The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework considers the interactions between the environment, societal impact, human decision-making, and technology design to support decision making. EVDT has been expanded to include a public health model in the Vida Decision Support System, which will help local leaders understand the relationships between societal factors relating to COVID-19. Key to the development of Vida are collaborative design and mutual learning with international and interdisciplinary teams. Collaborations with researchers and government officials (including public health, economics, environmental, and demographic data collection officials) in Angola, Brazil, Chile, Indonesia, Mexico and the United States provide in-depth understanding of local contexts. Lessons learned from these collaborations include the value of dialogues with teams from the same region but different topic areas (such as a space agency compared to a public health agency), allowing for time to learn the best way to combine diverse data types and find the tools each collaborator prefers, and encouraging the use of the preferred language of collaborators. During Vida’s development, each collaborator has worked to create their own version of Vida using local data sources, the US team has provided prototype analyses and models, and collaborators have shared individual insights among the whole network. These partnerships have yielded promising initial results to support decision making, with prototype tools incorporating local data on COVID cases, the environment, and socio-economic factors from Rio De Janeiro and Chile being evaluated. This collaborative design process will develop insights for decision-making, create a network of international collaborators that can exchange technical methods beyond the pandemic, and emphasize the principles of inclusive innovation and decoloniality by submitting to the preferences of local leaders in each country.

Land-use decisions, particularly in an agricultural setting, lie at the nexus of the colliding challenges of climate change and food insecurity. Understanding and guiding these decisions at the regional scale is a key strategy in the development of natural climate solutions and sustainable food production systems. These issues come together in a particularly high-stakes context in the Massachusetts cranberry industry, which occupies a position of significant economic and sociocultural importance in the region, but faces a number of challenges in the form of heightened competition, unstable prices, an aging farmer population, and changing ecological conditions. Many farmers are looking either for ways to become profitable, or to exit the industry in a financially sustainable way. One option is to sell their land to developers; another option, which is exciting to scientists and environmental advocates, is undergoing an active habitat rehabilitation to restore the beneficial ecosystem services of a functioning wetland environment. Integrating satellite data and in-situ sensor data collected over the past decade, we aim to conduct a systems analysis that unites the viewpoints of cranberry industry stakeholders and clarifies the trade-offs between environmental, economic, and social factors in the region. We propose to address this aim via three core research efforts: a contextual analysis of the industry; a valuation and mapping of key ecological, economic, and social factors; and an integrated modeling approach that models interactions and trade-offs between these factors. In particular, this presentation will focus on the progress we have made valuing and mapping key environmental and economic factors using publicly available satellite imagery and census data. This work demonstrates how these factors align with existing features of the natural and built environment, supports conservation organizations and municipalities in their restoration and conservation advocacy, and provides a foundation for future scenario mapping that will analyze trade-offs in different land use cases.

The COVID-19 pandemic has had a diverse range of both direct and indirect impacts on health (both physical and mental), the economy, and the environment. The relevant data sources used to inform pandemic-related decisions have been similarly diverse, though decision-makers have primarily relied upon data sets from non-satellite sources such as traditional public health data. As we move from initial crisis response to more long-term management, there is both an interest and a need for considering a wider diversity of data sources and impacts. It is difficult for any person to absorb and respond strategically to the broad sets of data that are relevant to the issues regarding COVID management. To address this, the authors propose a five part, integrated data visualization and modeling framework entitled the Vida Decision Support System. The goal of Vida is to create an accessible and openly available online platform that can be customized by the leadership team for a city or region and bring together knowledge from several areas of expertise. The five components of Vida, each of which serve to model a specific domain, include Public Health, Environment, Socio-economic Impacts, Public Policy, and Technology. This framework is currently being designed and evaluated with collaborators in Angola, Brazil, Chile, Indonesia, Mexico and the United States. The environmental data comes from sources such as in-situ sensors and both civil and commercial earth observation instruments (Landsat, VIIRS, Planet Labs’ PlanetScope, etc.) to track factors such as water quality, forest extent and health, air quality, human mobility, and nighttime urban lighting. Similarly, socioeconomic data derives from both in-situ sources, such as local statistical agencies, and from satellite products, such as those hosted by NASA’s Socioeconomic Data and Applications Center. The authors discuss the value provided by this framework to each of the collaborators, the process used to apply the framework to each local context, and future possibilities for Vida. Even though Vida was first developed and applied in response to COVID-19, it has applications in other public health contexts where policy, environment, and socio-economic impacts are closely tied.

Gold mining has played a significant role in Ghana’s economy for centuries. Regulation of this industry has varied over time and while large-scale mining is prevalent in the country, prevalence of artisanal mining, or Galamsey has escalated throughout Ghana in recent years. These mines are not only harmful to human health due to the use of Mercury in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. This study used machine learning and Google Earth Engine to quantify the footprint of artisanal gold mines in Ghana and understand how conversion of forested regions to mining has changed from 2002-2019. We used Landsat imagery and a random forest classification to classify areas of anomalous NDVI loss during this time period and used WorldView image collections to assess the accuracy of the model. We then used a 3-year moving average to calculate the year of maximum derivative NDVI values. We used this calculation to identify the year of conversion to mining. Within the study area of Southwestern Ghana, our analysis showed that approximately 35,000 ha of vegetation were converted to mining. The majority of this mining occurred between 2014 and 2017. Additionally, around 700 ha ha of mining occurred within protected areas defined by the World Database on Protected Areas. Often, artisanal mining appears to be co-located with rivers such as the Orin and Ankobra Rivers, demonstrating the potential of these mines to affect access to clean drinking water. Through the process of gold extraction, these mines leave a distinct footprint with a series of ponds following these major rivers. However, while the footprints of these ponds are spatially distinct, our model does not distinguish between active and inactive ponds if no remediation actions are taken following inactivity. Future research should work towards distinguishing between active and inactive mining sites to better understand current levels of mining activity in Ghana.