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Hurricanes devastated Florida’s East Coast – then seagrass made an unexpected comeback

Satellite imagery and AI tracking reveal unexpected recovery in Mosquito Lagoon following hurricanes and ecological collapse

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In Summary:

A new study documents the unexpected return of seagrass in Florida’s Indian River Lagoon after years of ecological decline. Using satellite imagery and machine learning, researchers tracked rapid regrowth in Mosquito Lagoon beginning in spring 2023. While the recovery offers hope, scientists caution that long-term stability is not guaranteed.

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Florida’s Indian River Lagoon has been in decline since 2011, when harmful algal blooms caused a severe loss of seagrass, a foundational component of shallow coastal ecosystems.

Seagrass meadows stabilize sediments, improve water clarity, and provide critical habitat and forage for species ranging from invertebrates to sea turtles and manatees. Seagrass also generates significant economic activity in the state of Florida.

Related: How hurricanes affect roofs in Florida.

The loss of seagrass in the Indian River Lagoon System undermined fisheries, tourism, and wildlife, ultimately leading to the starvation of more than 1,200 manatees from 2020-25, peaking in 2021-22.

Mosquito Lagoon is part of the Indian River Lagoon system that spans 28 miles (45 kilometers), running from Cape Canaveral in the south up to Ponce Inlet in the north. As in the rest of the lagoon system, years of nutrient pollution and recurring algal blooms had diminished seagrass cover to nearly zero by the early 2020s. By most accounts, Mosquito Lagoon had crossed a critical ecological tipping point.

In the fall of 2022, hurricanes Ian and Nicole struck Florida’s east coast within six weeks of one another, bringing intense rainfall, storm surges and coastal erosion. In the immediate aftermath, seagrass declined even further.

But a few months later, in the spring of 2023, seagrass began to return. Satellite imagery revealed rapid and widespread regrowth.

We are geographers who study environmental change. Our research documents this unexpected recovery and examines what it may reveal about ecosystem resilience in heavily degraded coastal systems.

One of us, Hannah Herrero, is a Volusia County native who grew up around the lagoon. She returned to her hometown at the outset of the COVID-19 pandemic. It was there that some local guides and fishermen she’d known for years suggested that our team use satellite imagery to assess the state of collapse in the lagoon.

The study we designed as a result used satellite imagery and machine learning, a type of artificial intelligence that uses advanced algorithms to learn and predict patterns, to track seagrass dynamics in Mosquito Lagoon before, during, and after the storms. This approach allowed us to observe change at a scale and frequency that are difficult to achieve with traditional field survey methods alone.

Tracking seagrass from space

Monitoring seagrass coverage “the old-fashioned way” involves going into the lagoon and laying out transects, straight lines that cut through a landscape, so standard observations could be recorded. We would then have to boat or wade along those lines to measure seagrass extent and locations, and manually create digital maps showing where it is present.

As you can imagine, this is a time-intensive process that’s limited by how far you can boat or swim in a day, and by financial resources.

So we decided to use satellite imagery instead. This method is not without its own challenges – water turbidity, or cloudiness, seasonal variability, and the patchy nature of vegetation that grows on the bottom of the lagoon all make it difficult to observe seagrass growth directly on the imagery.

To address this challenge, our study used imagery from NASA’s Harmonized Landsat–Sentinel program, which combines data from multiple satellites into a consistent record of photos of the same areas taken frequently over time. We analyzed imagery collected between September 2022 and January 2024, focusing on periods before and immediately after the hurricanes, as well as throughout the subsequent recovery.

We applied a machine learning model, Random Forest, to classify each image into seagrass and non-seagrass categories.

The machine learning algorithm is informed by training samples collected in the field, but once the model has learned the seagrass signature, it can then apply the classification model to the rest of the lagoon and, over time, with limited human input. We can then validate this classification.

two women wading in a body of water
The authors wade into Mosquito Lagoon to track seagrass growth while training their AI model. Captain William B. Wolfson, Grassroots Guide Service, New Smyrna Beach.

Heading into the field

First, we had to train the model using hundreds of GPS points collected in the field over multiple seasons. This step helps ensure that satellite classifications align with on‑the‑ground conditions and that images are accurately interpreted.

Over several weeks during the summers of 2020 through 2023, our team spent many hours navigating Mosquito Lagoon in a small skiff designed for shallow waters, recording the presence of seagrass.

