A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective
Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-ana...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/21/4347 |
| _version_ | 1797511888004710400 |
|---|---|
| author | Rabia Munsaf Khan Bahram Salehi Masoud Mahdianpari Fariba Mohammadimanesh Giorgos Mountrakis Lindi J. Quackenbush |
| author_facet | Rabia Munsaf Khan Bahram Salehi Masoud Mahdianpari Fariba Mohammadimanesh Giorgos Mountrakis Lindi J. Quackenbush |
| author_sort | Rabia Munsaf Khan |
| collection | DOAJ |
| description | Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies. |
| first_indexed | 2024-03-10T05:54:18Z |
| format | Article |
| id | doaj.art-db4bac565d084150926fcdb1fe2d315f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:54:18Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-db4bac565d084150926fcdb1fe2d315f2023-11-22T21:32:08ZengMDPI AGRemote Sensing2072-42922021-10-011321434710.3390/rs13214347A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing PerspectiveRabia Munsaf Khan0Bahram Salehi1Masoud Mahdianpari2Fariba Mohammadimanesh3Giorgos Mountrakis4Lindi J. Quackenbush5Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry (ESF), Syracuse, NY 13210, USADepartment of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry (ESF), Syracuse, NY 13210, USAC-CORE and Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaC-CORE and Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, CanadaDepartment of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry (ESF), Syracuse, NY 13210, USADepartment of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry (ESF), Syracuse, NY 13210, USAAlgae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.https://www.mdpi.com/2072-4292/13/21/4347harmful algal blooms (HABs)meta-analysisphytoplanktonremote sensingwater quality |
| spellingShingle | Rabia Munsaf Khan Bahram Salehi Masoud Mahdianpari Fariba Mohammadimanesh Giorgos Mountrakis Lindi J. Quackenbush A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective Remote Sensing harmful algal blooms (HABs) meta-analysis phytoplankton remote sensing water quality |
| title | A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective |
| title_full | A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective |
| title_fullStr | A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective |
| title_full_unstemmed | A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective |
| title_short | A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective |
| title_sort | meta analysis on harmful algal bloom hab detection and monitoring a remote sensing perspective |
| topic | harmful algal blooms (HABs) meta-analysis phytoplankton remote sensing water quality |
| url | https://www.mdpi.com/2072-4292/13/21/4347 |
| work_keys_str_mv | AT rabiamunsafkhan ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT bahramsalehi ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT masoudmahdianpari ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT faribamohammadimanesh ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT giorgosmountrakis ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT lindijquackenbush ametaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT rabiamunsafkhan metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT bahramsalehi metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT masoudmahdianpari metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT faribamohammadimanesh metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT giorgosmountrakis metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective AT lindijquackenbush metaanalysisonharmfulalgalbloomhabdetectionandmonitoringaremotesensingperspective |