Statistical classification methodology of SHOALS 3000 backscatter to mapping coastal benthic habitats

Collin, Antoine; Cottin, Antoine; Long, Bernard; Archambault, Philippe; Kuus, P.; Clarke, J. H.; Sohn, Gunho; Miller, Joyce

doi:10.1109/igarss.2007.4423520

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The reference water depth data at Ganquan Island were acquired on January 9, 2013 using a scanned hydrographic operational airborne LiDAR system (SHOALS-3000, Canada). This ALB system uses a green laser (532 nm) with 3000 pulses per second (3 kHz), and it has a nominal bathymetry accuracy of 0.3 m [46], [47], [48]. The reference data at Lingyang Reef were obtained on July 16, 2018 using the Mapper 5000 ALB system developed by the Shanghai Institute of Optics and Fine Mechanics.…”

Section: Alb Reference Datamentioning

confidence: 99%

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

Zhang

Chen

Liu

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate bathymetric maps are essential to understand marine and coastal ecosystems. With the development of satellite and sensor technology, satellite-derived bathymetry (SDB) has been widely used to measure the depth of nearshore waters. Employment of physics-based methods requires a series of optical parameters of the water column and seafloor, which limits the application of these methods to shallow-water bathymetry. Due to convenience, low costs, and high efficiency, empirical methods based on in-situ measurements and satellite imagery are increasingly used for nearshore bathymetry. These measurements are required to calibrate empirical models, so that reasonable accuracy can be achieved. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System, provides a novel opportunity for nearshore bathymetry. Using the new measurement strategy of photon counting, ICESat-2 can provide accurate bathymetric points from spaceborne observations, which can be used in place of in-situ water depth data. In this study, ICESat-2 bathymetric points and multispectral images were used to train four typical models and produce bathymetric maps for Shanhu Island, Ganquan Island, and Lingyang Reef in the Xisha Islands of China. We evaluated the bathymetric results by comparing them with reference depth data from airborne light detection and ranging. All models had a satisfactory accuracy, as well as multimodel and multisource image consistency. With the ICESat-2 bathymetric points, SDB is no longer limited by in-situ measurements. Hence, this approach could be extended to a larger scale to obtain nearshore bathymetric maps of coastal areas, surrounding islands, and reefs using free and open-access satellite data.

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Section: Alb Reference Datamentioning

confidence: 99%

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

Zhang

Chen

Liu

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The reference ALB data for Ganquan Island (orange solid circles in Figure 1b) were acquired on 15:38 of local time, 9 January 2013, using a scanned hydrographic operational airborne LiDAR system (SHOALS-3000, Vaughan, ON, Canada). Using a green laser light (532 nm) at 3000 pulses per second, this ALB system has a nominal bathymetric accuracy of 0.3 m [44,45].…”

Section: Spaceborne Images and Reference Alb Datamentioning

confidence: 99%

Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea

Liu

Chen

et al. 2022

Remote Sensing

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Accurate bathymetric and topographical information is crucial for coastal and marine applications. In the past decades, owing to its low cost and high efficiency, satellite-derived bathymetry has been widely used to estimate the depth of shallow water in coastal areas. However, insufficient spectral bands and availability of in situ water depths limit the application of satellite-derived bathymetry. Currently, the investigation about the bathymetric potential of hyperspectral imaging is relatively insufficient based on datasets of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). In this study, Zhuhai-1 hyperspectral images and ICESat-2 datasets were utilized to perform nearshore bathymetry and explore the bathymetric capability by selecting different bands based on classical empirical models (the band ratio model and the linear band model). Furthermore, experimental results achieved at the South China Sea indicate that the combination of blue (2 and 3 band) and green (9 band) bands and the combination of red (10 and 12 band) and near-infrared (29 band) bands are most suitable to achieve nearshore bathymetry. Correspondingly, the highest accuracy of bathymetry reached root mean square error values of 0.98 m and 1.19 m for different band combinations evaluated through bathymetric results of reference water depth. The bathymetric accuracy of Zhuhai-1 image is similar with that of Sentinel-2 when employing the blue and green bands. The combination of red and near-infrared bands has a higher bathymetric accuracy for Zhuhai-1 image than that for Sentinel-2 image.

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“…Valores tipicos de profundidad que aseguran una buena precision en aguas limpias es de 50 metros. Como regla general, se puede esperar el exito en operaciones a profundidades entre 2 y 3 veces la profundidad de Secchi, medida de claridad del agua mediante un ojo humano, de un patron estandar (disco bianco y negro), situado en el agua desde un barco [15], Asi, ALB permite obtener Modelos Digitales de profundidad o Batimetricos (Digital Depth Models, DDM) con un elevado grado de precision [16].…”

Section: Introduction Eunclassified

Sea Bottom Classification by Means of Bathymetric LIDAR Data

Velasco

Molina

Martínez

et al. 2014

IEEE Latin Am. Trans.

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Abstract-Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine surveys.

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Statistical classification methodology of SHOALS 3000 backscatter to mapping coastal benthic habitats

Cited by 4 publications

References 7 publications

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

Investigating the Shallow-Water Bathymetric Capability of Zhuhai-1 Spaceborne Hyperspectral Images Based on ICESat-2 Data and Empirical Approaches: A Case Study in the South China Sea

Sea Bottom Classification by Means of Bathymetric LIDAR Data

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