This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A new study finds that coastal forest loss in North Carolina's Albemarle-Pamlico Peninsula began accelerating after 2010 and may still be speeding up. The study also found that North Carolina lost more than 20% of its coastal forest between 1985 and 2021.
Researchers used satellite imagery to track coastal forest loss in North Carolina from 1985 through 2021 and found the state experienced a 21% loss of coastal forests, or approximately 64,220 hectares (158,700 acres). Just over 40,000 ha (98,800 acres) of that land was converted to marsh, ghost forest and shrub. That loss did not happen linearly, said Titilayo Tajudeen, lead author of a paper on the study and a graduate researcher at North Carolina State University.
"Between 2010 and 2021, we saw 23,876 ha (59,000 acres) of forest were converted to marsh, ghost forest and shrub," Tajudeen said. "That is 1.5 times higher than the 16,968 ha (41,900 acres) lost to marsh, ghost forest and shrub between 1985 and 2010." Sea level rise was the chief driver of forest loss. As sea levels rise, salty water inundates forest areas, killing trees and converting the area into ghost forest.
Conversion into ghost forest has sped up even more than overall forest loss. Ghost forests grew by 7,561 ha (18,700 acres) between 2010 and 2021, 2.5 times faster than between 1985 and 2010 (3,087 ha (7,600 acres)). Most of the highly affected areas were concentrated within one kilometer (0.6 miles) of the coast.
Some had been subject to a series of extreme events. "This area experienced severe drought from 2007 to 2011, and then also Hurricane Irene in 2011," Tajudeen said. "While these events occurred long before 2021, some of the areas simply never recovered.
Despite being protected, a combination of these extreme events along with rising sea levels has pushed them into new ecological states, including becoming ghost forests." To determine the rate of forest loss, researchers used two satellite imagery tools, known as Landsat 8 and Sentinel-2. Scientists used these image databases to train an AI model, known as a convolutional neural network, which processes the type of grid-like data that Landsat and Sentinel provide. By doing so, the neural network can help identify which areas of the image are forest land and which areas have been converted to marsh, ghost forest and shrub.
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