Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

Chust, Guillem; Grande, Maitane; Galparsoro, Ibon; Uriarte, Adolfo; Borja, Ángel

doi:10.1016/j.ecss.2010.07.002

Cited by 95 publications

(55 citation statements)

References 49 publications

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“…The efficiency of bathymetric lidar systems is greatly limited by turbidity, wave action, depth (up to 50-70 m in exceptionally good conditions), steep slopes, and rocky substrate Chust et al, 2010;Jalali et al, 2015). Current geomorphometric applications on bathymetric lidar data are mainly related to the exploration of coastal ecosystems (e.g.…”

Section: Bathymetric Lidarmentioning

confidence: 99%

“…In theory, the overlapping areas should yield very similar values, within their uncertainty and error ranges. However, important inconsistencies (up to 6.5 m) have been reported between depth measurements of the same areas using bathymetric lidar and MBESs (Quadros et al, 2008;Costa et al, 2009;Chust et al, 2010;Shih et al, 2014). This has implications for geomorphometry since terrain attributes will capture and classify these mismatches as features, especially as the differences usually occur locally (Chust et al, 2010).…”

Section: Broadscale Coastal Geomorphometrymentioning

confidence: 99%

“…However, important inconsistencies (up to 6.5 m) have been reported between depth measurements of the same areas using bathymetric lidar and MBESs (Quadros et al, 2008;Costa et al, 2009;Chust et al, 2010;Shih et al, 2014). This has implications for geomorphometry since terrain attributes will capture and classify these mismatches as features, especially as the differences usually occur locally (Chust et al, 2010). Also, coastal environments can be very dynamic; artefacts could appear in the DTM if the multi-source data are not collected at the same time and changes occurred between the data collections.…”

Section: Broadscale Coastal Geomorphometrymentioning

confidence: 99%

See 2 more Smart Citations

A review of marine geomorphometry, the quantitative study of the seafloor

Lecours

Dolan

Micallef

et al. 2016

Hydrol. Earth Syst. Sci.

179

120

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Abstract. Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, fourdimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acousticbased mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry.This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.

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Section: Bathymetric Lidarmentioning

confidence: 99%

Section: Broadscale Coastal Geomorphometrymentioning

confidence: 99%

Section: Broadscale Coastal Geomorphometrymentioning

confidence: 99%

See 1 more Smart Citation

A review of marine geomorphometry, the quantitative study of the seafloor

Lecours

Dolan

Micallef

et al. 2016

Hydrol. Earth Syst. Sci.

179

120

View full text Add to dashboard Cite

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“…Costa et al [3] found that in topographical charting, the LiDAR technique was comparable to the ship-based multibeam sonar technique, while the reflectance intensity parameter did not improve the results of habitat mapping in coral reef ecosystems. Chust et al [4] also found that reflectance was not particularly useful for classification purposes when analyzing data measured from the Oka estuary, Bay of Biscay, where waters are moderately turbid. They found that combined with multispectral (three visible bands plus NIR) data, the LiDAR-derived DEM gave good classification accuracy when used for discrimination between 22 supralittoral, intertidal and subtidal habitats.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

et al. 2015

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A data-driven method for describing the benthic cover type based on full-waveform bathymetric LiDAR data analysis is presented. The waveform of the bathymetric LiDAR return pulse is first modeled as a sum of three functions: a Gaussian pulse representing the surface return, a function modeling the backscatter and another Gaussian pulse modeling the return from the bottom surface. Two sets of variables are formed: one containing features describing the bottom return and the other describing various conditions, such as water quality and the depth of the seabed. Regression analysis is used to eliminate the effect of the condition variables on the features, after which the features are mapped onto a cell lattice using a self-organizing map (SOM). The cells of the SOM are grouped into seven clusters using the neighborhood distance matrix method. The clustering result is evaluated using the seabed substrate map based on sonar measurements, as well as delineation of photic zones in the study area. High correspondence between the clusters and the substrate type/photic zone has been obtained indicating that the proposed clustering method adequately describes the benthic cover in the study area. The bottom return pulse waveforms corresponding to the clusters and a cluster map of the study area are also presented. The method can be used for clustering full waveform bathymetric LiDAR data acquired from large areas to discover the structure of benthic cover types and to focus the field studies accordingly.Remote Sens. 2015, 7 13391

