25 Aug 2026

Wetland Elevation Change Following Beneficial Use of Dredged Material Nourishment

Abstract: The beneficial use of dredged material (BUDM) to nourish degrading wetlands is a direct solution to increase surface elevation to help wetlands keep pace with sea level rise (SLR). While there have been numerous demonstrations of BUDM in wetland environments, there is a limited understanding of the resultant spatial and temporal elevation response due to consolidation of the dredged material and underlying wetland foundation soils. To address this, surface elevations were monitored following multiple BUDM nourishments on a back-bay island in New Jersey. Field data was compared to consolidation models to assess the viability of current geotechnical modeling practices. Multispectral surveys were performed to document the revegetation of the nourished island over time. The placement of dredged material smothered the vegetation, but the site experienced significant revegetation after three full growing cycles. Approximately two years post BUDM, the nourished area experienced a 0.19 ± 0.11-m increase in elevation. The comparison of elevation immediately after nourishment and two years post-nourishment found that approximately two-thirds of elevation loss was the result of consolidation of deposited dredged material and the underlying wetland foundation. It was found that a reliance on solely laboratory data can induce a large degree of uncertainty within projected surface elevations. Thus, the implementation of in-situ geotechnical methods is strongly advised when possible. This investigation allowed for a comprehensive examination of geotechnical modeling methods for planning wetland nourishments, including predicting the ability to keep pace with sea level rise. Ultimately, the two BUDM nourishments offset the effects of SLR by approximately 18–28 years.

17 Aug 2026

Modeling Snow Optical Properties from Single Wavelength Airborne Lidar in Steep Forested Terrain

Abstract: Airborne lidar is a powerful tool used by water resource managers to map snow depth and aid in producing spatially distributed snow water equivalent (SWE) when combined with modeled density. However, limited research so far has focused on retrieving optical snow properties from lidar. Optical snow surface properties directly impact albedo, which has a major control on snowmelt timing, which is especially useful for water management applications. Airborne lidar instruments typically emit energy at a wavelength of 1,064 nm, which can be informative in mapping optical snow surface properties since grain size modulates reflectance at this wavelength. In this paper we present and validate an approach using airborne lidar for estimating snow reflectance and optical grain size at high spatial resolution. We utilize three lidar flights over the Boise National Forest, United States, during a winter season from December 2022 to March 2023. We discuss sensitivities to beam incidence angles, compare results to in situ measurements snow grain size, and perform spatial analyses to ensure reflectance and optical grain size varies across space and time as anticipated. Modeled optical grain size from lidar performed well (Root mean squared difference = 49 μm; percent mean absolute difference = 31%; n = 28), suggesting that aerial lidar surveys can be useful in mapping snow reflectance and optical grain size for dry snow, and may support development of other remote sensing technologies and aid water resources management.

29 Jun 2026

Automated Workflows for Airborne Lidar and Photogrammetry Snow Depth Analyses

Abstract: Lidar and photogrammetry techniques provide highly accurate methods for mapping snow depth distribution. However, postprocessing point clouds for snow depth estimation is more complex compared to other earth science applications. This paper presents ice-road-copters (IRC), an open-source Python toolkit that facilitates processing and georeferencing of lidar and photogrammetry point clouds. Case studies demonstrate the tool’s utility across different sensors and platforms over a complex mountainous study area. Results show that a well-configured digital elevation model (DEM) filter effectively removes most noise and outliers from point cloud data. The Simple Morphological Filter (SMRF) generally perform well for ground segmentation but optimal results across diverse terrains may require site-specific tuning, particularly of the elevation threshold and scalar parameters in more complex landscapes. Different methods, including manual depth measurements or snow-free features, can be used to coregister DEMs, reducing vertical errors, eliminating large bias and achieving comparable accuracy to using exposed control surfaces. Derived snow depth rasters showed strong agreement with in situ probe measurements—root-mean-square error (RMSE) of less than 16 cm. Overall, IRC simplifies the transformation of raw point clouds into high-resolution DEMs and value-added snow products, facilitating efficient multitemporal analysis to support military and hydrology applications.

29 Sep 2025

Introduction of the Pivox System—A Low-Cost, Rapidly Deployable Modular Lidar System

Abstract: Terrestrial light detection and ranging instruments can provide extremely valuable data for a multitude of applications in a wide variety of science and engineering fields. However, terrestrial lidar systems (TLS), are prohibitively expensive for many projects and require significant power and data resources to allow for the collection and transmittal of real-time lidar data, limiting their use in remote applications. To address the need for low-cost lidar data collection capabilities in remote environments, the US Army Corps of Engineers, Engineer Research Development Center, Cold Regions Research and Engineering Laboratory, and Geotechnical and Structures Laboratory (GSL) developed the Pivox System. The Pivox System integrates a Livox lidar sensor to a Raspberry Pi, allowing for real-time data collection, processing, and transmittal using a self-contained unit that also includes the power supply and communications equipment. We present data collected using the Pivox System in three diverse environments to measure changes in snow depth, the presence of lake ice, and erosion during a levee overtopping experiment.

