7 Apr 2026

Vehicle and Unpiloted Aerial System Interferometric Synthetic Aperture Radar Data Collection and Processing

Abstract: Interferometric synthetic aperture radar (InSAR) systems have a wide breadth of cold regions science and engineering applications such as determining snow water storage, permafrost thaw induced subsidence and frost heave of the active layer, and ground slope and infrastructure stability in permafrost dominated regions. Here, we present project planning, data collection, and processing workflows from two L-band InSAR systems, L-band SAR (GS-L) and UAS-mounted (GLSAR). The GS-L platform is integrated on a mobile, ground-based platform while the GLSAR is integrated on an uncrewed aerial system (UAS). We describe the postprocessing steps to produce radar back-scattered power and interferograms for the analysis of subsurface and near-surface phenomena. These steps are common to all the sensors discussed in this report and include kinematic postprocessing of the sensor positions, focusing on the raw radar returns in range and azimuth to form the radar image, and calculating the interferometric phase between acquisitions. With examples from each platform, we demonstrate the utility of these InSAR sensors and discuss acquisition scenarios in which either ground-based or UAS-borne systems may deliver higher-quality information from one another.

3 Mar 2026

Enhanced Spatial Resolution of Landsat Imagery Through Systematic Sensor Offset Exploitation: A Blended Pansharpening Approach

Purpose: This technical note presents a novel blended pansharpening methodology that exploits the systematic 7.5-meter (m) geometric offset between Landsat multispectral (MS) and panchromatic (pan) sensors to achieve selective spatial enhancement beyond conventional 15 m resolution limits. The approach creates a variable resolution product with an effective resolution of approximately 11.25 m and demonstrates superior spatial detail preservation in urban infrastructure while maintaining perfect spectral integrity.

13 Feb 2026

Standalone Color-Based Bathymetry Over 10 Years at Duck (NC, USA) from Optical Satellite Imagery and Wave Breaking Analysis

Abstract: Coastal hazard forecasting and morphological modeling rely on having accurate and up-to-date nearshore bathymetry. Traditional methods provide high precision but are expensive, complex to deploy, and only cover limited areas, leaving many coastal regions unmapped or under surveyed. In this context, Satellite-Derived Bathymetry provides a more accessible and scalable alternative, enabling frequent and global observations of the nearshore zone. This study applies the color-based log-band ratio method to extract nearshore bathymetry at Duck, North Carolina, a highly dynamic environment with a wide range of turbidity values and wave breaking extents. The log-band ratio method is an empirical approach for estimating shallow-water depths from multispectral satellite imagery which relies on the natural attenuation of light in water column, where the ratio of two spectral bands is logarithmically related to water depth. Unlike traditional SDB approaches, this method relies only on nearshore in situ wave height data, using satellite-detected breaking positions and breaker height-to-depth ratio as depth calibration points. Additionally, an automated approach is used to select images where the green/blue band penetrates sufficiently into the water to retrieve bathymetry avoiding the subjectivity of traditional manual selection. The method is validated through alongshore median- and profile-based assessments, yielding a median RMSE of ∼60 cm. Sensitivity tests on key parameters, including the breaker height-to-depth ratio and the calibration time window, demonstrate that a constant breaker height-to-depth ratio provides reliable results and that a significant number of calibration points is necessary for accurate bathymetry retrieval. This approach retrieves instant bathymetries and allows for the extraction of bathymetry evolution over time, with 90 bathymetry maps available over the 10-year period due to the very high resolution and 2-day revisit VEN𝜇S satellite and the 10-m/5-day Sentinel-2 mission. The method is transferable to other optical satellites such as Landsat, although it should be applied with caution, enabling long-term nearshore bathymetry monitoring from the 1980s to the present.