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“…LIDAR systems are fast, accurate and appropriate alternative solution for difficult shallow aquatic areas [10]. Some of these LIDAR systems can reach 70 m depths and 20 cm vertical accuracy [11]. Despite of the accuracy of these systems they are limited in coverage compared to satellite images and high costing of operation [12].…”

mentioning

confidence: 99%

Bathymetry Determination from High Resolution Satellite Imagery Using Ensemble Learning Algorithms in Shallow Lakes: Case Study El-Burullus Lake

Mohamed¹,

Negm²,

Zahran³

et al. 2016

IJESD

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Abstract-Determination of bathymetric information is key element for near off shore activities and hydrological studies such as coastal engineering applications, sedimentary processes and hydrographic surveying. Remotely sensed imagery has provided a wide coverage, low cost and time-effective solution for bathymetric measurements. In this paper a methodology is introduced using Ensemble Learning (EL) fitting algorithm of Least Squares Boosting (LSB) for bathymetric maps calculation in shallow lakes from high resolution satellite images and water depth measurement samples using Eco-sounder. This methodology considered the cleverest sequential ensemble that assigns higher weights as Boosting for those training sets that are difficult to fit. The LSB ensemble using reflectance of Green and Red bands and their logarithms from Spot-4 satellite image was compared with two conventional methods; the Principal Component Analysis (PCA) and Generalized Linear Model (GLM). The retrieved bathymetric information from the three methods was evaluated using Echo Sounder data. The LSB fitting ensemble resulted in RMSE of 0.15m where the PCA and GLM yielded RMSE of 0.19m and 0.18m respectively over shallow water depths less than 2m. The application of the proposed approach demonstrated better performance and accuracy compared with the conventional methods.Index Terms-Bathymetry, PCA, GLM, least square boosting. I. INTRODUCTIONAccurate bathymetric information is so important for costal science applications, shipping navigations and environmental studies of marine areas [1]. Mapping underwater features as rocks, sandy areas, sediments accumulation and coral reefs needs up to date water depths information [2], [3]. Water depths data are essential also for accomplishing sustainable management [4], bathymetric information constitutes a key element hydrological modeling, flooding estimation and degrading or sediments removing [5], [6]. Manuscript received November 25, 2014; revised April 27, 2015. The work was supported by Mission Department, Egyptian Ministry of Higher Education (MoHE), Egypt-Japan University of Science and Technology (E-JUST) and partially supported by JSPS "Core-to-Core Program, B. Asia-Africa Science Platforms".H. Mohamed and Abdelazim Negm are with the Environmental Engineering Department, School of Energy and Environmental Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt (e-mail: hassan.mohamed@ejust.edu.eg, negm@ejust.edu.eg).M. Zahran is with the Department of Geomatics Engineering, Faculty of Engineering at Shoubra, Benha University, Egypt (e-mail: mohamed.zahran01@feng.bu.edu.eg).O. Saavedra is with the Department of Civil Engineering, Tokyo Institute of Technology, Oookayama, Meguro, Tokyo, Japan. He is also with E-JUST, Egypt (e-mail: saavedra.o.aa@m.titech.ac.jp).Sonar remains the primary method for obtaining discreet water depth measurements with high accuracy [7]. Single beam sonar on survey vessel can acquire single point depths along sparsely surveying scan lines up to 50...

show abstract

Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

Cited by 95 publications

References 49 publications

A review of marine geomorphometry, the quantitative study of the seafloor

A review of marine geomorphometry, the quantitative study of the seafloor

Data-Driven Approach to Benthic Cover Type Classification Using Bathymetric LiDAR Waveform Analysis

Bathymetry Determination from High Resolution Satellite Imagery Using Ensemble Learning Algorithms in Shallow Lakes: Case Study El-Burullus Lake

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