Measurement of ocean water optical properties and seafloor reflectance with scanning hydrographic operational airborne lidar survey (SHOALS): I. Theoretical background

Kopilevich, Yuri; Feygels, Viktor; Tuell, Grady; Surkov, Alexey

doi:10.1117/12.618923

Cited by 21 publications

(15 citation statements)

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“…The effect of parameters listed in Table 3 on the return intensity value will be demonstrated in Equation (4) ( Section 5.2 ). Acquisition geometry parameters must now consider factors such as the bathymetric angle of incidence, or the angle off nadir at which the pulse is transmitted from the aircraft, aircraft altitude, refracted beam angle, and the receiver field of view [ 82 , 83 , 84 , 85 , 86 , 87 ]. The water depth has a significant effect on the power of the return pulse, as the power decays exponentially with depth [ 35 , 84 ].…”

Section: Effective Parameters Influencing Intensity Measurementsmentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

284

214

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In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

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Section: Effective Parameters Influencing Intensity Measurementsmentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

284

214

View full text Add to dashboard Cite

show abstract

“…the radius of the receiver aperture). Then, by leveraging the statistical interrelationships between the water's beam attenuation coefficient and its other optical properties as described in [5], we generate a proxy formulation for F as a function of the optical path length traveled in the water.…”

Section: Estimating Fov Lossmentioning

confidence: 99%

“…The phenomenon varies strongly with many geometric and environmental factors including optical path length, beam divergence, receiver FOV, and the beam attenuation of the water column. Analytical expressions for it are computationally intensive and not readily implementable in realtime systems [4,5]. Exploiting them, we developed a simpler and faster approach for predicting FOV loss that is based on using a look-up table.…”

Section: Introductionmentioning

confidence: 99%

New Procedure for Estimating Field-of-View Loss in Bathymetric Lidar

Tuell

Carr

2013

Imaging and Applied Optics

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Bathymetric lidar waveforms may be inverted to generate 3D reflectance images of the shallow-water seafloor. The accuracy of these images is improved when the radiometric inversion accounts for losses arising from the scattering of photons out of the receiver's field of view. Here, we present a new procedure for predicting this loss that is easily implementable in real time. Our technique employs a look-up table containing coefficients for a simple proxy equation yielding loss values that are close approximations to those computed with the full analytical expression for the field of view loss. The coefficients were determined by fitting the proxy equation to thousands of loss functions generated with the full analytical expression wherein we systematically varied geometric and environmental parameters. This work contributes to the goal of generating realtime 3D reflectance images in future bathymetric lidar systems.

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“…(1), F P is a function decreasing from unity to zero as in-water path length increases. Following [5], it may be modeled as…”

Section: Theory and Backgroundmentioning

confidence: 99%

“…(1) and (2) are related to inherent optical properties of the water (a; b b ; b f , and m) and are, generally, independent of the beam attenuation coefficient c. However, these parameters may be computed from the beam attenuation coefficient for particular water conditions using the empirical relationships provided in [5]. Thus, by varying c, we can systematically vary the other parameters of the water.…”

Section: Theory and Backgroundmentioning

confidence: 99%

Estimating field-of-view loss in bathymetric lidar: application to large-scale simulations

Carr

Tuell

2014

Appl. Opt.

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When designing a bathymetric lidar, it is important to study simulated waveforms for various combinations of system and environmental parameters. To predict a system's ranging accuracy, it is often necessary to analyze thousands of waveforms. In these large-scale simulations, estimating field-of-view loss is a challenge because the calculation is complex and computationally intensive. This paper describes a new procedure for quickly approximating this loss, and illustrates how it can be used to efficiently predict ranging accuracy.

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Measurement of ocean water optical properties and seafloor reflectance with scanning hydrographic operational airborne lidar survey (SHOALS): I. Theoretical background

Cited by 21 publications

References 0 publications

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

New Procedure for Estimating Field-of-View Loss in Bathymetric Lidar

Estimating field-of-view loss in bathymetric lidar: application to large-scale simulations

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