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  • Evaluation of Unmanned Aircraft System Coastal Data Collection and Horizontal Accuracy: A Case Study at Garden City Beach, South Carolina

    Abstract: The US Army Corps of Engineers (USACE) aims to evaluate unmanned aircraft system (UAS) technology to support flood risk management applications, examining data collection and processing methods and exploring potential for coastal capabilities. Foundational evaluation of the technology is critical for understanding data application and determining best practices for data collection and processing. This study demonstrated UAS Multispectral (MS) and Red Green Blue (RGB) image efficacy for coastal monitoring using Garden City Beach, South Carolina, as a case study. Relative impacts to horizontal accuracy were evaluated under varying field scenarios (flying altitude, viewing angle, and use of onboard Real-Time Kinematic–Global Positioning System), level of commercial off-the-shelf software processing precision (default optimal versus high or low levels) and processing time, and number of ground control points applied during postprocessing (default number versus additional points). Many data sets met the minimum horizontal accuracy requirements designated by USACE Engineering Manual 2015. Data collection and processing methods highlight procedures resulting in high resolution UAS MS and RGB imagery that meets a variety of USACE project monitoring needs for site plans, beach renourishment and hurricane protection projects, project conditions, planning and feasibility studies, floodplain mapping, water quality analysis, flood control studies, emergency management, and ecosystem restoration.
  • PUBLICATION NOTICE: Autonomous QUerying And PATHogen Threat Agent Sensor System (AQUA PATH): Monitoring Source Waters with Geospatially Wirelessly Networked Distributed Sensing Systems

    Abstract: Contaminants serve as health risks to recreational water, potable water, and marine life that result in undocumented effects on population exposure. In many areas of the world, the concern lies in contaminated drinking water, which would immediately effect social and economic order. As research advances for innovative solutions, the deployment of automated systems for source water monitoring could reduce the risk of exposure. Water quality monitoring typically involves sample collection and analyses that are performed in a laboratory setting. These results are normally presented after an 18−48 hr period. This report details the prototyped Autonomous QUerying And PATHogen threat agent sensor (AQUA PATH) geoenabled system that is able to detect the presence/absence of pathogenic bacteria indicators in source waters and report these values in the field, in less than 30 minutes. The AQUA PATH system establishes rapid field data collection and reports assessment of source waters bacterial loads at near shore inner coastal locations, which makes a leap forward compared to current presence/absence tests standards established by the EPA.
  • PUBLICATION NOTICE: Seamless Integration of Lidar-Derived Volumes and Geomorphic Features into the Sediment Budget Analysis System

    Abstract: This Regional Sediment Management Technical Note provides a workflow and case study documenting the process to integrate lidar-derived volume changes and changes quantified from geomorphic features into the Sediment Budget Analysis System. Sediment budgets provide an understanding of a region’s sediment sources, project needs, processes, data gaps, engineering actions, and ecological considerations. Elevation data from profiles or lidar, sediment characteristics, dredging and placement information, along with other coastal datasets, are used to understand sediment pathways and develop sediment budgets for a region. Workflows and tools have been updated or modified to integrate sediment budget tools, volume change tools, and remote sensing data for the creation of comprehensive regional sediment budgets. 
  • PUBLICATION NOTIFICATION: Local Spatial Dispersion for Multiscale Modeling of Geospatial Data: Exploring Dispersion Measures to Determine Optimal Raster Data Sample Sizes

    ABSTRACT: Scale, or spatial resolution, plays a key role in interpreting the spatial structure of remote sensing imagery or other geospatially dependent data. These data are provided at various spatial scales. Determination of an optimal sample or pixel size can benefit geospatial models and environmental algorithms for information extraction that require multiple datasets at different resolutions. To address this, an analysis was conducted of multiple scale factors of spatial resolution to determine an optimal sample size for a geospatial dataset. Under the NET-CMO project at ERDC-GRL, a new approach was developed and implemented for determining optimal pixel sizes for images with disparate and heterogeneous spatial structure. The application of local spatial dispersion was investigated as a three-dimensional function to be optimized in a resampled image space. Images were resampled to progressively coarser spatial resolutions and stacked to create an image space within which pixel-level maxima of dispersion was mapped. A weighted mean of dispersion and sample sizes associated with the set of local maxima was calculated to determine a single optimal sample size for an image or dataset. This size best represents the spatial structure present in the data and is optimal for further geospatial modeling.
  • PUBLICATION NOTICE: Web-Based Monitoring of Piezometers for the U.S. Army Corps of Engineers Moose Creek Dam, North Pole, Alaska

    The Moose Creek Dam, completed in 1979, is a 7.5-mile long earthen dam located in North Pole, Alaska designed to protect the Fairbanks and North Pole areas from flooding by regulating flow on the Chena River. The earth-filled embankment and a concrete control structure regulates flow with four gated bays. In 2014, two minor flood events took place within 1 month of each other.

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