12 Aug 2025

Assessing Shorelines Extracted from Satellite Imagery Using Coincident Terrestrial Lidar Linescans

Abstract: Previous analyses comparing CoastSat satellite-derived shorelines to morphological data highlighted site-specific errors in outputs related to concurrent wave runup conditions. We present a comparison of lidar-derived runup and beach elevation data to CoastSat satellite-derived waterlines extracted using two image sources and two threshold algorithms. Results show SDW extracted using Otsu thresholds correlated better with lidar-derived waterlines, SDW extracted using the weighted peaks threshold were consistently positioned in the upper swash and correlated better with a runup bulk statistic. Assigning the best-fit runup bulk statistic as the waterline elevation to weighted peaks SDW resulted in SDS with less scatter than the Otsu SDW. Horizontal errors for converted datum-referenced shoreline were lowest when SDW were converted to SDS using best-fit measured runup bulk statistics and a measured slope. For weighted peaks SDW from both image sources, assigning the best-fit parameterized runup bulk statistic and an average slope in the SDW to SDS conversion reduced error by ∼ 20% to ∼ 35% when compared to tidal elevation and average slope. These findings confirm runup corrections can improve native SDS outputs, although the magnitude of final shorelines error depended on specific imagery product, local beach slope, threshold technique, runup parametrization, and chosen reference contour.

21 Jun 2024

Observations of Beach Change and Runup, and the Performance of Empirical Runup Parameterizations during Large Storm Events

Abstract: Timeseries observations of beach elevation change and wave runup from a tower-mounted stationary lidar assess the skill of 2% runup exceedance (𝑅2%) estimates during four storm events at Duck, NC. The runup parameterization requires the foreshore beach slope, which generally unknown during high energy events. Pre-storm estimates are often used as a proxy. 𝑅2% hindcasts use the observed time-varying beach slope and a static pre-storm beach profile, yielding an 𝑅2% skill of 0.57. The skill drops to −1.0 using seasonal mean beach slopes and reduces after the peak of two storms with the appearance of beach cusps in the swash zone morphology. One storm’s runup is underpredicted by up to 1.0 m at high tides following the storm peak when cusps are present Additional pre- and post-storm mobile lidar surveys for one storm confirm ubiquitous small-scale beach cusps along 8 km of the local shoreline. The results suggest skillful runup estimates are often attainable given the availability of beach information before a storm. The parameterization errors increase when beach cusps develop, highlighting the need to extend standard one-dimensional runup parameterizations to account for two-dimensional effects.

28 Nov 2023

Total Water Level Controls on the Trajectory of Dune Toe Retreat

Abstract: This study examines the trajectory (slope) of coastal foredune toe retreat in response to nine storm events that impacted the Outer Banks, North Carolina, USA. High resolution, three-dimensional, repeat mobile terrestrial lidar observations over a four kilometer stretch of coast were used to assess spatiotemporal beach and dune evolution at the storm timescale. Consistent with existing field observations from other sandy coastlines, an upward toe retreat was observed for most instances of dune retreat in the Outer Banks. However, these new topographic data indicate that the retreat can proceed steeply downward when the maximum total water level (TWL) defined by the 2% runup exceedance level is not high enough, for long enough, to erode the dune face. Non-linear relationships were found between the dune toe retreat trajectory as well as both the magnitude and duration of TWL above the dune toe, where instances of upward- and downward-directed retreat are best differentiated using the 7% runup exceedance level, rather than the commonly used 2% level. This physically justified non-linear relationship is shown to be consistent with observations from other studies, and could be a more effective parameterization for the retreat trajectory than those currently implemented in wave-impact dune erosion models.

31 Mar 2022

Continued Investigation of Thermal and Lidar Surveys of Building Infrastructure

ABSTRACT: We conducted a combined lidar and thermal infrared survey from both ground-based and Unmanned Aerial System (UAS) platforms at McMurdo Station, Antarctica, in February 2020 to assess the building thermal envelope and infrastructure of the Crary Lab and the wet utility corridor (utilidor). These high-accuracy, coregistered data produced a 3-D model with assigned temperature values for measured surfaces, useful in identifying thermal anomalies and areas for potential improvements and for assessing building and utilidor infrastructure by locating and quantifying areas settlement and structural anomalies. The ground-based survey of the Crary Lab was similar to previous work performed by the team at both Palmer (2015) and South Pole (2017) Stations. The UAS platform focused on approximately 10,500 linear-feet of utilidor throughout McMurdo Station. The datasets of the two survey areas overlapped, allowing us to combine them into a single, georeferenced 3-D model of McMurdo Station. Coincident exterior temperature and atmospheric measurements and Global Navigation Satellite System real-time kinematic surveys provided further insights. Finally, we assessed the thermal envelope of the Crary Lab and the structural features of the utilidor. The resulting dataset is available for analysis and quantification.

12 May 2021

Methodology for Remote Assessment of Pavement Distresses from Point Cloud Analysis

Abstract: The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

14 Apr 2020

PUBLICATION NOTIFICATION: Coincidence Processing of Photon-Sensitive Mapping Lidar Data

 Link: http://dx.doi.org/10.21079/11681/35599 Report Number: ERDC/GRL TR-20-1Title: Coincidence Processing of Photon-Sensitive Mapping Lidar DataBy Christian Marchant, Ryan Kirkpatrick, and David OberApproved for Public Release; Distribution is Unlimited February 2020Abstract: Photon-sensitive mapping lidar systems are able to image at greater