A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices
This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different co...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/1/158 |
| _version_ | 1797497743115026432 |
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| author | Ele Vahtmäe Jonne Kotta Laura Argus Mihkel Kotta Ilmar Kotta Tiit Kutser |
| author_facet | Ele Vahtmäe Jonne Kotta Laura Argus Mihkel Kotta Ilmar Kotta Tiit Kutser |
| author_sort | Ele Vahtmäe |
| collection | DOAJ |
| description | This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R<sup>2</sup>) of production models were very high, estimated at the range of 0.82–0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation. |
| first_indexed | 2024-03-10T03:23:35Z |
| format | Article |
| id | doaj.art-ece5a26cd88d4fdd927c6991178c66f2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:23:35Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ece5a26cd88d4fdd927c6991178c66f22023-11-23T12:14:03ZengMDPI AGRemote Sensing2072-42922021-12-0114115810.3390/rs14010158A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral IndicesEle Vahtmäe0Jonne Kotta1Laura Argus2Mihkel Kotta3Ilmar Kotta4Tiit Kutser5Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaEstonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, EstoniaThis study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R<sup>2</sup>) of production models were very high, estimated at the range of 0.82–0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation.https://www.mdpi.com/2072-4292/14/1/158benthic vegetationprimary productionspectral indicesboosted regression trees modelling |
| spellingShingle | Ele Vahtmäe Jonne Kotta Laura Argus Mihkel Kotta Ilmar Kotta Tiit Kutser A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices Remote Sensing benthic vegetation primary production spectral indices boosted regression trees modelling |
| title | A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices |
| title_full | A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices |
| title_fullStr | A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices |
| title_full_unstemmed | A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices |
| title_short | A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices |
| title_sort | model based assessment of canopy scale primary productivity for the baltic sea benthic vegetation using environmental variables and spectral indices |
| topic | benthic vegetation primary production spectral indices boosted regression trees modelling |
| url | https://www.mdpi.com/2072-4292/14/1/158 |
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