CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation

Feygels, Viktor; Park, Joong Yong; Wozencraft, Jennifer M.; Aitken, Jennifer; Macon, Christopher L.; Mathur, Abhinav; Payment, Andy; Ramnath, Vinod

doi:10.1117/12.2017935

Cited by 17 publications

(8 citation statements)

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“…The next step in the process was the incidence angle correction. This correction is extremely important, since, unlike other bathymetric lidar systems, such as the Optech CZMIL (Feygels et al 2013) and SHOALS (Collin et al 2008), the EAARL-B does not attempt to maintain a constant off-nadir transmit pulse angle but instead scans back and forth across the field of view, passing nearly through nadir. This created a pronounced reduction in intensity towards the outer edges of the swath.…”

Section: Signal Processingmentioning

confidence: 99%

Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms

Wilson

Parrish

Battista

et al. 2019

Estuaries and Coasts

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Topobathymetric lidar is becoming an increasingly valuable tool for benthic habitat mapping, enabling safe, efficient data acquisition over coral reefs and other fragile ecosystems. In 2014, a novel topobathymetric lidar system, the Experimental Advanced Airborne Research Lidar-B (EAARL-B), was used to acquire data in priority habitat areas in the U.S. Virgin Islands (USVI), spanning the 0–44-m depth range. In this study, new algorithms and procedures were developed for generating seafloor relative reflectance, along with a suite of shape-based waveform features from EAARL-B. Waveform features were then correlated with percent cover of coral morphologies, domed and branched, and total cover of hard and soft corals. Results show that the EAARL-B can be used to produce useful seafloor relative reflectance mosaics and also that the additional waveform shape-based features contain additional information that may benefit habitat classification—specifically, to aid in distinguishing among hard corals and their coral morphologies, domed and branched. Knowing the spatial extent of changes in coral communities is important to the understanding of resiliency of coral reefs under stress from human impacts.

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Section: Signal Processingmentioning

confidence: 99%

Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms

Wilson

Parrish

Battista

et al. 2019

Estuaries and Coasts

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“…These sorts of data are needed to develop and test improved risk reduction methods that go beyond assessing coastal hazards (e.g., van Verseveld et al, 2015). The coastal zone LIDAR system includes a blue-green LIDAR that acquires bathymetric data in the nearshore region (Feygels et al, 2013;Tuell, Barbor, and Wozencraft, 2010). Although the working depth varies with water clarity, these data are very useful for assessing changes in coastal morphology.…”

Section: Accuracy and Precisionmentioning

confidence: 99%

Rapid, Remote Assessment of Hurricane Matthew Impacts Using Four-Dimensional Structure-from-Motion Photogrammetry

Sherwood

Warrick

Hill

et al. 2018

Journal of Coastal Research

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“…This system is capable to take measurements of simultaneous land and shallow water depth. It employs a frequency-doubled ND: YAG laser with a single-wavelength of 532 nm (green), a half-swath angle 20°, a 33 KHz pulse repetition frequency, and 1 mrad beam divergence that yields a laser footprint one 1000th of the laser optical range, the main technical characteristics of Aquarius system in shallow water mode was shown in Table 2 [ 40 ].…”

Section: Description Of Datasetsmentioning

confidence: 99%

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms

Ding

Zhu

et al. 2018

Sensors

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In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

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CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation

Cited by 17 publications

References 0 publications

Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms

Mapping Seafloor Relative Reflectance and Assessing Coral Reef Morphology with EAARL-B Topobathymetric Lidar Waveforms

Rapid, Remote Assessment of Hurricane Matthew Impacts Using Four-Dimensional Structure-from-Motion Photogrammetry

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms

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