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  • Channel Assessment Tools for Rapid Watershed Assessment

    Purpose: Existing Delta Headwaters Project (DHP) watershed stabilization studies are focused on restoration and stabilization of degraded stream systems. The original watershed studies formerly under the Demonstration Erosion Control (DEC) Project started in the mid 1980s. The watershed stabilization activities are continuing, and because of the vast number of degraded watersheds and limited amount of yearly funding, there is a need for developing a rapid watershed assessment approach to determine which watersheds to prioritize for further work. The goal of this project is to test the FluvialGeomorph (FG) toolkit to determine if the Rapid Geomorphic Assessment approach can identify channel stability trends in Campbell Creek and its main tributary. The FG toolkit (Haring et al. 2019; Haring et al. 2020) is a new rapid watershed assessment approach using high-resolution terrain data (Light Detection and Ranging [LiDAR]) to support U.S. Army Corps of Engineers (USACE) watershed planning. One of the principal goals of the USACE SMART (Specific Measureable Attainable Risk-Informed Timely) Planning is to leverage existing data and resources to complete studies. The FG approach uses existing LiDAR to rapidly assess either reach-specific analysis for smaller more focused studies or larger watersheds or ecosystems. The rapid assessment capability can reduce the time and cost of planning by using existing information to complete a preliminary watershed assessment and provide rapid results regarding where to focus more detailed study efforts.
  • Monitoring Ecological Restoration with Imagery Tools (MERIT): Python-based Decision Support Tools Integrated into ArcGIS for Satellite and UAS Image Processing, Analysis, and Classification

    Abstract: Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
  • Automated Terrain Classification for Vehicle Mobility in Off-Road Conditions

    ABSTRACT:  The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be informed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
  • Utilizing Data from the NOAA National Data Buoy Center

    Purpose: This Coastal and Hydraulics Engineering Technical Note (CHETN) guides users through the quality control (QC) and processing steps that are necessary when using archived U.S. National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) wave and meteorological data. This CHETN summarizes methodologies to geographically clean and QC NDBC measurement data for use by the U.S. Army Corps of Engineers (USACE) user community.
  • First Generation Automated Assessment of Airfield Damage from LiDAR Point Clouds

    Abstract: This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.
  • Demonstration of Autonomous Aerial Acoustic Recording Systems to Inventory Department of Defense Bird Populations

    Abstract: This demonstration project addressed the Department of Defense need for innovative technology for monitoring avian populations in inaccessible areas. This report presents results from field validation tests for an autonomous aerial acoustic recording system, a helium-filled weather balloon that transported an instrument payload over inaccessible areas (e.g., ordnance impact areas) to record avian vocalizations.
  • 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.
  • Raster-Based Floristic Quality Index: Proof of Concept

    Purpose: The purpose of this study was to develop and demonstrate a raster-based floristic quality index (FQIraster) as a proof of concept. This raster-based approach leverages many of the advantages of high spatial, spectral, and temporal resolution space-borne imagery as well as established remote sensing techniques (vegetation indices and feature classification) to provide rapid measures of vegetation productivity and biodiversity. The developed method should provide researchers and managers a new tool for quantifying and tracking the condition, response, and recovery of expansive wetland landscapes.
  • 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: Spatiotemporally coherent tensor decompositions for the analysis of trajectory data By Trevor Ruiz and Charlotte Ellison

    Abstract: Location acquisition technologies such as global positioning systems (GPS) sensors or telemetry devices generate abundant spatiotemporal measurements of movement of people, animals, and vehicles. The resultant data represent trajectories-paths in space and time traversed by moving objects- and can often be merged with additional information about the entities in motion from connected or external data sources (Zheng 2015). New data analysis frameworks may be able to uncover patterns of human behavior from the fused trajectory and contextual i information. This data and new insights gained from novel analysis tools are p potentially of great interest to the Army and the geospatial community.