19 Sep 2024

The Arctic Deployable Resilient Installation Water Purification and Treatment System (DRIPS): Microgrid Integration with Geoenabled Water Production and Disinfection Systems for Installations

Abstract: The purpose of the Arctic Deployable Resilient Installation water Purification and treatment System (DRIPS) is to be a critical asset in disaster response and military operations by providing a reliable and effective means of producing potable water and disinfection in a challenging and unpredictable environment, such as in an extremely cold climate. The objective of this effort was to deliver, integrate, and demonstrate the Arctic DRIPS to show that it can provide drinkable water to users of the microgrid within polar climate zones. Its adaptability, mobility, and comprehensive water treatment capabilities make it an invaluable resource for addressing water-related emergencies and water disruptions and for sustaining critical missions. It also addresses a point of need by improving the ability to meet demands while reducing convoy requirements and the logistical foot-print and ensuring the well-being of affected installations during disaster responses, training operations, normal water disruptions, and emergency preparation. The DRIPS was delivered to Fort Wainwright, a sub-Arctic installation, to demonstrate the integration of a water treatment component within a microgrid structure and to help them be better prepared to meet their water and energy requirement goals. The microgrid integration requirements were met upon implementation of this project.

19 Sep 2024

Case Study of Continental-Scale Hydrologic Modeling’s Ability to Predict Daily Streamflow Percentiles for Regulatory Application

Abstract: Regulatory practitioners use hydroclimatic data to provide context to observations typically collected through field site visits and aerial imagery analysis. In the absence of site-specific data, regulatory practitioners must use proxy hydroclimatic data and models to assess a stream's hydroclimatology. One intent of current-generation continental-scale hydrologic models is to provide such hydrologic context to ungaged watersheds. In this study, the ability of two state-of-the-art, operational, continental-scale hydrologic modeling frameworks, the National Water Model and the Group on Earth Observation Global Water Sustainability (GEOGloWS) European Centre for Medium-Range Weather Forecasts (ECMWF) Streamflow Model, to produce daily streamflow percentiles and categorical estimates of the streamflow normalcy was examined. The modeled stream-flow percentiles were compared to observed daily streamflow percentiles at four United States Geological Survey stream gages. The model's performance was then compared to a baseline assessment methodology, the Antecedent Precipitation Tool. Results indicated that, when compared to baseline assessment techniques, the accuracy of the National Water Model (NWM) or GEOGloWS ECMWF Streamflow Model was greater than the accuracy of the baseline assessment methodology at four stream gage locations. The NWM performed best at three of the four gages. This work highlighted a novel application of current-generation continental-scale hydrologic models.

19 Sep 2024

Automated Built-Up Infrastructure Land Cover Extraction Using Index Ensembles with Machine Learning, Automated Training Data, and Red Band Texture Layers

Abstract: Automated built-up infrastructure classification is a global need for planning. However, in-dividual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.

18 Sep 2024

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

Abstract: A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global and regional adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class, six-class, and five-class schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies. The results highlight performance in line with an intensive deep learning approach, and reasonably accurate models created without a full annual time series of imagery.

13 Sep 2024

Optimization Strategies for Geospatial Data on End-User Devices

Abstract: The ability to quickly disseminate geospatial data across all echelons, particularly those at the tactical edge, is critical to meeting threats described by the Multi-Domain Operations doctrine. The US Army Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL), is researching the optimization of the formats, data models, file sizes, and quality of geospatial products to be exploited by end-user devices (EUDs). This report describes a processing methodology comprising custom software and open-source tools to optimize Army Geospatial Enterprise Standard Sharable Geospatial Foundation and industry-accepted products for exploitation on EUDs. The Integrated Visual Augmentation System (IVAS) was emphasized, but other devices, including the Nett Warrior and Program Executive Office—Soldier targeting systems, were also studied. Additionally, we developed a compression methodology that reduced the size of three-dimensional model data by a factor of 9 without a loss in data quality. A summary of the results describes steps to address remaining technical issues and considers future efforts to further optimize geospatial data for additional EUDs and tactical applications.

12 Sep 2024

Establishing a Selection of Dust Event Case Studies for Regions in the Global South

Abstract: Airborne dust is an essential component of climatological and biogeochemical processes. Blowing dust can adversely affect agriculture, transportation, air quality, sensor performance, and human health. Therefore, the accurate characterization and forecasting of dust events is a priority for air quality researchers and operational weather centers. While dust detection and prediction capabilities have evolved over the preceding decades, the weather modeling community must continue to improve the location and timing of individual dust event fore-casts, especially for extreme dust outbreaks. Accordingly, Researchers at the US Army Engineer Research and Development Center (ERDC) are establishing a series of reference case study events to enhance dust transport model development and evaluation. These case studies support ongoing research to increase the accuracy of simulated dust emissions, dust aerosol transport, and dust-induced hazardous air quality conditions. This report documents five new contributions to the reference inventory, including detailed assessments of dust storms from three regions with differing meteorological forcing regimes. Here, we examine two extreme dust episodes that affected India, a multiday berg wind event in southern Africa, a strong but short-lived dust plume from the Atacama Desert of Chile, and a narrow, isolated dust plume emanating from a dry lake bed in Patagonia.

11 Sep 2024

Terrestrial Vision-Based Localization Using Synthetic Horizons

Abstract: Vision-based localization could improve navigation and routing solutions in GPS-denied environments. In this study, data from a Carnegie Robotics MultiSense S7 stereo camera were matched to a synthetic horizon derived from foundation sources using novel two-dimensional correlation techniques. Testing was conducted at multiple observation locations over known ground control points (GCPs) at the US Army Engineer Research and Development Center (ERDC), Geospatial Research Laboratory (GRL), Corbin Research Facility. Testing was conducted at several different observational azimuths for these locations to account for the many possible viewing angles in a scene. Multiple observational azimuths were also tested together to see how the amount of viewing angles affected results. These initial tests were conducted to help future efforts testing the S7 camera under more realistic conditions, in different environments, and while expanding the collection and processing methodologies to additional sensor systems.

9 Sep 2024

Leveraging Artificial Intelligence and Machine Learning (AI/ML) for Levee Culvert Inspections in USACE Flood Control Systems (FCS)

Abstract: Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic plat-form and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.

21 Jun 2024

Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood

Abstract: This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to US$61 million of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the US Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data.

20 Jun 2024

Spatial Variations in Vegetation Fires and Emissions in South and Southeast Asia during COVID-19 and Pre-pandemic

Abstract: Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, few studies focused on vegetation fires and the changes during COVID compared to pre pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations. Results from the short term 2020 COVID versus 2019 non COVID year showed a decline in fire counts varying from -2.88 to 79.43%. The exceptions in South Asia include Afghanistan and Sri Lanka, and Cambodia and Myanmar in Southeast Asia. The burnt area decline for 2020 compared to 2019 varied from -0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012–2020 pre pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from -0.24 to -1, although most countries experienced a decrease in fire counts. The study highlights variations in fires and emissions useful for fire management and mitigation.