Extracting Shallow-Water Bathymetry from Lidar Point Clouds Using Pulse Attribute Data: Merging Density-Based and Machine Learning Approaches

Abstract

To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML¹) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space.

Keywords:

• Airborne Lidar
• Florida Keys
• extreme gradient boosting
• k-means clustering
• shallow water bathymetry

Disclosure statement

The authors have no potential competing financial nor non-financial interests in the work presented.

Data availability statement

Data that support the findings of this study are available at the link doi.org/10.6084/m9.figshare.12597404. SBET data in the required format are provided at the figshare link. Though the .las data used are available to the public, the authors are not authorized to make them directly available. A small sample of the data for a single data tile are provided at the figshare link. Complete data sets (2016_420500e_2728500n.laz, 2016_426000e_2708000n.laz, 2016_428000e_2719500n.laz, and 2016_430000e_2707500n.laz) can be downloaded from https://coast.noaa.gov/digitalcoast/data/ (Data set name: 2016 NGS Topobathy Lidar: Key West FL’) as compressed .laz files. These can be decompressed using the LASzip tool which can be downloaded from laszip.org.

Notes

1 A list of acronyms is provided at the end of the article.

Additional information

Funding

This work was supported by the National Oceanic and Atmospheric Administration (NOAA) Grant NA15NOS400020.