Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach
Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/831 |
| _version_ | 1797263960992382976 |
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| author | Mohammadali Hemati Masoud Mahdianpari Hodjat Shiri Fariba Mohammadimanesh |
| author_facet | Mohammadali Hemati Masoud Mahdianpari Hodjat Shiri Fariba Mohammadimanesh |
| author_sort | Mohammadali Hemati |
| collection | DOAJ |
| description | Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha<sup>−1</sup> was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems. |
| first_indexed | 2024-04-25T00:21:19Z |
| format | Article |
| id | doaj.art-9af813d29e0440c39e3cfa63544d401f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:19Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9af813d29e0440c39e3cfa63544d401f2024-03-12T16:54:09ZengMDPI AGRemote Sensing2072-42922024-02-0116583110.3390/rs16050831Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale ApproachMohammadali Hemati0Masoud Mahdianpari1Hodjat Shiri2Fariba Mohammadimanesh3Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, CanadaDepartment of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, CanadaCivil Engineering Department, Faculty of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X7, CanadaC-Core, St. John’s, NL A1B 3X5, CanadaCoastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha<sup>−1</sup> was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems.https://www.mdpi.com/2072-4292/16/5/831aboveground biomasscoastal wetlandstidal marshAVIRIS-NGmultispectralSAR |
| spellingShingle | Mohammadali Hemati Masoud Mahdianpari Hodjat Shiri Fariba Mohammadimanesh Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach Remote Sensing aboveground biomass coastal wetlands tidal marsh AVIRIS-NG multispectral SAR |
| title | Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach |
| title_full | Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach |
| title_fullStr | Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach |
| title_full_unstemmed | Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach |
| title_short | Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach |
| title_sort | integrating sar and optical data for aboveground biomass estimation of coastal wetlands using machine learning multi scale approach |
| topic | aboveground biomass coastal wetlands tidal marsh AVIRIS-NG multispectral SAR |
| url | https://www.mdpi.com/2072-4292/16/5/831 |
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