Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically comp...

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Bibliographic Details
Main Authors: Bingqing Liu, Tom Sevick, Hoonshin Jung, Erin Kiskaddon, Tim Carruthers
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Remote Sensing
Subjects:
submerged aquatic vegetation
carbon balance model
Landsat 8/9-OLI
deep learning
Online Access:https://www.mdpi.com/2072-4292/15/15/3765

Internet

https://www.mdpi.com/2072-4292/15/15/3765

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