SHOALS-enabled 3D benthic mapping

Tuell, Grady; Park, Joong Yong; Aitken, Jennifer; Ramnath, Vinod; Feygels, Viktor; Guenther, Gary C.; Kopilevich, Yuri

doi:10.1117/12.607010

Cited by 14 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other uses include classifying wetland species, measuring submerged aquatic vegetation, evaluating coral reef health, and identifying underwater bottom type (e.g. sand, seagrass, mud, coral, etc., (Tuell et al, 2005)). …”

Section: Mapping Requirementsmentioning

confidence: 93%

The Need for Sustained and Integrated High-Resolution Mapping of Dynamic Coastal Environments

Stockdon¹,

Lillycrop²,

Howd³

et al. 2006

mar technol soc j

View full text Add to dashboard Cite

A B S T R A C TThe coastal zone of the United States is a dynamic environment evolving in response to both natural processes and human activities. In order to protect coastal populations and resources, a detailed understanding of the physical setting and of the processes responsible for change is required. A sustained program of mapping coastal areas provides a means to establish baseline conditions, document change, and, in conjunction with models of physical processes, predict future behavior. Recent advances in mapping technology, including airborne lidars and hyperspectral imagers, allow for the rapid collection of high-resolution elevation data and land use information on a national scale. These rich data sets are critical to evaluating risk associated with coastal hazards, such as flooding during extreme storms. For example, coastal elevation data is a fundamental parameter in storm surge models that predict where flooding will occur, and land use maps serve as the foundation of assessments that identify the resources and populations that are most vulnerable. A comprehensive, national coastal mapping plan that is designed to collect, manage, and distribute these data, as well as to take advantage of recent progress in mapping technology, will provide a wealth of information for studying the processes of physical change, for determining areas vulnerable to coastal hazards, and for protecting and managing our coastal communities and resources.

show abstract

Section: Mapping Requirementsmentioning

confidence: 93%

The Need for Sustained and Integrated High-Resolution Mapping of Dynamic Coastal Environments

Stockdon¹,

Lillycrop²,

Howd³

et al. 2006

mar technol soc j

View full text Add to dashboard Cite

show abstract

“…Equation (1), adapted from [3], represents the optical domain signal detected from the sea floor for a temporally narrow pulse with transmitted peak power P t . Equation (1) can also represent the optical domain signal detected from arbitrary depth D by replacing ρ with β π (the water backscattering coefficient) and by treating the stretch factor as unity.…”

Section: Theory and Backgroundmentioning

confidence: 99%

“…Rather, it is often computed once for a specific scenario and then applied to all waveforms in the study. In some applications, computation of F P is avoided altogether, and it is treated as a modulator of K [3,7]. This coupling leads to an estimate of system attenuation K sys , which is not satisfactory for analyzing the ranging accuracy of alternate lidar designs in widely varying environmental conditions.…”

Section: Theory and Backgroundmentioning

confidence: 99%

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

Carr

Tuell

2014

Appl. Opt.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The performance for detection of small targets and sea bottom features was studied e.g. by Guenther et al, 1 West and Lillycrop, 2 and Steinvall et al 3,4 The work by Tuell et al 5,6 have demonstrated the potential to produce estimates of green laser reflectance by analyzing lidar waveforms, which provides information for the fusion of lidar and hyperspectral data for classification of e.g. sea floor vegetation.…”

Section: Introductionmentioning

confidence: 98%

Processing of airborne lidar bathymetry data for detailed sea floor mapping

Tulldahl

2014

Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; And Military Applications in Hyperspectral Imagin

View full text Add to dashboard Cite

Airborne bathymetric lidar has proven to be a valuable sensor for rapid and accurate sounding of shallow water areas. With advanced processing of the lidar data, detailed mapping of the sea floor with various objects and vegetation is possible. This mapping capability has a wide range of applications including detection of mine-like objects, mapping marine natural resources, and fish spawning areas, as well as supporting the fulfillment of national and international environmental monitoring directives. Although data sets collected by subsea systems give a high degree of credibility they can benefit from a combination with lidar for surveying and monitoring larger areas. With lidar-based sea floor maps containing information of substrate and attached vegetation, the field investigations become more efficient. Field data collection can be directed into selected areas and even focused to identification of specific targets detected in the lidar map. The purpose of this work is to describe the performance for detection and classification of sea floor objects and vegetation, for the lidar seeing through the water column. With both experimental and simulated data we examine the lidar signal characteristics depending on bottom depth, substrate type, and vegetation. The experimental evaluation is based on lidar data from field documented sites, where field data were taken from underwater video recordings. To be able to accurately extract the information from the received lidar signal, it is necessary to account for the air-water interface and the water medium. The information content is hidden in the lidar depth data, also referred to as point data, and also in the shape of the received lidar waveform. The returned lidar signal is affected by environmental factors such as bottom depth and water turbidity, as well as lidar system factors such as laser beam footprint size and sounding density.

show abstract

SHOALS-enabled 3D benthic mapping

Cited by 14 publications

References 0 publications

The Need for Sustained and Integrated High-Resolution Mapping of Dynamic Coastal Environments

The Need for Sustained and Integrated High-Resolution Mapping of Dynamic Coastal Environments

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

Processing of airborne lidar bathymetry data for detailed sea floor mapping

Contact Info

Product

Resources

About