15 Dec 2023

Ranking Ports by Vessel Demand for Depth

Abstract: The US Army Corps of Engineers (USACE) traditionally uses two metrics to evaluate the maintenance of coastal navigation projects: tonnage at the associated port (representing relative importance) and the controlling depth in the channel (representing operating condition). These are incorporated into a risk-based decision framework directing funds where channel conditions have deteriorated and the disrupted tonnage potential is the highest. However, these metrics fail to capture shipper demand for the maintained depth service provided by the USACE through dredging. Using automatic identification system (AIS) data, the USACE is pioneering new metrics describing vessel demand for the channel depth, represented by vessel encroachment volume (VEV). VEV describes the volume of the hull intruding into a specified clearance margin above the bed and captures how much vessels use the deepest portions of USACE-dredged channels. This study compares the VEV among 13 ports over 4 years by combining AIS, tidal elevations, channel surveys, and sailing draft. The ports are ranked based on the services demanded by their user base to inform the decision framework driving dredge funding allocations. Integrating demand for-depth metrics into the Harbor Maintenance Fee assessment and/or Trust Fund disbursements could alleviate the constitutionality concerns and several criticisms levied against Harbor Maintenance funding.

5 Sep 2023

Dining Facility Whole-Building Evaluation to Reduce Solid Waste: Opportunities and Best Practices for Optimization and Management of Food Waste

On military installations, an average of 1.2 pounds in food waste is dis-posed per person per day, accounting for 68% of dining facility (DFAC) refuse and 46% of the total installation refuse stream, making food waste the heaviest portion of installation solid waste. At a single installation, this can contribute up to 1.5 million dollars lost yearly from food waste alone. Department of Defense Instruction (DoDI) 4715.23 (DoD 2016) establishes policy and prescribes procedures to implement waste management through waste prevention and recycling. The US Army Installation Management Commands (IMCOM) installations have limited resources and limited personnel to study which source reduction methods are optimal to reduce food waste given their unique mission requirements. This study identifies opportunities for optimization and management of solid waste across IMCOM installations. Recycling is not enough to significantly reduce the economic or environmental costs to the DoD. Army installations pay over $100 million annually in disposal fees. Source reduction is emphasized in regulations but not prioritized in process modifications or technology solutions. Additionally, food waste contributes to excessive global greenhouse gas emissions, which affect global warming and climate change. A multitiered approach is necessary, placing more emphasis on source reduction advances and initiatives.

20 Apr 2020

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.