An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data
Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3875 |
| _version_ | 1827680617130622976 |
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| author | Chunyi Zhong Peng Chen Delu Pan |
| author_facet | Chunyi Zhong Peng Chen Delu Pan |
| author_sort | Chunyi Zhong |
| collection | DOAJ |
| description | Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-<i>a</i> profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas. |
| first_indexed | 2024-03-10T06:53:08Z |
| format | Article |
| id | doaj.art-856300bd1bc9488584a28842858b2628 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:08Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-856300bd1bc9488584a28842858b26282023-11-22T16:42:09ZengMDPI AGRemote Sensing2072-42922021-09-011319387510.3390/rs13193875An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar DataChunyi Zhong0Peng Chen1Delu Pan2State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou 310012, ChinaPhytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-<i>a</i> profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas.https://www.mdpi.com/2072-4292/13/19/3875lidarsubsurface phytoplankton layerautomatic threshold determinationrobust estimation |
| spellingShingle | Chunyi Zhong Peng Chen Delu Pan An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data Remote Sensing lidar subsurface phytoplankton layer automatic threshold determination robust estimation |
| title | An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data |
| title_full | An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data |
| title_fullStr | An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data |
| title_full_unstemmed | An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data |
| title_short | An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data |
| title_sort | improved adaptive subsurface phytoplankton layer detection method for ocean lidar data |
| topic | lidar subsurface phytoplankton layer automatic threshold determination robust estimation |
| url | https://www.mdpi.com/2072-4292/13/19/3875 |
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