Coastal forested wetlands (CFWs) are a critical component of the coastal wetland mosaic and offer numerous ecosystem services (i.e. carbon sequestration, storm surge attenuation, groundwater recharge)1. Coastal climate change (i.e. sea level rise, storm surge, hurricanes) is expected to play a large role in the decline of this system2 (Fig. 1). Nearly 14,000 km2 of CFWs were lost in the North American Coastal Plain from 1996 – 2016, which is ~4x greater than global mangrove decline over a similar time period 3,4 (Fig. 2). However, the mangrove loss is mostly anthropogenic, whereas CFW loss was driven primarily by climate change. Additionally, conversion of CFW to marsh not only represents a significant shift in the ecological community, but also in carbon stocks. It is estimated that marshes will take 130 to 760 years for their soil carbon to store the same amount of carbon that existing CFWs hold currently (per unit area)5. This highlights the potential role that CFWs play in carbon sequestration at the global scale and should motivate efforts to conserve and restore CFWs.
However, there is a critical information gap in that there are no global datasets on the presence or condition of coastal forested wetlands. The best dataset for this habitat is produced by the NOAA Coastal Change Analysis program, which is only generated for coastal regions of the United States every 5 years using Landsat imagery. Critically, this impedes researchers and communities from understanding how this habitat is changing around the world. A high resolution, global dataset would allow scientists to answer myriad questions, which are not limited to:
1) How is global CFW areal coverage being affected by climate change?; 2) What are the present carbon stocks of the world’s CFWs?; and 3) How is carbon sequestration potential of CFW’s modified by climate change?
High spatial resolution satellite imagery can be leveraged to produce a global map of CFW coverage. Answers to the aforementioned questions are critical to enhance our global understanding of carbon sequestration and stocks but are currently unanswerable due to the lack of global data regarding CFWs. Filling this information gap requires an interdisciplinary team, which should include experts in remote sensing (ecology and hydrology), machine learning, and data visualization. A suite of geospatial data will be needed to accomplish this task, which include optical and radar satellite data from the Sentinel 1 and 2 missions, respectively, LiDAR, MERIT DEM, NASA Global Ecosystem Dynamics Investigation (GEDI), the MERIT Global Hydrography data, and the Global Surface Water map layers6-8. These datasets will be used to train a machine learning model based on known CFWs. Developing the model will require a lot of computational power, which can be offset by partially or fully developing the classification scheme in Google Earth Engine (GEE), which is a cloud-based geospatial platform. It has access to the above datasets, or they can be added by the user, and has a machine learning capability built-in and through TensorFlow. By using numerous modes of data, it should be possible to extract additional inferences about CFWs including landscape context. These landscape context data inferences can be used proactively to develop a habitat suitability map that indicates areas where CFWs can exist currently. Simple hydrologic models can be used with the habitat suitability map to identify locations for future habitability under different climate scenarios. A data visualization specialist will be critical in developing ways to make the data interpretable and useful for other users. While the task may seem overwhelming, efforts such as this have been accomplished at a much smaller regional scale9,10. Developing an accurate, global classification scheme is always challenging, but the benefits are too important for the challenge to be a deterrent.
These data can be leveraged to proactively aid climate change mitigation and restoration efforts. Unlike other coastal ecosystem types, CFWs are unable to keep pace with the current rate of sea level rise and don’t have the natural ability to transgress upslope11–13. As such, many CFWs are dying in place due to anthropogenic hydrologic controls reducing freshwater flows and existing drainage networks acting as conduits for saltwater intrusion14–17. Having an accurate understanding of where CFWs exist will allow for more targeted hydrologic intervention with the hopes of restoration. The habitat suitability map would allow conservation groups to develop assisted migration plans with an eye toward future environmental conditions18. Further, those interested in carbon mitigation/banking could identify areas where CFWs can be planted currently, which would help to offset CFW loss via afforestation efforts. The research will produce a new, unique dataset whose products will lead to real-world benefits beyond their ability to advance our scientific understanding of the world.
Elliott White Jr. is an Assistant Professor in the Earth System Science Department at Stanford University. He is a coastal wetland scientist that uses an interdisciplinary (i.e. ecohydrology, remote sensing, and biogeochemistry) approach to understand how coastal vegetation are being affected by climate change.
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The goal of our lab is to create a high-spatial resolution map of coastal forested wetlands at global scale. If we know precisely where these ecologically critical but fragile forests are located, we can manage freshwater flows to counteract saltwater introgression due to rising sea levels, and we can assist in their migration inland, preserving their critical function in protecting coastlines and sequestering carbon.
Across the continent, a number of first nations are in the process of reintroducing bison to the grasslands in which they were once the primary grazer and an ecologically vital species. Initial experiences and evolutionary considerations suggest that this may be ecologically beneficial in terms of grassland biodiversity, carbon cycle, and resilience to climate change. However, these questions have not yet been studied at scale. In this lab, we will leverage remote sensing to scale up from ground measurements, establishing the large-scale patterns of bison impact.
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Markets in voluntary carbon credits are increasingly providing a flow of capital for regenerating ecosystems. The problem is, thriving and resilient ecosystems are not just carbon. We need to find ways to structure credits to incentivize the diverse and functional ecosystems we want, not merely high-concentrations of carbon. We will design the technological tools to support a market in bundled ecological credits.
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Leveraging The Earthshot Institute’s broad scientific and technical expertise, the Impact and Risk Lab helps investors and governments who earnestly want to forecast, measure, and address the socio-ecological risks to and/or impacts from their work. For a given system, we build simple process-based models to identify key socio-ecological risks and outcomes. We then draw on big data to improve and train our models, generating quantitative predictions and developing measurement systems for verification.