Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors
Seawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and location...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2104 |
| _version_ | 1797532400343842816 |
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| author | Tapio Suominen Jan Westerholm Risto Kalliola Jenni Attila |
| author_facet | Tapio Suominen Jan Westerholm Risto Kalliola Jenni Attila |
| author_sort | Tapio Suominen |
| collection | DOAJ |
| description | Seawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and locations. We introduce a workflow using remote sensing to identify surface waters undergoing similar dynamics. The method, called ocean surface dynamics partitioning, classifies pixels based on their temporal change patterns instead of their properties at successive time snapshots. We apply an efficient parallel computing method to calculate Dynamic Time Warping (DTW) time series distances of large datasets of Earth Observation MERIS-instrument reflectance data R<sub>rs</sub>(510 nm) and R<sub>rs</sub>(620 nm), and produce a matrix of time series distances between 12,252 locations/time series in the Baltic Sea, for both wavelengths. We define cluster prototypes by hierarchical clustering of distance matrices and use them as initial prototypes for an iterative process of partitional clustering in order to identify areas that have similar reflectance dynamics. Lastly, we compute distances from the time series of the reflectance data to selected physical factors (wind, precipitation, and changes in sea surface temperature) obtained from Copernicus data archives. The workflow is reproducible and capable of managing large datasets in reasonable computation times and identifying areas of distinctive dynamics. The results show spatially coherent and logical areas without <i>a priori</i> information about the locations of the satellite image time series. The alignments of the reflectance time series vs. the observational time series of the physical environment clarify the causalities behind the cluster formation. We conclude that following the changes in an aquatic realm by biogeochemical observations at certain temporal intervals alone is not sufficient to identify environmental shifts. We foresee that the changes in dynamics are a sensitive measure of environmental threats and therefore they will be important to follow in the future. |
| first_indexed | 2024-03-10T10:58:37Z |
| format | Article |
| id | doaj.art-7bc768cc022241f18359e363167b3782 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:58:37Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7bc768cc022241f18359e363167b37822023-11-21T21:39:45ZengMDPI AGRemote Sensing2072-42922021-05-011311210410.3390/rs13112104Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical FactorsTapio Suominen0Jan Westerholm1Risto Kalliola2Jenni Attila3Department of Geography and Geology, University of Turku, FI-20014 Turku, FinlandFaculty of Science and Engineering, Åbo Akademi University, FI-20500 Turku, FinlandDepartment of Geography and Geology, University of Turku, FI-20014 Turku, FinlandData and Information Centre, Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, FinlandSeawaters exhibit various types of cyclic and trend-like temporal alterations in their biological, physical, and chemical processes. Surface water dynamics may vary, for instance, when the timings, durations, or amplitudes of seasonal developments of water properties alter between years and locations. We introduce a workflow using remote sensing to identify surface waters undergoing similar dynamics. The method, called ocean surface dynamics partitioning, classifies pixels based on their temporal change patterns instead of their properties at successive time snapshots. We apply an efficient parallel computing method to calculate Dynamic Time Warping (DTW) time series distances of large datasets of Earth Observation MERIS-instrument reflectance data R<sub>rs</sub>(510 nm) and R<sub>rs</sub>(620 nm), and produce a matrix of time series distances between 12,252 locations/time series in the Baltic Sea, for both wavelengths. We define cluster prototypes by hierarchical clustering of distance matrices and use them as initial prototypes for an iterative process of partitional clustering in order to identify areas that have similar reflectance dynamics. Lastly, we compute distances from the time series of the reflectance data to selected physical factors (wind, precipitation, and changes in sea surface temperature) obtained from Copernicus data archives. The workflow is reproducible and capable of managing large datasets in reasonable computation times and identifying areas of distinctive dynamics. The results show spatially coherent and logical areas without <i>a priori</i> information about the locations of the satellite image time series. The alignments of the reflectance time series vs. the observational time series of the physical environment clarify the causalities behind the cluster formation. We conclude that following the changes in an aquatic realm by biogeochemical observations at certain temporal intervals alone is not sufficient to identify environmental shifts. We foresee that the changes in dynamics are a sensitive measure of environmental threats and therefore they will be important to follow in the future.https://www.mdpi.com/2072-4292/13/11/2104MERISEarth Observationtime seriesdynamicsdynamic time warpingparallel computing |
| spellingShingle | Tapio Suominen Jan Westerholm Risto Kalliola Jenni Attila Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors Remote Sensing MERIS Earth Observation time series dynamics dynamic time warping parallel computing |
| title | Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors |
| title_full | Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors |
| title_fullStr | Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors |
| title_full_unstemmed | Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors |
| title_short | Partition of Marine Environment Dynamics According to Remote Sensing Reflectance and Relations of Dynamics to Physical Factors |
| title_sort | partition of marine environment dynamics according to remote sensing reflectance and relations of dynamics to physical factors |
| topic | MERIS Earth Observation time series dynamics dynamic time warping parallel computing |
| url | https://www.mdpi.com/2072-4292/13/11/2104 |
| work_keys_str_mv | AT tapiosuominen partitionofmarineenvironmentdynamicsaccordingtoremotesensingreflectanceandrelationsofdynamicstophysicalfactors AT janwesterholm partitionofmarineenvironmentdynamicsaccordingtoremotesensingreflectanceandrelationsofdynamicstophysicalfactors AT ristokalliola partitionofmarineenvironmentdynamicsaccordingtoremotesensingreflectanceandrelationsofdynamicstophysicalfactors AT jenniattila partitionofmarineenvironmentdynamicsaccordingtoremotesensingreflectanceandrelationsofdynamicstophysicalfactors |