Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods
Abstract Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Remote Sensing in Ecology and Conservation |
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| Online Access: | https://doi.org/10.1002/rse2.401 |
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| _version_ | 1850098429578969088 |
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| author | Amy Stone Sharyn Hickey Ben Radford Mary Wakeford |
| author_facet | Amy Stone Sharyn Hickey Ben Radford Mary Wakeford |
| author_sort | Amy Stone |
| collection | DOAJ |
| description | Abstract Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates. |
| format | Article |
| id | doaj-art-d6d0c5d6c37e4d55bfe7e6464386d2d4 |
| institution | DOAJ |
| issn | 2056-3485 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj-art-d6d0c5d6c37e4d55bfe7e6464386d2d42025-08-20T02:40:43ZengWileyRemote Sensing in Ecology and Conservation2056-34852025-02-01111203910.1002/rse2.401Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methodsAmy Stone0Sharyn Hickey1Ben Radford2Mary Wakeford3Centre for Water and Spatial Sciences The School of Agriculture and Environment, University of Western Australia Perth Western Australia 6009 AustraliaCentre for Water and Spatial Sciences The School of Agriculture and Environment, The Oceans Institute, University of Western Australia Perth Western Australia 6009 AustraliaCentre for Water and Spatial Sciences The School of Agriculture and Environment, The Oceans Institute, University of Western Australia Perth Western Australia 6009 AustraliaAustralian Institute of Marine Science (AIMS) Crawley Western Australia 6009 AustraliaAbstract Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates.https://doi.org/10.1002/rse2.401Dronesemergent coral reefsobject basedpixel basedsegmentationstructure from motion |
| spellingShingle | Amy Stone Sharyn Hickey Ben Radford Mary Wakeford Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods Remote Sensing in Ecology and Conservation Drones emergent coral reefs object based pixel based segmentation structure from motion |
| title | Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods |
| title_full | Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods |
| title_fullStr | Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods |
| title_full_unstemmed | Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods |
| title_short | Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods |
| title_sort | mapping emergent coral reefs a comparison of pixel and object based methods |
| topic | Drones emergent coral reefs object based pixel based segmentation structure from motion |
| url | https://doi.org/10.1002/rse2.401 |
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