstract

There is an increasing need for tools to translate earth observation data into societally relevant metrics to inform human decision-making. To address this need, we present a multi-disciplinary, interactive modeling framework to advance ecological forecasting and policymaking using earth observation data. The Environment-Vulnerability-Decision-Technology (EVDT) Modeling Framework will integrate four models into one tool that can be adapted to specific applications; the four models address the following: earth science models of the Environment: Human Vulnerability and Societal Impact; Human Behavior and Decision-Making; and Technology Design for earth observation systems including satellites, airborne platforms and in-situ sensors The capabilities provided by this framework will improve the management of earth observation and socioeconomic data in a format usable by non-experts, while harnessing cloud computing, machine learning, economic analysis, complex systems modeling, and model-based systems engineering. This paper presents a prototype that demonstrates the viability of the framework via a case study: the mangrove forests in the Guaratiba area of Rio de Janeiro. These mangroves are vulnerable due to urbanization and rising sea levels. They provide a variety of ecosystem services, including serving as a mechanism for carbon sequestration, supporting subsistence fishing, preventing coastal erosion, and attracting an ecotourism industry. The case study of mangrove and community health in Rio de Janeiro demonstrates all four model components. The Environment Model builds upon work by biospheric scientists Fatoyinbo and Lagomasino to use earth observation data, cloud computing, and machine learning to track mangrove extent, health, and vulnerability over time for a 600 km2 area, as well as work by the ESPAÇO research group at the Federal University of Rio de Janeiro on the local mangrove ecosystem. To build the Human Vulnerability and Societal Impact Model, we are collaborating with ecosystem services economist Suhyun Jung to explain how policies impact mangrove health and how mangroves impact socioeconomic wellbeing. To create the Human Decision Making Model, we have partnered with the Pereira Passos Institute (the data science office of the Rio de Janeiro municipal government) to understand the policy history and socioeconomic factors. The Technology Model accounts for the types of data collection used by policy makers since 1975. Through such collaborations, we are able to build an integrated, interactive decision support tool that policymakers can use to assess mangrove health, ecosystem services value, and policy consequences. The model helps answer such questions as: (a) What is the state of the mangroves over time? (b) How are human communities impacting the mangroves? (c) what is the value of the mangrove ecosystem services to human communities? and (d) what policies can improve human and mangrove outcomes? This case study is demonstrative of the viability of a similar approach for ecosystems around the world.