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NOAA Scientist Helps Make Mapping Vital Seagrass Habitat Easier and More Accurate

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Shoal grass seagrass on a sandy ocean floor.

Seagrass beds serve as important habitat for a variety of marine life, and understanding their growth patterns better can help fisheries management and restoration efforts. (NOAA)

Amy Uhrin was sensing a challenge ahead of her. As a NOAA scientist working on her PhD, she was studying the way seagrasses grow in different patterns along the coast, and she knew that these underwater plants don’t always create lush, unbroken lawns beneath the water’s surface.

Where she was working, off the North Carolina coast near the Outer Banks, things like the churning motion of waves and the speed of tides can cause seagrass beds to grow in patchy formations. Clusters of bigger patches of seagrass here, some clusters of smaller patches over there. Round patches here, elongated patches over there.

Uhrin wanted to be able to look at aerial images showing large swaths of seagrass habitat and measure how much was actually seagrass, rather than bare sand on the bottom of the estuary. Unfortunately, traditional methods for doing this were tedious and tended to produce rather rough estimates. These involved viewing high-resolution aerial photographs, taken from fixed-wing planes, on a computer monitor and having a person digitally draw lines around the approximate edges of seagrass beds.

While that can be fairly accurate for continuous seagrass beds, it becomes more problematic for areas with lots of small patches of seagrass included inside a single boundary. For the patchy seagrass beds Uhrin was interested in, these visual methods tended to overestimate the actual area of seagrass by 70% to more than 1,500%. There had to be a better way.

Seeing the Light

Patches of seagrass beds of different sizes visible from the air.

Due to local environmental conditions, some coastal areas are more likely to produce patchy patterns in seagrass, rather than large beds with continuous cover. (NOAA)

At the time, Uhrin was taking a class on remote sensing technology, which uses airborne—or, in the case of satellites, space-borne—sensors to gather information about the Earth’s surface (including information about oil spills). She knew that the imagery gathered from satellites (i.e. Landsat) is usually not at a fine enough resolution to view the details of the seagrass beds she was studying. Each pixel on Landsat images is 30 meters by 30 meters, while the aerial photography gathered from low-flying planes often delivered resolution of less than a meter (a little over three feet).

Uhrin wondered if she could apply to the aerial photographs some of the semi-automated classification tools from imagery visualization and analysis programs which are typically used with satellite imagery. She decided to give it a try.

First, she obtained aerial photographs taken of six sites in the shallow coastal waters of North Carolina’s Albemarle-Pamlico Estuary System. Using a GIS program, she drew boundaries (called “polygons”) around groups of seagrass patches to the best of her ability but in the usual fashion, which includes a lot of unvegetated seabed interspersed among seagrass patches.

Six aerial photographs of seagrass habitat off the North Carolina coast, with yellow boundary lines drawn around general areas of seagrass habitat.

Aerial photographs show varying patterns of seagrass growth at six study sites off the North Carolina coast. The yellow line shows the digitally drawn boundaries around seagrass and how much of that area is unvegetated for patchy seagrass habitat. (North Carolina Department of Transportation)

Next, Uhrin isolated those polygons of seagrass beds and deleted everything else in each image except the polygon. This created a smaller, easier-to-scan area for the imagery visualization program to analyze. Then, she “trained” the program to recognize what was seagrass vs. sand, based on spectral information available in the aerial photographs.

Though limited compared to what is available from satellite sensors, aerial photographs contain red, blue, and green wavelengths of light in the visible spectrum. Because plants absorb red and blue light and reflect green light (giving them their characteristic green appearance), Uhrin could train the computer program to classify as seagrass the patches where green light was reflected.

Classify in the Sky

Amy Uhrin stands in shallow water documenting data about seagrass inside a square frame of PVC pipe.

NOAA scientist Amy Uhrin found a more accurate and efficient approach to measuring how much area was actually seagrass, rather than bare sand, in aerial images of coastal North Carolina. (NOAA)

To Uhrin’s excitement, the technique worked well, allowing her to accurately identify and map smaller patches of seagrass and export those maps to another computer program where she could precisely measure the distance between patches and determine the size, number, and orientation of seagrass patches in a given area.

