Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery

Collin, Antoine; Ramambason, Camille; Pastol, Yves; Casella, Elisa; Rovere, Alessio; Thiault, Lauric; Espiau, Benoît; Siu, Gilles; Lerouvreur, Franck; Nakamura, Nao; Hench, James L.; Schmitt, Russell J.; Holbrook, Sally J.; Troyer, Matthias; Davies, Neil

doi:10.1080/01431161.2018.1500072

Cited by 69 publications

(47 citation statements)

References 28 publications

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“…The quantitative analysis of agreement with the baseline map showed that both methods, despite being very different in terms of input data and methodology, gave comparable results regarding map agreement and error. The map agreement of 67% (OBIA) and 69% (BTM) is acceptable, although lower than what has been reported in some recent studies that mapped reef [28][29][30]. These differences might be related to various factors, e.g., shallow warm-water coral reefs can be mapped with satellite or drone-based optical sensors, which give more spectral information than a one-band SSS backscatter dataset.…”

Section: Discussioncontrasting

confidence: 52%

Reef Mapping Using Different Seabed Automatic Classification Tools

et al. 2020

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There is a great demand to develop new acoustic techniques to efficiently map the seabed and automate the interpretation of acoustic, sedimentological, and imaging data sets, eliminating subjectivity. Here, we evaluate the potential, limitations and complementariety of distinct supervised and automatic classification techniques in the mapping of reefs by comparing these results with a reference map. The study was carried out in the Abrolhos Continental Shelf (Eastern Brazilian Continental Margin) using a multibeam echosounder and side scan sonar (SSS) dataset. Two automatic supervised techniques were applied. A reference map was derived by detailed manual interpretation carried out by three experts. The two supervised classification techniques were: benthic terrain modeler (BTM), a morphometric classification with focus on spatial analyses of the bathymetric grid derivatives, and object-based image analysis (OBIA), a segmentation applied to the backscatter data from the SSS mosaic. Both automatic techniques obtained similar values of reef coverage area, but overestimated the reef area when compared with the reference map. The agreement between BTM and OBIA results and the reference map was 69% and 67%, respectively. Disagreement was mainly due to quantity of reef (both methods over-estimated reef), while the disagreement in spatial allocation was relatively low, it indicates that both methods are reasonable representation of the spatial patterns of reef. Efficient mapping of reef in the wider area of the Abrolhos Continental Shelf will be best achieved by a further development of automatic methods tested against reference maps obained from representative areas of the seabed. By combining the results of the two automatic methods, it was possible to create an ensemble map, which achieved better agreement with the reference dataset.

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Section: Discussioncontrasting

confidence: 52%

Reef Mapping Using Different Seabed Automatic Classification Tools

et al. 2020

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“…Recent advances in spaceborne and airborne imageries have been successful for improving the habitat discrimination within the complex reefscape [7,8]. The increase in the spatial and spectral resolutions provided by the satellite WorldView-3 has augmented the classification of 10 coral reef classes at 0.3 m spatial resolution based on the 16-band superspectral dataset [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, only five visible spectral bands were appropriate to provide benthic information given the strong light absorption by water from infrared [10]. The green wavelength of the airborne bathymetric Light Detection And Ranging (LiDAR) helped separate the five reef states at 0.5 m point spacing, only based on LiDAR surface and intensity predictors, trained by red-green-blue (RGB) imagery that was acquired from an unmanned aerial vehicle (UAV), called a drone [7]. The emission of electromagnetic radiation (i.e., active remote sensing) in the form of a laser is necessary to penetrate seawater in order been efficient to help create RGB orthomosaics of honeycomb worm reefs [12] and even digital surface models (DSMs) of underwater reef colonies, using a photogrammetric approach [13].…”

Section: Introductionmentioning

confidence: 99%

Improving Intertidal Reef Mapping Using UAV Surface, Red Edge, and Near-Infrared Data

Collin

Dubois

James

et al. 2019

Drones

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Coastal living reefs provide considerable services from tropical to temperate systems. Threatened by global ocean-climate and local anthropogenic changes, reefs require spatially explicit management at the submeter scale, where socioecological processes occur. Drone surveys have adequately addressed these requirements with red-green-blue (RGB) orthomosaics and digital surface models (DSMs). The use of ancillary spectral bands has the potential to increase the mapping of all reefscapes that emerge during low tide. This research investigates the contribution of the drone-based red edge (RE), near-infrared (NIR), and DSM into the classification accuracy of five main habitats of the largest intertidal biogenic reefs in Europe, built by the honeycomb worm Sabellaria alveolata. Based on photoquadrats and the maximum likelihood algorithm, overall, producer’s and user’s accuracies were distinctly augmented. When isolated, the DSM provided the highest gain percentage (3.42%), followed by the NIR (2.58%), and RE (2.02%). When joined, the combination of the DSM with both RE and NIR was the best contributor (4.98%), followed by the DSM with RE (4.80%), DSM with NIR (3.74%), and RE with NIR (3.22%). At the class scale, all datasets increasingly advantaged sand, gravel, reef, mud and water. The rather low effect of the DSM with NIR (3.74%) was assumed to be linked with a statistical noise originated from redundant information in the intertidal area.

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“…Various mapping technologies have been employed for mapping both shallow and deep areas of the ocean. Passive optical and active blue-green light detection and ranging (LiDAR) remote-sensing systems are used to map shallow benthic habitats [5,6]. Acoustic mapping systems, such as multibeam echo sounding (MBES) instruments, acquire both bathymetry (depth) and backscatter (intensity) data in wide swaths at deeper depths that can be used to map seafloor substrate characteristics [7,8].…”

Section: Introductionmentioning

confidence: 99%

Reef Rover: A Low-Cost Small Autonomous Unmanned Surface Vehicle (USV) for Mapping and Monitoring Coral Reefs

Raber

Schill

2019

Drones

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In the effort to design a more repeatable and consistent platform to collect data for Structure from Motion (SfM) monitoring of coral reefs and other benthic habitats, we explore the use of recent advances in open source Global Positioning System (GPS)-guided drone technology to design and test a low-cost and transportable small unmanned surface vehicle (sUSV). The vehicle operates using Ardupilot open source software and can be used by local scientists and marine managers to map and monitor marine environments in shallow areas (<20 m) with commensurate visibility. The imaging system uses two Sony a6300 mirrorless cameras to collect stereo photos that can be later processed using photogrammetry software to create underwater high-resolution orthophoto mosaics and digital surface models. The propulsion system consists of two small brushless motors powered by lithium batteries that follow pre-programmed survey transects and are operated by a GPS-guided autopilot control board. Results from our project suggest the sUSV provides a repeatable, viable, and low-cost (<$3000 USD) solution for acquiring images of benthic environments on a frequent basis from directly below the water surface. These images can be used to create SfM models that provide very detailed images and measurements that can be used to monitor changes in biodiversity, reef erosion/accretion, and assessing health conditions.

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Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery

Cited by 69 publications

References 28 publications

Reef Mapping Using Different Seabed Automatic Classification Tools

Reef Mapping Using Different Seabed Automatic Classification Tools

Improving Intertidal Reef Mapping Using UAV Surface, Red Edge, and Near-Infrared Data

Reef Rover: A Low-Cost Small Autonomous Unmanned Surface Vehicle (USV) for Mapping and Monitoring Coral Reefs

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