监测危机中的生态系统:利用深度学习测量佛罗里达州蚊子泻湖的海草草甸损失

Stephanie A. Insalaco, Hannah V. Herrero, Russ Limber, Clancy Oliver, William B. Wolfson
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引用次数: 0

摘要

佛罗里达州蚊子泻湖的生态系统自 2010 年代以来迅速恶化,关键海草物种明显减少。海草对泻湖中的许多物种至关重要,但营养过剩、藻类大量繁殖、划船、海牛吃草以及其他因素导致了海草的减少。为了了解这种减少,一个深度神经网络分析了 2000 年至 2020 年的 Landsat 图像。结果显示,2013 年后海草大量减少,与 2011-2013 年的超级藻华相吻合。海草丰度每年都不同,模型在海草覆盖率较高的年份表现最佳。虽然深度学习方法成功识别了海草,但它也揭示了近期海草覆盖率几乎为零的情况。如果配合适当的蚊子湖恢复政策,这种监测方法将有助于生态系统的恢复。
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Monitoring an Ecosystem in Crisis: Measuring Seagrass Meadow Loss Using Deep Learning in Mosquito Lagoon, Florida
The ecosystem of Mosquito Lagoon, Florida, has been rapidly deteriorating since the 2010s, with a notable decline in keystone seagrass species. Seagrass is vital for many species in the lagoon, but nutrient overloading, algal blooms, boating, manatee grazing, and other factors have led to its loss. To understand this decline, a deep neural network analyzed Landsat imagery from 2000 to 2020. Results showed significant seagrass loss post-2013, coinciding with the 2011–2013 super algal bloom. Seagrass abundance varied annually, with the model performing best in years with higher seagrass coverage. While the deep learning method successfully identified seagrass, it also revealed that recent seagrass coverage is almost non-existent. This monitoring approach could aid in ecosystem recovery if coupled with appropriate policies for Mosquito Lagoon's restoration.
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