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ERDC Library Catalog

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  • Evaluation of Unmanned Aircraft Systems for Flood Risk Management: Results of Terrain and Structure Assessments

    Abstract: The 2017 Duck Unmanned Aircraft Systems (UAS) Pilot Experiment was conducted by the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory, Field Research Facility (FRF), to assess the potential for different UAS to support US Army Corps of Engineers coastal and flood risk management. By involving participants from multiple ERDC laboratories, federal agencies, academia, and private industry, the work unit leads were able to leverage assets, resources, and expertise to assess data from multiple UAS. This report compares datasets from several UAS to assess their potential to survey and observe coastal terrain and structures. In this report, UAS data product accuracy was analyzed within the context of three potential applications: (1) general coastal terrain survey accuracy across the FRF property; (2) small-scale feature detection and observation within the experiment infrastructure area; and (3) accuracy for surveying coastal foredunes. The report concludes by presenting tradeoffs between UAS accuracy and the cost to operate to aid in selection of the best UAS for a particular task. While the technology and exact UAS models vary through time, the lessons learned from this study illustrate that UAS are available at a variety of costs to satisfy varying coastal management data needs.
  • A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing

    Abstract: Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.
  • Data Collection Tools for River Geomorphology Studies: LiDAR and Traditional Methods

    Abstract: The purpose of this review is to highlight LiDAR data usage for geomorphic studies and compare to other remote sensing technologies. This review further identifies survey efficiencies and issues that can be problematic in using LiDAR digital elevation models (DEMs) in completing surveys and geomorphic analysis. US Army Corps of Engineers (USACE) geospatial data collection guidance (EM 1110-1-1000) (USACE 2015) aligns with the American Society for Photogrammetry and Remote Sensing Positional Accuracy Standards for Digital Geospatial Data (ASPRS 2014). Geomorphic assessment technologies are rapidly evolving, and LiDAR data collection methods are at the forefront. The FluvialGeomorph (FG) toolbox, developed to support USACE watershed planning, is a recent example of the use of LiDAR high-resolution terrain data to provide a new, efficient approach for rapid watershed assessments (Haring et al. 2020; Haring and Biedenharn 2021). However, there are advantages and disadvantages in using LiDAR data compared to other remote sensing technologies and traditional topographic field survey methods.
  • Joint Chilean and US Mobility Testing in Extreme Environments

    Abstract: Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist with terrain identification and overall vehicle mobility. Analysis of winter vehicle/sensor data collected in collaboration with the Chilean Army in Lonquimay, Chile during July and August 2019 will be discussed in this report.
  • Evaluating Drone Truthing as an Alternative to Ground Truthing: An Example with Wetland Plant Identification

    Purpose: Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
  • Characterizing Snow Surface Properties Using Airborne Hyperspectral Imagery for Autonomous Winter Mobility

    Abstract: With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aerial Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A Pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
  • Reproducibility Assessment and Uncertainty Quantification in Subjective Dust Source Mapping

    Abstract: Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
  • Simultaneous Mapping of Coastal Topography and Bathymetry from a Lightweight Multicamera UAS

    Abstract: A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long d Coastal mapping, multiview stereo (MVS), nearshore morphology, remote sensing, structure from motion (SfM), Unmanned Aircraft Systems (UAS)well timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
  • Remotely Sensed Habitat Assessment of Bottomland Hardwood and Swamp Habitat: West Shore Lake Pontchartrain Hurricane Storm Damage Risk Reduction System Potential Impact Area

    Purpose: This study used remote sensing techniques to identify and assess the current condition of bottomland hardwood (BLH) and swamp habitats within the West Shore Lake Pontchartrain (WSLP) hurricane storm-damage risk reduction system (HSDRRS) project area. This effort provides baseline knowledge of the location and quality of these habitats prior to the construction of the WSLP HSDRRS project. The resultant products will assist the USACE—New Orleans District (MVN) by informing ecosystem decision-making related to environmental assessments.
  • Methodology for Remote Assessment of Pavement Distresses from Point Cloud Analysis

    Abstract: The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.