29 Jun 2026

Evaluation of Water Hyacinth (Eichhornia Crassipes) Response to Herbicides Using Unmanned Aerial System Imagery

Abstract: Water hyacinth is a highly invasive aquatic species in the southern United States that requires intensive management through frequent herbicide applications. Quantifying management success in large-scale operations is challenging with traditional survey methods that rely on boat-based teams and can be time-consuming and labor-intensive. In contrast, an unmanned aerial system (UAS) allows a single operator to survey a waterbody more efficiently and rapidly, enhancing both coverage and data collection. Therefore, the objective of this research was to develop remote sensing techniques to assess herbicide efficacy for water hyacinth control in an outdoor mesocosm study. Experiments were conducted in spring and summer 2023 to compare and correlate data from visual evaluations of herbicide efficacy against nine vegetation indices (VIs) derived from UAS-based red-green-blue imagery. Penoxsulam, carfentrazone, diquat, 2,4-D, florpyrauxifen-benzyl, and glyphosate were applied at two rates, and experimental units were evaluated for 6 wk. The carotenoid reflectance index (CRI) had the highest Spearman’s correlation coefficient with visually evaluated efficacy for 2,4-D, diquat, and florpyrauxifen benzyl (> −0.77). The visible atmospherically resistance index (VARI) had the highest correlation with carfentrazone and penoxsulam treatments (> −0.70), and the excess greenness minus redness index had the highest correlation for glyphosate treatments (> −0.83). CRI had the highest correlation coefficient with the most herbicide treatments, and it was the only VI tested that did not include the red band. These VIs were satisfactory predictors of mid-range visually evaluated herbicide efficacy values but were poorly correlated with extremely low and high values, corresponding to nontreated and necrotic plants. Future research should focus on applying findings to real-world (nonexperimental) field conditions and testing imagery with spectral bands beyond the visible range.

26 Jun 2026

Demonstration of a Remotely Operated Vehicle for Inspecting Holt Lock and Dam

Purpose: This report describes the US Army Engineer Research and Development Center–Environmental Laboratory (ERDC-EL), Robotic Characterization of Battlefield and Operational Environments (RCBOE) Team’s application of a small inspection-class remotely operated vehicle (ROV) to inspect underwater structures at the Holt Lock and Dam located near Tuscaloosa, Alabama.

12 Jun 2026

Automated Snow Cover Detection on Mountain Glaciers Using Spaceborne Imagery and Machine Learning

Abstract: Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit over-all accuracies of 92 %–98 %, κ scores of 84 %–96 %, and F scores of 93 %–98 % for all image products. The median difference between automatically and manually delineated median snow line altitudes is −31 m across all image products. The Sentinel-2 classifier produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and firn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal the timing of minimum snow cover conditions varies by up to a month between Arctic and midlatitude sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.

12 Jun 2026

Spatiotemporal Patterns of Accumulation and Surface Roughness in Interior Greenland with a GNSS-IR Network

Abstract: The dry-snow zone is the largest region of the Greenland Ice Sheet, yet temporally and spatially dense observations of surface accumulation and surface roughness in this area are lacking. We use the global navigation satellite system interferometric reflectometry (GNSS-IR) technique with a novel, low-cost GNSS network of 12 stations in the vicinity of the ice sheet summit to reveal temporal and spatial patterns of accumulation of the upper snow layer. We show that individual measurements are highly precise, while the aggregate of hundreds of daily measurements across a large spatial footprint can detect millimeter-level surface changes and is biased by −2.7 ± 3.0 cm com-pared to a unique validation data set that covers a similar spatial extent to the instrument sensing footprint. Using the validation data set, we find that the reflectometry technique is most sensitive to the surrounding 4–20 m of the surface, with the GNSS antenna at a height of 1–2 m above ground level. Along with an exceptionally high accumulation rate at the beginning of the study, we also detect an across-slope dependence in accumulation rates at yearly timescales. For the first time, we also validate GNSS-IR sensitivity to meter-scale surface heterogeneities such as sastrugi, and we construct a time series of surface roughness evolution that suggests a seasonal pattern of heightened wintertime roughness features in this region. These surface accumulation and rough-ness measurements provide a novel data set for these critical variables and show a statistically significant relationship with occurrences of both high winds and precipitation events but only moderate correlations, suggesting that other processes may also contribute to accumulation and enhanced surface roughness in the interior region of Greenland.

15 May 2026

Application of Rapid Response Reporting Tools to Improve Harmful Algal Bloom Management: US Army Corps of Engineers (USACE)–Omaha District

Abstract: Harmful algal blooms (HABs) pose significant threats to critical water resources, including potable water supply, fish and wildlife propagation, recreation, and overall water quality, managed by the US Army Corps of Engineers (USACE). To address these challenges, USACE needs innovative technologies that can enhance monitoring and management across the diverse portfolio of inland waterbodies they oversee. This technical report presents a case study from the Omaha District in which open-source software (R), satellite imagery, and traditional water quality parameters were integrated to produce near-real-time reports to improve HAB monitoring and management. The approach enabled timely identification of the areas most susceptible to HABs and provided actionable data to inform management strategies, such as hypolimnetic withdrawal, and other management actions. The findings demonstrate that combining remote sensing with open-source analytics can serve as a proof of concept for improving the efficiency of HAB monitoring programs. Ultimately, these tools facilitate more responsive decision-making by reducing resource demands and establishing a foundation for broader adoption of open-source tools in HAB management across USACE districts.

13 May 2026

Processing and Optimization of Global Land Ice Measurements from Space (GLIMS) Glacier Polygon Shapefiles for Army Geospatial Data Model Integration

Abstract: This technical note documents the methodology used to prepare glacier polygon datasets from the Global Land Ice Measurements from Space (GLIMS) database for integration into Army geospatial workflows. The Army Geospatial Data Model contains a feature class within the GGDM (Ground-Warfighter Geospatial Data Model) for permanent snow, defined operationally as snow persisting on the ground for more than two years. However, in cryospheric science, snow that persists across multiple accumulation seasons transitions into firn and ultimately becomes glacial ice. Thus, most “permanent snow” surfaces are more accurately classified as permanent ice, and GGDM does not currently contain a dedicated feature class representing this land-surface category. The GLIMS database provides authoritative, globally maintained glacier and perennial ice extents, making it ideally suited to fill this structural gap in the GGDM schema. The purpose of this work is to (1) transform raw GLIMS glacier polygons into a clean, nonoverlapping, attribute-free dataset; (2) standardize the geometry for compatibility with GGDM; and (3) establish a US Army Engineer Research and Development Center (ERDC)–compliant workflow for maintaining a credible representation of global permanent ice surfaces.

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).