6 May 2026

Two-Dimensional Thermal and Dynamical Strain in Landfast Sea Ice from InSAR: Results From a New Analytical Inverse Method and Field Observations

Abstract: Observing continuous strain in sea ice at geophysical scales of tens of meters to kilometers requires displacement measurements made with millimeter-scale precision. Satellite-based interferometric synthetic aperture radar (InSAR) provides such precise measurements of relative surface displacement over broad spatial areas at regular intervals and, unlike point displacement measurements, it allows confident delineation of continuously deforming regions. However, InSAR only captures the 1-D component of surface displacement parallel to a radar’s lines-of-sight. Additional analysis is required to translate between these 1-D observations and the horizontal or vertical displacements they arose from. Previous studies utilize an iterative inverse model to constrain estimates of horizontal surface displacement from InSAR. Here we build upon that work outlining a new analytical inverse modeling method for quantifying displacement and strain over continuous regions of sea ice and provide comparison between model results and independent displacement observations. We demonstrate the inverse method over both landfast and drifting ice along the Alaskan coastline. These intercomparisons highlight environments in which displacements inverted from interferograms may be used as an independent estimator of surface strain, as well as the potential for the outlined inverse methods to be used in conjunction with other observing methods.

24 Apr 2026

Conceptual Sediment Budget Creation Using CorpsCam Imagery: Holland Harbor, Michigan

Abstract: This Regional Sediment Management (RSM) technical note (TN) discusses the development of a conceptual sediment budget at Holland Harbor, Michigan, using CorpsCam imagery. Imagery from May 2020 through October 2021 was analyzed to calculate volume change along Ottawa Beach, just north of the entrance to Holland Harbor. Shoaling rates and longshore sediment transport rates were calculated to supplement the beach volume change rates, with a sediment budget developed as the final product. This is a companion piece to the ERDC/TN RSM-26-1, Conceptual Sediment Budget Creation Using CorpsCam Imagery: Lynnhaven Inlet, Virginia.

24 Apr 2026

Conceptual Sediment Budget Creation Using CorpsCam Imagery: Lynnhaven Inlet, Virginia

Abstract: This Regional Sediment Management technical note (RSM TN) discusses the development of a conceptual sediment budget at Lynnhaven Inlet, Virginia, using CorpsCam imagery. Analysis of imagery collected between September 2022 and July 2024 is used to calculate the volume change along the beaches adjacent to the inlet. The final budget incorporates shoaling change rates and estimated longshore-sediment transport rates. This is a companion piece to the ERDC/TN RSM-26-2 Conceptual Sediment Budget Creation Using CorpsCam Imagery: Holland, Michigan.

20 Apr 2026

Multimethod Change-Detection Analysis Using Prithvi-EO-2.0: A Comparative Study of Traditional and Segmentation-Based Approaches for Vector Database Validation

Abstract: This technical note presents an evaluation of the performance of four change-detection methodologies, with a focus on validating and maintaining authoritative vector-feature databases using Earth observation data. In this study, we implemented traditional pixel-to-pixel change detection, feature-data-compliant segmentation, pixel-to-feature segmentation, and feature-to-pixel change detection, leveraging the Prithvi-EO-2.0 Vision Transformer model (Szwarcman et al. 2025), to analyze imagery from California’s Central Valley. The analysis of Sentinel-2 imagery from California’s Central Valley (in 2021–2023) demonstrated that there was a trade-off between sensitivity and reliability in the change-detection approaches: feature-to-feature methods achieved the highest sensitivity (0.637 average), while the feature-to-pixel approach provided the most reliable validation (0.280 average), exceeding the performance of traditional pixel-to-pixel methods (0.256 average).

15 Apr 2026

Satellite Image Quality Classification with ImageNet Transfer Learning and Data Fusion

Abstract: This Coastal and Hydraulics Engineering Tech Note (CHETN) documents the development of a convolutional neural network (CNN) to automate quality control on image classification, a process previously done by subject matter experts (SMEs), within the Littoral Zone Maneuver Support Tool (LZMST). LZMST was created to support rapid exploration of an unknown littoral region by analyzing global satellite data and wave and current models to best estimate the coastal conditions and help identify potential hazards. In support of this mission, images from Landsat-8 (Roy et al. 2014) and Sentinel-2a/2b (Drusch et al. 2012) are graded on their predicted usefulness for LZMST, which is usually done by expert selection. A CNN model is developed to automate this task, by utilizing transfer learning on a CNN using ImageNet (Krizhevsky et al. 2017) weights combined with a small data set of classifications from the CoastSat (Vos et al. 2019) python application. Because the expert selection of images is incredibly time consuming, the data set used to develop this tool was small (approximately 3,500 images), which can make creation of a data-driven algorithm difficult. This CHETN highlights the usefulness of using transfer learning to eliminate the need for large data sets and demonstrates that ImageNet weights can be successfully used to assist in quality detection on multispectral imagery from the Landsat-8 and Sentinel-2a/2b missions.

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.