Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation
Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those da...
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/16/4077 |
| _version_ | 1797583267556229120 |
|---|---|
| author | Kai L. Kopecky Gaia Pavoni Erica Nocerino Andrew J. Brooks Massimiliano Corsini Fabio Menna Jordan P. Gallagher Alessandro Capra Cristina Castagnetti Paolo Rossi Armin Gruen Fabian Neyer Alessandro Muntoni Federico Ponchio Paolo Cignoni Matthias Troyer Sally J. Holbrook Russell J. Schmitt |
| author_facet | Kai L. Kopecky Gaia Pavoni Erica Nocerino Andrew J. Brooks Massimiliano Corsini Fabio Menna Jordan P. Gallagher Alessandro Capra Cristina Castagnetti Paolo Rossi Armin Gruen Fabian Neyer Alessandro Muntoni Federico Ponchio Paolo Cignoni Matthias Troyer Sally J. Holbrook Russell J. Schmitt |
| author_sort | Kai L. Kopecky |
| collection | DOAJ |
| description | Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts. |
| first_indexed | 2024-03-10T23:36:27Z |
| format | Article |
| id | doaj.art-3dc028395881494696afe629a08a765a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:36:27Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3dc028395881494696afe629a08a765a2023-11-19T02:54:13ZengMDPI AGRemote Sensing2072-42922023-08-011516407710.3390/rs15164077Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image SegmentationKai L. Kopecky0Gaia Pavoni1Erica Nocerino2Andrew J. Brooks3Massimiliano Corsini4Fabio Menna5Jordan P. Gallagher6Alessandro Capra7Cristina Castagnetti8Paolo Rossi9Armin Gruen10Fabian Neyer11Alessandro Muntoni12Federico Ponchio13Paolo Cignoni14Matthias Troyer15Sally J. Holbrook16Russell J. Schmitt17Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USAVisual Computing Lab ISTI-CNR, 56124 Pisa, ItalyDipartimento di Scienze Umanistiche e Sociali, University of Sassari, 07100 Sassari, ItalyCoastal Research Center, Marine Science Institute, University of California, Santa Barbara, CA 93106, USAVisual Computing Lab ISTI-CNR, 56124 Pisa, Italy3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, ItalyDepartment of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USADepartment of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, ItalyDepartment of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, ItalyDepartment of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, ItalyInstitute of Theoretical Physics, ETH Zurich, 8093 Zurich, SwitzerlandTerradata AG, 8152 Glattpark, SwitzerlandVisual Computing Lab ISTI-CNR, 56124 Pisa, ItalyVisual Computing Lab ISTI-CNR, 56124 Pisa, ItalyVisual Computing Lab ISTI-CNR, 56124 Pisa, ItalyMicrosoft Quantum, Redmond, WA 98052, USADepartment of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USADepartment of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USADetecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.https://www.mdpi.com/2072-4292/15/16/4077coral bleachingcoral reef monitoringunderwater photogrammetrychange detectionartificial intelligenceimage segmentation |
| spellingShingle | Kai L. Kopecky Gaia Pavoni Erica Nocerino Andrew J. Brooks Massimiliano Corsini Fabio Menna Jordan P. Gallagher Alessandro Capra Cristina Castagnetti Paolo Rossi Armin Gruen Fabian Neyer Alessandro Muntoni Federico Ponchio Paolo Cignoni Matthias Troyer Sally J. Holbrook Russell J. Schmitt Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation Remote Sensing coral bleaching coral reef monitoring underwater photogrammetry change detection artificial intelligence image segmentation |
| title | Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation |
| title_full | Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation |
| title_fullStr | Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation |
| title_full_unstemmed | Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation |
| title_short | Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation |
| title_sort | quantifying the loss of coral from a bleaching event using underwater photogrammetry and ai assisted image segmentation |
| topic | coral bleaching coral reef monitoring underwater photogrammetry change detection artificial intelligence image segmentation |
| url | https://www.mdpi.com/2072-4292/15/16/4077 |
| work_keys_str_mv | AT kailkopecky quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT gaiapavoni quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT ericanocerino quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT andrewjbrooks quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT massimilianocorsini quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT fabiomenna quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT jordanpgallagher quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT alessandrocapra quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT cristinacastagnetti quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT paolorossi quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT armingruen quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT fabianneyer quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT alessandromuntoni quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT federicoponchio quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT paolocignoni quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT matthiastroyer quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT sallyjholbrook quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation AT russelljschmitt quantifyingthelossofcoralfromableachingeventusingunderwaterphotogrammetryandaiassistedimagesegmentation |