As the water regime changes within Everglades National Park in response to mitigation efforts, identifying mangrove presence and tracking long-term changes is imperative to park management.
Water is one of the most important resources on the planet, not just for humans, but for all other forms of life. For Everglades National Park, a 120-mile freshwater river known as the ÛÏRiver of GrassÛ provides the park with much of its freshwater flow, allowing for a high diversity of wildlife (1). This park, consisting of 1.5 million acres of federally protected land, is the largest subtropical system in the United States (2). Following the ÛÏRiver of GrassÛ to the coast, you will then find mangroves just before you reach the ocean. These specialized plants live in the transition zone between salt and freshwater habitats, and are essential to the continued functioning of the system. Managers and scientists at Everglades National Park are interested in studying mangroves and their distributions as an indicator for freshwater flow in the park.
In the early 1900s, flooding became a major issue in southern Florida, and in response Congress implemented flood control measures by enacting the South Florida Project, developing over 1000 miles of canals, 720 miles of levees, and 16 pumping stations (3), which effectively diverted a large percentage of the freshwater flow away from the Everglades. As a result, mangroves encroached inland as the freshwater flow became depleted and saltwater intruded into the marshes of the Everglades.
While many research projects have been conducted in the Everglades in the last several decades, Everglades National Park tasked NASA DEVELOP to assess long-term trends in mangrove distributions. For this project the team used Google Earth Engine API, a recently-released platform that easily allows users to import archived satellite data, conduct analyses, and collaborate with other users within Google’s cloud computing environment. Over the span of two 10-week terms, DEVELOP teams were able to produce a replicable methodology identifying mangrove distributions from 1995-2016, and forecasted mangrove distribution to the year 2030 using imagery from Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and the European Space Agency’s Sentinel-2 MSI (Figure A).
Mangrove abundance changed dramatically over the 20- year study period (Figure B), with the greatest change occurring in the 2005-2010 period with over 4000 hectares (ha) lost. This period was characterized by several hurricanes making landfall in the Everglades, which can cause dramatic shifts in vegetation regimes and take several years to return to their original state. Overall, mangrove extent decreased between 1995 and 2016, which is mostly attributed to the return of freshwater flow through the park due to new water management practices. However, these changes are slow-going, and mangroves have not been replaced by freshwater marshes in most areas, but rather saltwater marsh/sawgrass prairie. The forecasting maps produced indicated that if the return of freshwater flow to the park has no impact, then mangroves will continue to expand inward at the expense of freshwater marshes. However, if restoration efforts are successful and do impact the ecology of the park, mangroves are pushed toward the coast as the freshwater allows for the expansion of freshwater marsh in the southwestern region of the park.
These results were tailored to inform the current management strategy of the park ÛÒ to restore freshwater flow to the ÛÏRiver of Grass.Û Our results indicate that as more freshwater flow is added to the Everglades, mangroves will be pushed back toward the coast, which corresponds to diminished saltwater intrusion into the park. Further, these classifications are an easy way to assess trends in mangrove coverage over time, without having to leave the office. The methodology produced to identify mangroves can easily be adapted to studies of mangroves in other regions of the world, and the methodology also employed a median-pixel cloud filter that is also widely applicable to other research questions beyond mangroves.
The median-pixel cloud filter allows the user to fill gaps in a scene resulting from the cloud and cloud shadow mask included with Landsat (FMask). Using multiple scenes from the series collection, a temporal aggregate is created to fill in gaps in the final image by using the median value of pixels from the previous year (Figure C). A similar approach was applied to Sentinel-2 imagery using a cloud score from band QA60 and reducing to a percentile to keep only pixels with minimal cloud cover. This masking procedure is especially useful in study areas with high cloud frequency where cloud cover is often a deterrent to using satellite imagery.
Rachel Cabosky is a current student at Miami University and worked with DEVELOP at NASA Langley Research Center as an independent research consultant on the Everglades Eco Forecasting project.
Caitlin Toner is a recent graduate from Macalester College and is working with DEVELOP at NASA Langley Research Center and Bureau of Land Management at Idaho State University’s GIS Training and Research Center as an independent research consultant.
Amy Wolfe is a recent graduate from the University of Virginia. Since working with DEVELOP at NASA Langley Research Center as an independent research consultant, she has since moved to work with GIS in the private sector.
Tyler Rhodes is a recent graduate from Old Dominion University. Since working with DEVELOP at NASA Langley Research Center as an independent research consultant, he has since moved to Piscah National Forest with the U.S. Forest Service.
Adama Ba is a recent graduate from George Mason University, and worked with DEVELOP at NASA Langley Research Center as an independent research consultant.
Emily GotschalkåÊis a graduate of Christopher Newport University and is working with DEVELOP at NASA Langley Research Center as an independent research consultant.