Based on the processing of CZMIL data collected in Hawaii during a JALBTCX mission (2013) and in the Pacific for The Ocean Cleanup project (October, 2016), we demonstrate the possibility of reliably estimating the seawater column’s optical properties from lidar waveforms in deep clear (Jerlov class I and IB) waters. With minor improvements to the data processing method previously applied to Florida survey data (2003, 2006, 2012–2017), we estimate the diffuse attenuation coefficient at the wavelength of 532 nm, Kd (532), to be 0.045–0.060 m-1 in both regions. The results are in good agreement with space satellite data for the days of the lidar surveys and with Jerlov’s Kd curves for water classes I and IB.
CZMIL is an airborne multi-sensor system that exploits the data fusion paradigm to generate automated and high- resolution 3D environmental maps of coastal zones. CZMIL is used to map the near-shore environment for engineering and nautical charting applications on a recurring basis under the U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program. We have developed a mathematical framework, based on the contributing individual systematic and environmental parameters, for the estimation of Total Propagated Uncertainties (TPU) associated with CZMIL bathymetric data. We developed the TPU model based on the General Law of the Propagation of Variances. TPU is a critical metadata that characterizes the quality of a hydrographic survey. If there are issues with data integrity, TPU can be used as a diagnostic tool to provide insight and identify the particular lidar sub-system or processing module that is responsible. Moreover, because the overall ranging accuracy for any specific lidar bathymetric system is a function of water column properties, we have developed a simplified water-depth uncertainty model based on the different water types that CZMIL typically surveys. This depth uncertainty model is utilized in the TPU model. In this paper we will discuss the methodology, and list and discuss the CZMIL parameters that contribute to the uncertainty. We will also present TPU estimates for sample CZMIL datasets and compare theoretical and actual uncertainties. These actual or empirical uncertainties are estimated by comparing CZMIL positional data with ground truth.
Airborne bathymetric lidar (Light Detection and Ranging) systems measure photoelectrons on the optical path (range
and angle) at the photocathode of a returned laser pulse at high rates, such as every nanosecond. The collected
measurement of a single pulse in a time series is called a waveform. Based on the calibration of the lidar system, the
return signal is converted into units of received power. This converted value from the lidar waveform data is used to
compute an estimate of the reflectance from the returned backscatter, which contains environmental information from
along the optical path. This concept led us to develop a novel tool to visualize lidar data in terms of the returned
backscatter, and to use this as a data analysis and editing tool. The full lidar waveforms along the optical path, from laser
points collected in the region of interest (ROI), are voxelized into a 3D image cube. This allows lidar measurements to
be analyzed in three orthogonal directions simultaneously. The laser pulse return (reflection) from the seafloor is visible
in the waveform as a pronounced "bump" above the volume backscatter. Floating or submerged objects in the water may
also be visible. Similarly, forest canopies and tree branches can be identified in the 3D voxelization. This paper discusses
the possibility of using this unique three-orthogonal volume visualizing tool to extract environmental information for
carrying out rapid environmental assessments over forests and water.
CZMIL is an integrated lidar-imagery system and software suite designed for highly automated generation of physical and environmental information products for coastal zone mapping in the framework of the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP). This paper presents the results of CZMIL system validation in turbid water conditions along the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. Results of the USACE May-October 2012 mission in Green Bay, WI and Lake Erie are presented. The system performance tests show that CZMIL successfully achieved 7-8m depth in Mississippi with Kd =0.46m-1 (Kd is the diffuse attenuation coefficient) and up to 41m in Florida when Kd=0.11m-1. Bathymetric accuracy of CZMIL was measured by comparing CZMIL depths with multi-beam sonar data from Cat Island, MS and from off the coast of Fort. Lauderdale, FL. Validation demonstrated that CZMIL meets USACE specifications (two standard deviation, 2σ, ~30 cm). To measure topographic accuracy we made direct comparisons of CZMIL elevations to GPS-surveyed ground control points and vehicle-based lidar scans of topographic surfaces. Results confirmed that CZMIL meets the USACE topographic requirements (2σ, ~15 cm). Upon completion of the Green Bay and Lake Erie mission there were 89 flights with 2231 flightlines. The general hours of aircraft engine time (which doesn't include all transit/ferry flights) was 441 hours with 173 hours of time on survey flightlines. The 4.8 billion (!) laser shots and 38.6 billion digitized waveforms covered over 1025 miles of shoreline.
CZMIL is an integrated lidar-imagery sensor system and software suite designed for the highly automated generation of physical and environmental information products for mapping the coastal zone. This paper presents the results of CZMIL system validation in turbid water conditions on the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. The system performance test shows that CZMIL successfully achieved 7-8m depth in Kd =0.46m-1 (Kd is the diffuse attenuation coefficient) in Mississippi and up to 41m when Kd=0.11m-1 in Florida. With a seven segment array for topographic mode and the shallow water zone, CZMIL generated high resolution products with a maximum pulse rate of 70 kHz, and with 10 kHz in the deep water zone. Diffuse attenuation coefficient, bottom reflectance and other environmental parameters for the whole multi km2 area were estimated based on fusion of lidar and CASI-1500 hyperspectral camera data.
