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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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/15/3765 |
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| author | Bingqing Liu Tom Sevick Hoonshin Jung Erin Kiskaddon Tim Carruthers |
| author_facet | Bingqing Liu Tom Sevick Hoonshin Jung Erin Kiskaddon Tim Carruthers |
| author_sort | Bingqing Liu |
| collection | DOAJ |
| description | 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 complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for <i>with/without SAV</i> scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 10<sup>5</sup> tonne CO<sub>2</sub>e y<sup>−1</sup> and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 10<sup>5</sup> tonne CO<sub>2</sub>e y<sup>−1</sup>) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales. |
| first_indexed | 2024-03-11T00:17:53Z |
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| id | doaj.art-dfc60445078145baae82855dcbf030f4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:17:53Z |
| publishDate | 2023-07-01 |
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| spelling | doaj.art-dfc60445078145baae82855dcbf030f42023-11-18T23:30:29ZengMDPI AGRemote Sensing2072-42922023-07-011515376510.3390/rs15153765Quantifying 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 NetworkBingqing Liu0Tom Sevick1Hoonshin Jung2Erin Kiskaddon3Tim Carruthers4The Water Institute, 1110 S. River Road, Baton Rouge, LA 70802, USAThe Water Institute, 1110 S. River Road, Baton Rouge, LA 70802, USAThe Water Institute, 1110 S. River Road, Baton Rouge, LA 70802, USAThe Water Institute, 1110 S. River Road, Baton Rouge, LA 70802, USAThe Water Institute, 1110 S. River Road, Baton Rouge, LA 70802, USASubmerged 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 complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for <i>with/without SAV</i> scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 10<sup>5</sup> tonne CO<sub>2</sub>e y<sup>−1</sup> and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 10<sup>5</sup> tonne CO<sub>2</sub>e y<sup>−1</sup>) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.https://www.mdpi.com/2072-4292/15/15/3765submerged aquatic vegetationcarbon balance modelLandsat 8/9-OLIdeep learning |
| spellingShingle | Bingqing Liu Tom Sevick Hoonshin Jung Erin Kiskaddon Tim Carruthers 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 Remote Sensing submerged aquatic vegetation carbon balance model Landsat 8/9-OLI deep learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | submerged aquatic vegetation carbon balance model Landsat 8/9-OLI deep learning |
| url | https://www.mdpi.com/2072-4292/15/15/3765 |
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