31 Aug 2026

Streamlining Coastal Restoration Monitoring: A US Army Corps of Engineers Practitioner’s Guide to Metric Prioritization and Field Versus Remote Sensing Approaches

Abstract: Despite the increasing availability of remote sensing technologies, environmental monitoring methodologies remain inconsistent for coastal restoration projects, and this limits the ability to accurately compare performance and benefits between restoration types. This technical report addresses these challenges by systematically prioritizing coastal monitoring metrics and summarizing traditional field methods against emerging remote sensing technologies to support streamlined monitoring decisions for coastal ecosystem restoration projects. Through a targeted review of 375 monitoring metrics across 15 coastal restoration types and a structured workshop with nine US Army Corps of Engineer (USACE) restoration practitioners representing eight districts/divisions, this study identified 44 commonly referenced metrics and prioritized 16 high-ranking metrics based on project familiarity, logistics, regulatory drivers, and forecasting utility. The prioritized metrics, focusing on flora/habitat and geomorphological parameters, were then used to form the basis of a coastal ecosystem restoration monitoring case study using both traditional ground-based methods (real-time kinematic [RTK]-GPS surveys, vegetation plots) and advanced remote sensing technologies (uncrewed aircraft system [UAS]–mounted lidar, multispectral imagery). This technical report summarizes and synthesizes key findings from the workshop and case study and provides decision-making frameworks for optimizing the trade-offs of traditional and emerging monitoring technologies based on specific project objectives.

24 Aug 2026

Evaluating Snow Depth Retrievals from Sentinel-1 Volume Scattering over NASA SnowEx Sites

Abstract: Snow depth retrievals from spaceborne C-band synthetic aperture radar backscatter have the potential to fill an important gap in the remote monitoring of sea-sonal snow. Sentinel-1 SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more re-search into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.

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.

11 Aug 2026

Continental-scale mapping of forest tree density in North America using remote sensing and deep learning with uncertainty quantification

Abstract: Accurate, spatially consistent estimates of tree density remain elusive at continental scales, limiting our ability to assess forest structure, carbon stocks, and biodiversity. Existing global assessments have relied on simplified statistical models and sparse, heterogeneous ground data that are insufficient to capture nonlinear ecological interactions and spatial variability. To address these limitations, we integrated more than 600,000 harmonized ground-based forest inventory plots with satellite-derived vegetation indices, climate surfaces, soil properties, and topographic covariates to develop a deep learning framework for high-resolution mapping of tree density across North America. We evaluated four modeling approaches—generalized linear models, ridge regression, random forest, and a feedforward neural network. Among all models tested, the FFNN achieved the highest predictive accuracy, and was used to produce a wall-to-wall tree density map at 3 km resolution for the continent. We estimated that the total number of forest trees with diameter at breast height ≥ 10 cm across North America ranges from 339 to 514 billion, substantially lower than the widely cited estimate of 603 billion trees reported by Crowther et al. (2015). When smaller stems were included, totals more than doubled, reaching 738 billion to 1.12 trillion trees. We quantified uncertainty using Monte Carlo Dropout, generating pixel-level error estimates and confidence intervals. Spatial patterns reveal high tree densities in boreal and temperate forests, intermediate densities in mixed broadleaf regions, and relatively low densities in deserts, Mediterranean systems, and tundra. Compared to the global GLM-based benchmark by Crowther et al. (2015), our deep learning framework achieves markedly higher predictive accuracy, aligns more closely with national forest inventory statistics, and provides explicit uncertainty quantification, supporting applications in carbon accounting, biodiversity modeling, and ecosystem monitoring at scales through region specific calibration and validation.

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.