We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to
form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also
developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion
algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data
at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with
either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification
accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.
Integration of a bathymetric lidar and imaging spectrometer in CHARTS presented the challenge of developing new
algorithms and software for combining these two types of data. To support this development, we conducted several
field campaigns to collect airborne and in-situ data of the water column and seafloor. This work, sponsored by the
Office of Naval Research (ONR) led to development of the Rapid Environmental Assessment (REA) processor. REA
can be used to produce seafloor reflectance images from both sensors, and classification maps of the seafloor.
Ultimately, REA became the prototype software for CZMIL, and the CZMIL Data Processing System (DPS) has been
produced as a continuous refinement of REA. Here, we describe the datasets collected and illustrate results achieved
with the REA software.
A significant challenge in the CZMIL program was to develop a topographic/bathymetric lidar delivering high spatial
resolution 3D data in shallow, turbid waters, without sacrificing performance in deeper waters.
To support analysis of the trade space inherent in the design process, we developed a waveform simulator capable of
predicting CZMIL waveforms by varying parameters of the physical design and environmental properties of the seafloor
and water column.
Here, we describe the predicted performance of the proposed hardware and algorithms for generating seafloor point
clouds in a number of simulated environments.
Range measurements in CZMIL1,2 are accomplished with signal processing techniques applied to green lidar waveforms.
In the design phase of the project, we developed software to simulate waveforms for CZMIL, and have used these
simulated waveforms to design ranging algorithms, and test their accuracies. Our results indicate the topographic ranging
accuracy to hard targets should be on the order of 2cm. In this paper, we discuss the simulations, algorithms, and results.
The University of Southern Mississippi's Center of Higher Learning has developed a Waveform Viewer, Attribute
Viewer, and a 3D Editor for use in the CZMIL Point Cloud Manual Editor (CME). The Waveform Viewer displays
various channels of CZMIL waveforms within the 2D/3D editor interface of CME. This module provides the user an
interactive tool set consisting of a cross sectioning mechanism for the intensity time-bin relationship, waveform file
output, and zooming capabilities. The Attribute Viewer provides the data analyst with information to analyze various
environmental and spatial parameters that might contribute to errors in the measured points. The 3D Editor offers the
benefits of capturing depth outliers; an intuitive visual connectivity with the 2D editor; and the implementation of
volumetric directional slice isolation of data outliers.
KEYWORDS: Data fusion, Data modeling, Reflectivity, LIDAR, Signal attenuation, Image fusion, CZMIL, Image classification, Double positive medium, 3D modeling
CZMIL will simultaneously acquire lidar and passive spectral data. These data will be fused to produce enhanced
seafloor reflectance images from each sensor, and combined at a higher level to achieve seafloor classification. In the
DPS software, the lidar data will first be processed to solve for depth, attenuation, and reflectance. The depth
measurements will then be used to constrain the spectral optimization of the passive spectral data, and the resulting water
column estimates will be used recursively to improve the estimates of seafloor reflectance from the lidar. Finally, the
resulting seafloor reflectance cube will be combined with texture metrics estimated from the seafloor topography to
produce classifications of the seafloor.
Estimation of water column optical properties and seafloor reflectance (532 nm) is demonstrated using recent SHOALS data collected at Fort Lauderdale, Florida (November, 2003). To facilitate this work, the first radiometric calibrations of SHOALS were performed. These calibrations permit a direct normalization of recorded data by converting digitized counts at the output of the SHOALS receivers to input optical power. For estimation of environmental parameters, this normalization is required to compensate for the logarithmic compression of the signals and the finite frequency of the bandpass of the detector/amplifier. After normalization, the SHOALS data are used to estimate the backscattering coefficient, the beam attenuation coefficient, the single-scattering albedo, the VSF asymmetry, and seafloor reflectance by fitting simulated waveforms to actual waveforms measured by the SHOALS APD and PMT receivers. The resulting estimates of these water column optical properties are compared to in-situ measurements acquired at the time of the airborne data collections. Images of green laser bottom reflectance are also presented and compared to reflectance estimated from simultaneously acquired passive spectral data.
For the past two decades, hydrographic surveyors have used Optech's bathymetric laser technology to accurately measure water depths and to describe the geometry of the shallow-water seafloor. Recently, we have demonstrated the potential to produce bottom images from estimates of SHOALS-1000T green laser reflectance, and spatial variations in the optical properties of the water column by analyzing time-resolved waveforms. We have also performed the electronic and geometric integration of an imaging spectrometer into SHOALS, and have developed a first generation of software which provides for the exploitation of the combined laser and hyperspectral data within a fusion paradigm. In this paper, we discuss relevant sensor and data fusion issues, and present recent 3D benthic mapping results.
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