1 Oct 2025

Predicting Seagrass Habitat Suitability with Remote Sensing and Machine Learning: A Case Study in the Mississippi-Alabama Barrier Islands

Abstract: Seagrass occupies sandy platforms landward of the Mississippi-Alabama barrier islands, where the benthos experiences consistent sediment transport. This work characterized benthos surrounding Cat Island, Mississippi, to assess the influence of elevation and geomorphological features (e.g., slopes, flats, peaks, and valleys) on seagrass presence. Two machine learning algorithms predicted seagrass presence/absence (from airborne hyperspectral imagery) based on elevation and geomorphology (derived from airborne lidar bathymetry) for 2016, 2018, and 2019. Results indicated elevation primarily influenced seagrass presence, with minimal impact from geomorphology. Elevation change was not predictive, suggesting seagrass tolerated observed deposition/erosion rates. This research showcases remote sensing and machine learning efficacy in predicting seagrass habitat suitability (greater than 70% accuracy) and conveys implications for conservation.

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.

25 Sep 2025

Considerations and Lessons Learned for Remote Sensing Data Acquisition of Understudied Wetland Vegetation Metrics

Purpose: Traditional field-based methods for monitoring wetland ecosystems are often limited by accessibility and cost, hindering comprehensive assessment of these vital habitats. These wetlands often present challenges for mapping and monitoring due to their size, location, and diverse vegetation types. Therefore, thorough planning and execution are essential for collecting reliable data for analysis and generating meaningful results. To overcome these challenges, we investigated how remote sensing data captured from uncrewed aerial systems (UAS), such as multispectral imagery and lidar, can be effectively used to develop and validate metrics for measuring wetland vegetation characteristics as an alternative to traditional field-based methods.

24 Sep 2025

Simulating Environmental Conditions for a Severe Dust Storm in Southwest Asia Using the Weather Research and Forecasting Model: A Model Configuration Sensitivity Study

Abstract: Dust aerosols create hazardous air quality conditions that affect human health, visibility, and military operations. Numerical weather prediction models are important tools for predicting atmospheric dust by simulating dust emission, transport, and chemical evolution. We assessed the Weather Research and Forecasting (WRF) model’s ability to simulate the atmospheric conditions that drove a major dust event in Southwest Asia during July–August 2018. We evaluated five WRF configurations against satellite observations and Reanalysis Version 5 (ERA5) reanalysis data, focusing on the event’s synoptic evolution, storm progression, vertical structure, and surface wind fields. Results revealed substantial differences between configurations using Noah and Noah Multiparameterization (Noah-MP) land surface models (LSMs), with Noah providing a superior representation of meteorological conditions despite theoretical expectations of similar performance in arid environments. The best-performing configuration (Noah LSM, Mellor–Yamada–Nakanishi–Niino planetary boundary layer scheme, and spectral nudging) of the five considered accurately simulated the progression of a low-level jet streak and the associated surface winds responsible for dust mobilization throughout the event. This study supports the US Army Engineer Research and Development Center’s efforts to improve dust forecasting and establishes a foundation for evaluating dust emission parameterizations by isolating meteorological forcing errors from dust model physics.

29 Aug 2025

Monitoring of Understudied Wetlands: State of Knowledge

Abstract: Some wetlands can present unique challenges for mapping and monitoring due to their size, location, foliage architecture, and spectral characteristics. For instance, assessing ecological condition and restoration success using traditional remote-sensing systems in forested and ephemeral wetlands is onerous. Therefore, the purpose of this technical note is to evaluate the state of knowledge and technology related to the use of remote sensing in assessing vegetation dynamics in understudied and hard to monitor wetlands. Ultimately, this exercise will identify data gaps and recommend improvements for analyzing and modeling wetland systems and trends, quantifying disturbance impacts, and assist efficiencies of data collection to improve management decisions, which in turn will help in reaching restoration goals.

27 Aug 2025

Demonstration of a Remotely Operated Vehicle for Inspecting the Chicago Electrical Fish Dispersal Barrier

Purpose: This report describes the US Army Engineer Research and Development Center (ERDC) application of a remotely operated vehicle to inspect an electrical fish dispersal barrier at the bottom of the Chicago Sanitary Ship Canal (CSSC) for the US Army Corps of Engineers–Chicago District.

26 Aug 2025

The Quick Response Toolbox User’s Guide

Abstract: Regional-scale beach morphology, volume, and shoreline changes are quantified using the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) ArcGIS Python toolboxes. This user’s guide details the JALBTCX toolbox framework and the operation of the Quick Response Toolbox. A walkthrough for each individual step within the toolbox will be presented along with example data from Homer, Alaska. Best practices and example data and figures are included as additional documentation for new users.