Shallow seabed mapping and classification using waveform analysis and bathymetry from SHOALS lidar data

Cottin, Antoine; Forbes, Donald L.; Long, Bernard

doi:10.5589/m09-036

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…These areas are highly dynamic through time and space, necessitating high-quality fine-resolution spatial data over a relatively broad extent in order to capture pattern at relevant spatial scales. The primary source of spatial data for investigations of coastal landscape structure is aerial imagery sourced from aircraft or satellite-based remote sensing (Dekker et al, 2006;McKenzie et al, 2001), though other forms of remote sensing, such as bathymetric lidar (Cottin et al, 2009) and sonar (Barrell and Grant, 2013) can also provide relevant data. Aerial methods provide synoptic and continuous data while minimizing the time and labour required by alternative methods (i.e.…”

Section: Introductionmentioning

confidence: 99%

High-resolution, low-altitude aerial photography in physical geography

Barrell

Grant

2015

Progress in Physical Geography: Earth and Environment

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Intertidal landscapes are highly complex and dynamic habitats that exhibit variability over a range of spatial and temporal scales. The spatial arrangement of structure-forming biogenic features such as seagrasses and bivalves influences ecosystem function and the provision of important ecosystem services, though quantification and monitoring of intertidal landscape structure has been hindered by challenges collecting spatial data in the coastal zone. In this study, an intertidal landscape mosaic of eelgrass (Zostera marina) and blue mussels (Mytilus edulis) was observed using low-altitude aerial photography from a balloon-mounted digital camera platform. Imagery representing seagrass-bivalve landscape structure was classified and analysed using multiple metrics of landscape composition and configuration at the patch scale and the landscape scale. Patch-scale imagery was compared to a previously collected dataset in order to track temporal changes in seagrass patch metrics over a 26-month period. Seagrass and bivalve patches exhibited distinct spatial patterning at different spatial scales. At the patch scale, the change in seagrass metrics was consistent with patch border expansion at the expense of patch density and integrity. These methods demonstrate a novel approach for collecting high-resolution spatial data that could also be valuable to physical geographers dealing with similar fine-scale landscapes. The application of spatial metrics at multiple spatial scales quantified elements of the configuration and composition of a seagrass-bivalve habitat mosaic and allowed for the tracking of patch metrics through time to depict landscape change. Continued development of landscape metrics within intertidal habitats will increase understanding of the ecological function of these areas with benefits to management and monitoring of ecosystem health.

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Section: Introductionmentioning

confidence: 99%

High-resolution, low-altitude aerial photography in physical geography

Barrell

Grant

2015

Progress in Physical Geography: Earth and Environment

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“…Cottin et al proposed a method to classify shallow seabed textures and algae coverage types by extracting waveform parameters of bottom returns collected by the bathymetric LiDAR of scanning hydrographic operational airborne LiDAR survey (SHOALS). The overall accuracy of this classification was up to 67% [ 3 ]. Narayanan et al input waveform parameters of the Optech’s SHOALS-3000 hydrographic mapping system to a decision tree and rotation forest algorithms for classification, and the average overall accuracy was 91% [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea

Wang

Yang

et al. 2018

Sensors

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Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

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“…After removing influences from other factors, the spot reflectance could be derived to provide further information for habitat mapping (Chust et al 2010;Micallef et al 2012). Besides reflectance, other waveform features such as skewness, kurtosis, pulse width and pulse area, could also be useful for characterizing the substrate type (Cottin et al 2009;Collin et al 2011Collin et al , 2012Tulldahl and Wikström 2012).…”

Section: Introductionmentioning

confidence: 99%

Bathymetric LiDAR Green Channel Derived Reflectance: An Experiment from the Dongsha 2010 Mission

Lin¹,

Shih²,

Chen³

et al. 2016

Terr. Atmos. Ocean. Sci.

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Bathymetric LiDAR utilizes a green laser capable of penetrating water and surveying seafloor topography. The intensity of the echo identified as the seafloor carries information about the substrate type. However, besides the reflectance characteristics of the substrate, there are also other influencing factors, including those from the LiDAR system and the environment. The data collected in the 2010 Dongsha atoll bathymetric LiDAR mission is processed and analyzed in this study. The corrections for environmental factors, mainly contributed from the water column that is presented as the inherent optical parameters of water are retrieved from the recorded green laser waveform. Those from the system, such as deviations between the signal receptors or between laser beam angles to each interface, are eliminated with data from IMU (Inertial Measurement Unit), scanner controlling mechanisms, etc. The resulting reflectance from each flight line is compared in the overlap area. The reflectance of the west side strip is subtracted from that of the east side strip. The reflectance is scaled between zero and one. While the mean of the differences is -0.0037, the standard deviation is 0.0436 for the flight line with a flight height of 400 m. The mean and standard deviation are -0.0058 and 0.0272, respectively, with flight height of 300 m. When interpolating the reflectance from the 300 m flight altitude dataset into a surface after subtracting the point measurement from the 400 m flight altitude, the mean and standard deviations are -0.0215 and 0.0382, respectively. This indicates that the consistency among flight lines of the same flight altitude is higher than those from different flight altitudes. A WorldView-2 (WV-2) image is compared with the LiDAR reflectance, and after atmospheric correction, the green band reflectance from WV-2 showed high similarities between the two image types. However, in the deep water region the one derived from LiDAR has much more information content.

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Shallow seabed mapping and classification using waveform analysis and bathymetry from SHOALS lidar data

Cited by 13 publications

References 12 publications

High-resolution, low-altitude aerial photography in physical geography

High-resolution, low-altitude aerial photography in physical geography

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea

Bathymetric LiDAR Green Channel Derived Reflectance: An Experiment from the Dongsha 2010 Mission

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