Chesapeake Conservancy’s data science team has developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. Supported by EPRI, an independent, non-profit energy research and development institute; Lincoln Electric System; and the Grayce B. Kerr Fund, Inc., this method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. The results are published in the peer-reviewed journal Science of the Total Environment.

The team trained a machine learning (convolutional neural network) model for high-resolution (1m) wetland mapping with freely available data from three areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory, mapped wetlands with 94% accuracy.

“We’re happy to support this exciting project as it explores new methods for wetlands delineation using satellite imagery,” says EPRI Principal Technical Leader Nalini Rao. “It has the potential to save natural resource managers time in the field by using a GIS tool right from their desks. Plus, it can help companies and the public manage impacts to wetlands as infrastructure builds are planned to meet decarbonization targets.”

The Infrastructure Investment and Jobs Act is pouring hundreds of billions of dollars into projects that will have an impact on the landscape, according to Environmental Policy Innovation Center’s Restoration Economy Center Director Becca Madsen, a former EPRI researcher. "However, the data that we rely on to minimize impacts to wetlands is distressingly outdated. There has never been a better time to invest in updating our nation’s wetland data and establishing a sustainable and cost-effective process for keeping them updated.”

When the model is scaled up to predict wetlands in larger areas like the Chesapeake Bay, it will be a game changer, according to Chesapeake Conservancy’s Senior Data Scientist Kumar Mainali. “It obviates the need for manual mapping of wetlands as well as mapping wetlands with traditional machine learning which require a lot of data processing, curation and manual feature engineering, both of which are time-consuming, labor intensive and very expensive.”

What it means

The new model will help infrastructure planners avoid wetlands in the planning process, resulting in cost savings and wetlands conservation. Potential beneficial situations include ongoing efforts to expand and develop renewable energy, which requires expanding electric power infrastructure.

The product of the model is a map of wetland probability. This probability data may be used to map the most likely wetland extent, but if users prefer, they can map wetland extent with a lower probability threshold. The resulting map limits the likelihood of wetland omission even though it maps more wetlands than are present in reality.

There could also be potential to use this model to map locations where wetlands have already been lost since they were mapped with NWI. Additionally, potential locations for wetland restoration could also be identified. For example, persistently wet agricultural fields are picked up by the model even though for the purposes of field wetland delineation, these areas are not considered wetlands when actively farmed.

Visit the Chesapeake Conservancy website for more information.