2019 NOAA Bathymetric Lidar with Waveform Metrics: Saipan, CNMI

Time of data collection.

The average point spacing is 0.63 meters. It is spatially variable with an estimated minimum of 0.09 meters and an estimated maximum of 2.23 meters. This includes all points, not ground points only.

Custom download app allowing subsetting, projection change, and generation of derived products.

Input Data File Specification: LAS 1.4

Output File Compression: Zip

Bulk download of data files in vertical orthometric datum, geographic coordinates.

This graphic displays the footprint for this lidar data set.

Woolpert. (2019). Lidar Mapping Report: Project ID 17502, Work Unit 219835. NOAA CONTRACT NUMBER: EA-133C-16-CQ-0046 and USGS CONTRACT NUMBER: G16PC00022.

This report describes the collection of the dataset used as input for this dataset. Not all parts are applicable.

Collin, A., Archambault, P., & Long, B. (2011). Predicting species diversity of benthic communities within turbid nearshore using full waveform bathymetric LiDAR and machine learners. PloS one, 6(6), e21265. https://doi.org/10.1371/journal.pone.0021265

Kashani, A. G., Olsen, M. J., Parrish, C. E., & Wilson, N. (2015). A review of LiDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration. Sensors, 15(11), 28099-28128. https://doi.org/10.3390/s151128099

Kogut, T., Tomczak, A., Stowik, A., & Oberski, T. (2022). Seabed modelling by means of airborne laser bathymetry data and imbalanced learning for offshore mapping. Sensors, 22(9), 3121. https://doi.org/10.3390/s22093121

NOAA and USGS. (2019). USGS/NOAA Topobathy Lidar DEM: CNMI (Aguijan, Rota, Saipan, Tinian). Online: https://chs.coast.noaa.gov/htdata/raster5/elevation/CNMI_Topobathy_DEM_2019_9474/ (Accessed 15 November 2023).

Parrish, C. E., Rogers, J. N., & Calder, B. R. (2014). Assessment of waveform features for lidar uncertainty modeling in a coastal salt marsh environment. IEEE Geoscience and Remote Sensing Letters, 11(2), 569‐573. https://doi.org/10.1109/LGRS.2013.2280182

Wilson, N., Parrish, C. E., Battista, T., Wright, C. W., Costa, B., Slocum, R. K., ... & Tyler, M. T. (2019). Mapping seafloor relative reflectance and assessing coral reef morphology with EAARL-B topobathymetric Lidar waveforms. Estuaries and Coasts, 1-15. https://doi.org/10.1007/s12237-019-00652-9

nccos.data@noaa.gov

The return number field appears to be zero for all points, which is not a legitimate value. The extra bytes fields are lacking descriptions and documentation of the fields has not been provided. The classification scheme does not conform to LAS 1.4 specifications. The process for creating the extra bytes waveform metrics has not been documented.

All lidar data were acquired using a HE4X sensor (Figure 4). The HE4X is a latest generation topographic and bathymetric lidar sensor. The system provides denser data than previous traditional bathymetric lidar systems. It is unique in its ability to acquire bathymetric lidar, topographic lidar and 4-band digital camera imagery simultaneously.

The HE4X provided up to 500 kHz topographic data and an effective 140 kHz shallow bathymetric data and a 40 kHz deep channel. While not a required deliverable for this survey, 4-band 80 MP digital camera imagery was also collected simultaneously with the sensor's RCD-30 camera and utilized during data editing in some cases.

The bathymetric and topographic lasers are independent and do not share an optical chain or receivers, so they are optimized for their specific function. As with any bathymetric lidar, maximum depth penetration is a function of water clarity and seabed reflectivity. The HE4X is designed to penetrate to 3 times the secchi depth. This is also represented as Dmax = 4/K, where K is the diffuse attenuation coefficient, and assuming K is between 0.1 and 0.3, a normal sea state and 15% seabed reflectance.

Both the topographic and bathymetric sub-systems use a palmer scanner to produce an elliptical scan pattern of laser points with a degree of incidence ranging from +/-14 degrees (front and back) to +/-20 degrees (sides), providing a 40 degree field of view. This has the benefit of providing multiple look angles on a single pass and helps to eliminate shadowing effects. This can be of particular use in urban areas, where all sides of a building are illuminated, or for bathymetric features such as the sides of narrow water channels, or features on the seafloor such as smaller objects and wrecks. It also assists with penetration in the surf zone where the back scan passes the same ground location a couple of seconds after the front scan, allowing the areas of whitewater to shift.

Position and orientation data were acquired in the aircraft using a NovAtel SPAN with LCI-100C IMU. All data were post-processed using NovAtel Inertial Explorer software to provide a tightly coupled position and orientation solution.

A single base station was used to control trajectory processing providing final trajectories for Saipan and Tinian on NAD83 (MA11), Epoch 2010, located in the Saipan airport. This base station was replaced for each of the three separate collects of the project (Table 11). SPN1, SPN2 and SPN3 were occupied with a Trimble GNSS receiver by Woolpert. Due to the distance of Rota, Aguijan, Farallon de Medinilla, and Pagan from the single base station on Saipan and their remoteness a precise point positioning (PPP) solution was used for them on ITRF2014.

To establish a reliable coordinate for SPN1 data were uploaded to the National Geodetic Service (NGS) Online Positioning User Service (OPUS), and for SPN2 and SPN3 Trimble CenterPoint RTX Post-Processing service was used. The average OPUS or RTX coordinate from multiple days of observations was used to process the final trajectories.

