NASA and U.S. Geological Survey (USGS) satellite images have unveiled a concerning phenomenon in Florida: the emergence of “ghost forests” in the state’s mangrove ecosystems.

These watery graveyards of once-thriving trees are a product of mangrove forests’ increasing vulnerability to intensifying storms and environmental change.

Mangroves are vital to the Florida Everglades. Known for their picturesque, half-submerged roots that create natural tunnels for kayakers, these trees also serve essential ecological and environmental functions.

Mangroves stabilize coastlines, buffer against erosion, store carbon and provide critical shelter for marine life.

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Florida is estimated to have 600,000 acres of mangrove forests, though their distribution has changed in recent decades mostly as a consequence of human activity, according to the state’s Department of Environmental Protection.

Although mangroves are renowned for their resilience, including their ability to withstand flooding, the recent findings published in the journal Remote Sensing of Environment revealed that stronger and more frequent hurricanes are overwhelming this natural adaptability.

Using decades of Landsat satellite data spanning from 1999 to 2023, researchers identified troubling patterns in mangrove recovery following hurricanes. Unlike previous studies that typically focused on single events, this study paints a broader picture of how these forests respond to storms over time.

The team categorized mangrove conditions into four groups: healthy (unaffected by storms), disturbed (impacted by storms but recovered within a single growing season), recovering (requiring more than one season to recover) and declining (failing to recover and entering long-term decline).

The aftermath of recent hurricanes revealed a sharp rise in the number of declining mangroves. These forests, unable to recover, often transition into “ghost forests,” where dead trees haunt the once bustling coastline.

“Our monitoring has shown a significant increase in the area of mangroves that have lost their natural recovery capacity following recent hurricanes, such as Irma in 2017 and Ian in 2022,” Zhe Zhu, a co-author of the study and a former member of the USGS–NASA Landsat science team said in a statement.

The Landsat-based maps from the study showed clear contrasts between mangrove recovery after hurricanes Wilma (2005) and Irma (2017) in the southern Everglades National Park bordering the Gulf of Mexico.

While most mangroves recovered naturally after Wilma, a significant portion of mangroves impacted by Irma entered a long-term decline, turning into ghost forests.

“Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions,” the researchers wrote in the study.

A key innovation of this study was the use of a machine-learning algorithm to analyze satellite data, enabling continuous and detailed tracking of mangrove conditions. This approach provides an early warning system, helping land managers identify areas at risk before losses become irreversible.

“Our research aims to provide an early warning system for mangrove decline, helping to identify areas at risk before irreversible loss occurs,” Zhu said.

The researchers plan to refine their algorithm to better differentiate between the drivers of mangrove change, such as extreme weather, rising sea levels or human activity. They also aim to expand the study to monitor mangroves globally.

“By identifying whether changes are driven by extreme weather events, rising sea levels, or human activities, we can provide more targeted insights for conservation and management strategies in a rapidly changing environment,” Zhu said.

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Reference

Yang, X., Zhu, Z., Kroeger, K. D., Qiu, S., Covington, S., Conrad, J. R., & Zhu, Z. (2024). Tracking mangrove condition changes using dense Landsat time series. Remote Sensing of Environment, 315, 114461. https://doi.org/10.1016/j.rse.2024.114461