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.

20 Aug 2025

Examining the Impact of the 2007 Zaca Fire on the Long-Term Hydrological Recovery of the Santa Cruz Creek Watershed in Southern California

Abstract: This study focuses on the Santa Cruz Creek watershed in Southern California, an area severely impacted by the 2007 Zaca Fire. The region is representative of wildfire-prone Mediterranean-climate catchments. We assess long-term post-fire hydrological recovery using a novel dual approach: (1) simulating 16 storm events over a 23-year period to evaluate pre-fire, post-fire, and recovery conditions, and (2) directly comparing two similar storm events—one pre-fire and one during recovery—to isolate changes in watershed response. Hydrological modeling employed HEC-HMS with the Deficit and Constant Loss Method, the ModClark Transform Model, and the Linear Reservoir Baseflow Model. Remote sensing data, including Enhanced Vegetation Index and SERVES Soil Moisture, enhanced modeling and analysis. Vegetation cover, soil moisture, and several watershed parameters show substantial recovery after five years. EVI reached 84 % of pre-fire values, while initial soil moisture deficit, time of concentration, and storage coefficient each recovered to roughly 70 %. Fast baseflow exceeded pre-fire levels at 143 %, but slow baseflow declined to 20 %. Peak discharge and direct runoff volume declined from post-fire highs of 173 % and 136 % to 125 % and 84 % of pre-fire levels, respectively. Although vegetative conditions stabilize, watershed hydrology remains altered.

20 Aug 2025

Trade-offs Between Field and Remote Geomorphic Monitoring of Coastal Marsh Restoration Sites

Abstract: Coastal marsh restoration presents geomorphic monitoring challenges because these sites are often remote or inaccessible, and time and financial resources for field data may be limited. Yet, elevation and shoreline characteristics contribute to the overall health and longevity of coastal marshes. The expansion of Uncrewed Aircraft System (UAS) technology and new satellite platforms offer opportunities to complement ground-based geomorphic monitoring and overcome the challenges of traditional field methods. Here, we compare field-based and remote-sensing approaches to monitor two restored coastal wetlands in Louisiana. At Spanish Pass, methods for measuring site elevation, shoreline position, and shoreline geomorphic types were compared. Ground surveys strongly correlated with UAS-lidar digital elevation model (DEM) elevations (R2 = 0.97. UAS and satellite imagery were accurate to within 3 meters of field-shoreline positions, and UAS-lidar-derived shorelines had the lowest error. At LaBranche, UAS-lidar DEM data were paired with airborne lidar and legacy ground surveys to track temporal changes in elevation, indicating minimal elevation change. The study demonstrates the accuracy and utility of satellite and UAS remote sensing for monitoring shoreline positions and elevations but notes that shoreline classifications could be improved with additional quantification. These findings help practitioners assess the trade-offs and benefits of various monitoring methods.

2 Jul 2025

Remote Detection of Soil Shear Strength in Arctic and Subarctic Environments

Abstract: Soil shear strength affects many military activities and is affected significantly by plant roots. Unfortunately, root contribution to soil shear strength is difficult to measure and predict. In the boreal forest ecosystem, soil and hydrologic dynamics make soil shear strength less predictable, while the need for prediction grows due to the rapid changes occurring in this environment. Our current study objectives are to (1) observe possible aboveground vegetation indicators of soil shear strength variation across soils and other environmental heterogeneity, (2) observe possible image-based indicators of soil shear strength variation, and (3) identify the best remote-sensing data source for predicting soil shear strength variation. A total of 65 sites were sampled from a diversity of soil and vegetation types across interior Alaska and Ontario, Canada. Ground-collected data were analyzed to develop a predictive model, while a similar approach was undertaken with Sentinel-2 imagery. Results indicate that both ground-collected data and satellite imagery can reasonably predict boreal forest soil shear strength, with satellite imagery providing the higher predictive ability. A comparison of 10 m Sentinel-2 and submeter Maxar imagery indicated that Sentinel-2 provides a better prediction of soil shear strength.