RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System
Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3406 |
| _version_ | 1797520915351732224 |
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| author | Grayson R. Morgan Cuizhen Wang James T. Morris |
| author_facet | Grayson R. Morgan Cuizhen Wang James T. Morris |
| author_sort | Grayson R. Morgan |
| collection | DOAJ |
| description | Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., <i>Spartina alterniflora</i>) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; <i>R</i><sup>2</sup> = 0.39) and excess green index (ExG; <i>R</i><sup>2</sup> = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for <i>S. alterniflora</i> biomass estimation using sUAS as an on-demand, personal remote sensing tool. |
| first_indexed | 2024-03-10T08:05:16Z |
| format | Article |
| id | doaj.art-b6dc361459d04256a826a924f75178b0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:16Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b6dc361459d04256a826a924f75178b02023-11-22T11:08:25ZengMDPI AGRemote Sensing2072-42922021-08-011317340610.3390/rs13173406RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial SystemGrayson R. Morgan0Cuizhen Wang1James T. Morris2Department of Geography, University of South Carolina, Columbia, SC 29208, USADepartment of Geography, University of South Carolina, Columbia, SC 29208, USABelle Baruch Institute for Marine & Coastal Sciences, University of South Carolina, Columbia, SC 29208, USACoastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., <i>Spartina alterniflora</i>) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; <i>R</i><sup>2</sup> = 0.39) and excess green index (ExG; <i>R</i><sup>2</sup> = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for <i>S. alterniflora</i> biomass estimation using sUAS as an on-demand, personal remote sensing tool.https://www.mdpi.com/2072-4292/13/17/3406unmanned aircraftbiomasscoastalwetlandRGBdrone |
| spellingShingle | Grayson R. Morgan Cuizhen Wang James T. Morris RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System Remote Sensing unmanned aircraft biomass coastal wetland RGB drone |
| title | RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System |
| title_full | RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System |
| title_fullStr | RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System |
| title_full_unstemmed | RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System |
| title_short | RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System |
| title_sort | rgb indices and canopy height modelling for mapping tidal marsh biomass from a small unmanned aerial system |
| topic | unmanned aircraft biomass coastal wetland RGB drone |
| url | https://www.mdpi.com/2072-4292/13/17/3406 |
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