Initial data coverage analysis and quality checks to ensure there were no potential system issues were carried out in the field prior to demobilization of the sensor. Final processing was conducted in Woolpert's offices.

In general data were initially processed in Leica's Lidar Survey Studio (LSS) using final processed trajectory information. LAS files from LSS were then imported to a Terrascan project where spatial algorithms were used to remove noise and classify bare earth/ground. Manual review was conducted in both Terrascan and LP360 prior to product creation.

Lidar processing was conducted using the Leica Lidar Survey Studio (LSS) software. Calibration information, along with processed trajectory information were combined with the raw laser data to create an accurately georeferenced lidar point cloud for the entire survey in LAS v1.4 format. All points from the topographic and bathymetric laser include 16-bit intensity values.

During this LSS processing stage, an automatic land/water discrimination is made for the bathymetric waveforms. This allows the bathymetric (green) pulses over water to be automatically refracted for the pulse hitting the water surface and travelling through the water column, producing the correct depth. Another advantage of the automatic land/water discrimination is that it permits calculation of an accurate water surface over smaller areas, allowing simple bathymetric processing of smaller, narrower streams and drainage channels. Sloping water surfaces are also handled correctly.

Prior to processing, the hydrographer can adjust waveform sensitivity settings dependent on the environment encountered and enter a value for the refraction index to be used for bathymetry. The index of refraction is an indication of the water type. Values used for sensitivity settings and the index of refraction are included in the LSS processing settings files. A value of 1.34206 was used for the index of refraction, indicating saltwater.

In the field, default waveform sensitivity settings were used for processing. In order to determine the optimal waveform sensitivity settings for final processing, sample areas were selected and processed with multiple different settings, to iteratively converge on the best possible settings. This is done by reviewing the processed point cloud and waveforms within sample areas. Settings affect which waveform peaks are classified as valid seabed, and which peaks are classified as noise. Optimal settings strike a balance between the amount of valid data that is classified as seabed bottom, and the amount of noise that is incorrectly classified due to peaks in the waveforms. Ideally all valid data is selected, while only a small amount of noise remains to be edited out. Once optimal threshold settings were chosen, these were used for the entire project.

It is important to note that all digitized waveform peaks are available to be reviewed by the hydrographer; both valid seabed bottom and peaks classed as noise. This allows the hydrographer to review data during TerraScan and LP360 editing for valid data such as objects that may have been misclassified as noise.

LSS processing produced LAS files in 1.4 format. Additional QC steps were performed prior to import to TerraScan. Firstly, the derived water surface was reviewed to ensure a water surface was correctly calculated for all bathymetry channels. No significant issues were apparent. Spot checks were also made on the data to ensure the front and back of the scans remained in alignment and no calibration or system issues were apparent prior to further data editing in TerraScan.

LSS stores data in multiple LAS files for a single flight line. Each file corresponds to a single .dat file from the raw airborne data. Woolpert merged these multiple files into a single file per flight line and moved data into a standard class definition in preparation for data editing using Woolpert's proprietary scripts within SAFE's FME software.

Data produced by LSS for flights over Saipan and Tinian were processed on the NAD83 (MA11) Epoch 2010 datum in UTM 55N Zone with units in meters, and elevations on the ellipsoid also in meters. Data produced for Aguijan and Rota were processed on the ITRF2014 datum in UTM 55N Zone with units in meters, with elevations on the ellipsoid also in meters.

After data were processed in LSS and the data integrity reviewed, Aguijan, and Rota were transformed from the ITRF2014 ellipsoid to the NAD83 (MA11) Epoch 2010 ellipsoid using VDatum. With the entire project now on the correct ellipsoid, data were organized into tiles within a TerraScan project. The tile layout is the same as that provided with the project deliverables.

Data classification and spatial algorithms were applied in Terrasolid's TerraScan software. Customized spatial algorithms, such as isolated points and low point filters, were run to remove gross fliers in the topographic and bathymetric data. A grounding algorithm was also run on the topographic data to distinguish between points representing the bare earth, and other valid topo lidar points representing features such as vegetation, buildings, and so forth. Algorithms were run on the entire dataset.

Data were reviewed manually to reclassify any valid bathy points incorrectly identified by the automated routines in LSS as invalid, and vice versa. In addition, any topo points over the water were reclassified to correct the ground representation. Manual editing was conducted both in TerraScan and LP360. Steps for manual editing included:

- Re-class any topo unclassified laser data and bathy seabed data from the water surface to a water surface class

- Review bathymetry in cross section.

- Re-class suitable data to Seabed (Class 40).

- Re-class any noise in the bathy ground class to bathy noise (Class 45).

- Review bathymetry using imagery and nautical charts and re-class obvious man-made objects to Submerged Object (Class 43).

Once editing was completed in TerraScan the data were vertically transformed to the NMVD03 datum using GEOID12B.

Data were received by NOAA OCM from NOAA NCCOS in NAD83(MA11) UTM 55N coordinates with the vertical in NMVD03/Geoid12b. Waveform metrics had been added as extra bytes, but no documentation of the process was provided. The projection and geoid model were removed to produce geographic coordinates on the ellipsoid (EPSG:6321) for incorporation into the NOAA Digital Coast to allow custom processing.