“This now allows you to calculate how much of the polygon is actually seagrass vegetation,” said Uhrin, “which is good for fisheries management.” The young of many commercially important species, such as blue crabs, clams, and flounder, live in seagrass beds and actively use the plants. Young scallops, for example, cling to the blades of seagrass before sliding off and burrowing into the sediment as adults.

In addition, being able to better characterize the patterns of seagrass habitat could come in handy during coastal restoration planning and assessment. Due to local environmental conditions, some areas are more likely to produce patchy patterns in seagrass. As a result, efforts to restore seagrass habitat should aim for restoring not just cover but also the original spatial arrangement of the beds.

And, as Uhrin noted, having this information can “help address seagrass resilience in future climate change scenarios and altered hurricane regimes, as patchy seagrass areas are known to be more susceptible to storms than continuous meadows.”

The results of this study, which was done in concert with a colleague at the University of Wisconsin-Madison, have been published in the journal Estuarine, Coastal and Shelf Science.

Author: Ashley Braun

Ashley Braun is the Web Editor for NOAA's Office of Response and Restoration (OR&R). That means she writes and edits a good portion of what appears on OR&R’s web pages, blog, and social media.

8 thoughts on “NOAA Scientist Helps Make Mapping Vital Seagrass Habitat Easier and More Accurate

  1. Hi. Excellent work. Does it work in water with low water transparency due to suspended sediments, like in coastal mangrove lagoon?

    • Thanks for your question, Pedro! We’ve reached out to Dr. Uhrin and will get back to you with her answer.

    • Hi Pedro, here’s an answer from Dr. Amy Uhrin: “I appreciate the comment. Because this paper represents a portion of a larger study focusing on seagrass spatial configuration and utilizing historically studied seagrass field sites that are confined to the main body of the Albemarle-Pamlico Sound Estuary System, the classification technique presented in the paper was not tested in the highly colored and turbid waters that occur in the interior of the estuary as you move up into the marsh-fringed shallow portions of feeder tributaries.”

  2. Good for her. My first impression was that this was one more governmentally “crafted” article showing Homo sapiens’ destruction of the world, the universe and everything. I was pleasantly surprised.

  3. Sounds good but it doesn’t address the fact that often, NOAA in Florida is counting the unvegetated areas as seagrass habitat if there has been seagrass coverage in years past based on aerial photography, even if it is bare in the most recent growing season. Mitigation is being requested by NOAA through the Corps’ regulatory permitting program for the unvegetated seagrass habitat.

    • Hi Danielle, here’s a response from Dr. Amy Uhrin: “Thanks for your comment. Similar to Florida, the North Carolina Coastal Habitat Protection Plan (CCHPP) defines seagrass habitat as areas that “have been vegetated by one or more [seagrass] species … within the past 10 annual growing seasons and that meet the average physical requirements of water depth (six feet or less), average light availability (secchi depth of one foot or more), and limited wave exposure that characterize the environment suitable for growth of SAV.” Therefore, if seagrass habitat extent is the variable of choice, then photointerpretation is likely sufficient for mapping efforts.

      However, while mapped polygon data derived from visual photinterpretation are indicative of seagrass habitat extent these are insufficient to accurately characterize seagrass spatial pattern or estimate area actually occupied by seagrass vegetation because small patches (< 1 meter diameter) are mapped as bound aggregates. Because I am interested in how seagrass spatial configuration changes in response to localized hydrodynamic drivers (i.e., wind-wave exposure, tidal currents), in an area of the country where seagrass seascapes display high spatial pattern heterogeneity (lots of small patches), I need fine-scale maps of seagrass seascapes for which spatial pattern can be characterized. This was the driving force behind this work.

      Thus, I developed a classification procedure that would identify these small patches and eliminate the bare substrate signature. The resulting seagrass maps can be pulled into geospatial software (e.g., Fragstats) to characterize fine-grained spatial pattern using landscape configuration metrics."

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