It wasn’t always easy – Florida summers are intensely hot and humid, and Mosquito Lagoon definitely lived up to its name. But we got to see a wide variety of wildlife, including manatees, dolphins, sea turtles, and alligators. And occasionally, on lucky days, we even spotted a roseate spoonbill or reddish egret.

Our experience in the field highlighted why this system matters: Mosquito Lagoon is a remarkably vibrant place, teeming with wildlife. These long days on the lagoon, surrounded by its biodiversity and immersed in its unique sense of place, are what anchor the remote sensing data to on-the-ground ecological conditions and make the resulting models credible.

timelapse gif of Mosquito Lagoon seagrass coverage
This time-lapse of satellite images shows the three phases of seagrass coverage the authors observed in Mosquito Lagoon between September 2022 and January 2024. Stephanie Insalaco-Wyner

What we found

Our analysis reveals three distinct phases of seagrass coverage.

First, seagrass declined sharply following hurricanes Ian and Nicole. By December 2022 and early 2023, satellite imagery showed virtually no detectable seagrass across the lagoon.

Then, in March 2023, we identified a statistically significant shift. Seagrass began to reappear, initially in small, scattered patches.

Finally, during late spring and summer 2023, seagrass expanded rapidly. By July 2023, it covered more than 20% of the lagoon, levels not observed in more than a decade. Coverage then declined again during the winter of 2023–24, as expected based on seasonal growth cycles. But even our last observation, completed in January 2024, showed seagrass covering 4.3% of the lagoon, substantially higher than pre-recovery levels during the winter season.

In spring 2026, seagrass in Mosquito Lagoon has remained at stable levels. Although it still experiences fluctuations due to algal blooms, seasonality, and other ecosystem changes, we have not seen a complete loss of seagrass again, as was occurring for over a decade.

Importantly, this pattern was not random. Regrowth occurred primarily in the central and southern parts of the lagoon, areas historically known to support dense seagrass meadows. The timing also aligned with established seagrass seasonal growth patterns, which strengthens our confidence that the observed changes reflect true ecological recovery.

How storms may have contributed

We cannot prove that hurricanes directly caused the seagrass recovery that we document in our study. Further study beyond the scope of our work is needed to evaluate this possibility. However, we believe the sequence of events suggests that the storms may have altered environmental conditions in ways that enabled regrowth.

Hurricane Ian delivered large volumes of fresh water into the lagoon, potentially suppressing salt‑tolerant macroalgae that compete with seagrass for sunlight and nutrients.

Six weeks later, Hurricane Nicole breached coastal dunes and created several new inlets between the lagoon and the Atlantic Ocean. These openings allowed saltwater into the lagoon, likely altering salinity and water circulation.

The hurricanes may also have redistributed seagrass fragments and mobilized dormant seed banks, accelerating regrowth once conditions stabilized. Ecologists have observed similar mechanisms in other coastal systems affected by tropical cyclones.

seagrass underwater in Mosquito Lagoon
The surprising comeback of seagrass in Mosquito Lagoon bodes well for local wildlife and the people whose livelihoods depend on it. Hannah Herrero

Beyond Mosquito Lagoon

Mosquito Lagoon’s collapse and eventual tentative recovery illustrate both the vulnerability and resilience of coastal ecosystems. Even after years of decline, the Mosquito Lagoon coastal ecosystem demonstrated an ability to recover relatively rapidly when physical conditions shifted.

At the same time, resilience does not guarantee permanence, and we believe this recovery should be viewed cautiously.

From a practical standpoint, our study also highlights the value of satellite imagery and machine learning for ecosystem monitoring. These tools allow scientists, resource managers, and local communities to detect change consistently and respond before losses spread.The Conversation

Key Points:

  • Seagrass in the Indian River Lagoon declined sharply after harmful algal blooms began in 2011
  • More than 1,200 manatees died between 2020 and 2025 due to habitat loss tied to seagrass decline
  • Satellite data shows seagrass in Mosquito Lagoon rebounded rapidly in 2023
  • Hurricanes Ian and Nicole may have helped create conditions for regrowth
  • Researchers say the recovery highlights both ecosystem resilience and ongoing vulnerability

This article is republished from The Conversation under a Creative Commons license.

Indian River Lagoon, Mosquito Lagoon, seagrass recovery, Florida environment, algal blooms, manatees, coastal ecosystems, satellite imagery, machine learning, hurricane impact

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