When Captain John Smith first explored the Chesapeake Bay in 1607, the “Great Shellfish Bay,” as it was called by the Algonquian Natives, had transparent waters teeming with aquatic life, massive oyster reefs, tremendous waterfowl flocks, and uninterrupted virgin hardwood forests on its shores. Four hundred years after Smith’s famous surveying expedition, North America’s largest estuary exhibits signs of stress and its once legendary seafood productivity has waned.
In the intervening years since Smith’s exploration, farmlands, cities, and suburbs have largely replaced the dense forests surrounding the Bay, the waters of the Chesapeake have become brownish-green with overabundant algae, and the once bountiful fish, crabs, and oysters have fallen victim to over-harvesting, habitat destruction, and disease. While still a commercially important ecosystem—home to some 3700 species—four centuries of regional population growth have crippled the Bay’s productivity and earned it a place on the U.S. Environmental Protection Agency’s “dirty waters” list.
Information collected from 438-miles above Earth by the Landsat satellites, has brought Bay scientists one step closer to better controlling pollution levels throughout the watershed. This is because Landsat affords a bird’s-eye view of the watershed at a scale appropriate for deciphering human land use and land cover patterns.
“Land cover data is critical for decision-making at the Chesapeake Bay Program,” CBP GIS team leader, John Wolf says. To take on the Herculean task of water quality improvement over such a vast area, Bay managers use models to mathematically synthesize large amounts of information and predict the most effective methods for lessening watershed pollution. The nature of the watershed’s landscape––the amount of paved or impervious surfaces, the proportions of cropland and rangeland, forested regions, marshland and the like––is a key input for these predictive models8. Accordingly, Landsat-derived land cover data are fed into the Chesapeake Bay Program’s Watershed Model in order to predict where nutrient loads can be expected and where managers should take action.