25 Sep 2024

Comparing the Thermal Infrared Signatures of Shallow Buried Objects and Disturbed Soil

Abstract: The alteration of physical and thermal properties of native soil during object burial produces a signature that can be detected using thermal infrared (IR) imagery. This study explores the thermal signature of disturbed soil compared to buried objects of different compositions (e.g., metal and plastic) buried 5 cm below ground surface (bgs) to better understand the mechanisms by which soil disturbance can impact the performance of aided target detection and recognition (AiTD/R). IR imagery recorded every five minutes were coupled with meteorological data recorded on 15-minute intervals from 1 July to 31 October 2022 to compare the diurnal and long-term fluctuations in raw radiance within a 25 × 25 pixel area of interest (AOI) above each target. This study examined the diurnal pattern of the thermal signature under several varying environmental conditions. Results showed that surface effects from soil disturbance increased the raw radiance of the AOI, strengthening the contrast between the object and background soil for several weeks after object burial. Enhancement of the thermal signature may lead to expanded windows of object visibility. Target age was identified as an important element in the development of training data sets for machine learning (ML) classification algorithms.

15 Aug 2022

Thermal Infra-Red Comparison Study of Buried Objects between Humid and Desert Test Beds

Abstract: This study pertains to the thermal variations caused by buried objects and their ramifications on soil phenomenology. A multitude of environmental conditions were investigated to observe the effect on thermal infrared sensor performance and detection capabilities. Correlations between these external variables and sensor contrast metrics enable determinable key factors responsible for sensor degradation. This document consists of two parts. The first part is a summary of data collected by the U.S. Army Corps of Engineers, Engineer and Research and Development Center Cold Regions Research and Engineering Laboratory (ERDC-CRREL), ERDC-Geotechnical Structures Laboratory, and Desert Research Institute at the Yuma Proving Ground (YPG) site in February 2020 and observations from this activity. The second part is a comparison of target visibility between data collected at YPG and data collected at the ERDC-CRREL test site in 2018.

15 Aug 2022

Identification and Preventative Treatment of Overwintering Cyanobacteria in Sediments: A Literature Review

Abstract: Freshwaters can experience growths of toxin-producing cyanobacteria or harmful algal blooms (HABs). HAB-producing cyanobacteria can develop akinetes, which are thick-enveloped quiescent cells akin to seeds in vascular plants or quiescent colonies that overwinter in sediment. Overwintering cells produce viable “seed beds” for HAB resurgences and preventative treatments may diminish HAB intensity. The purpose of this literature review was to identify (1) environmental factors triggering germination and growth of overwintering cells, (2) sampling, identification, and enumeration methods, and (3) feasibility of preventative algaecide treatments. Conditions triggering akinete germination (light ≥0.5 µmol m-2s-1, temperature 22-27℃) differ from conditions triggering overwintering Microcystis growth (temperature 15-30℃, nutrients, mixing). Corers or dredges are used to collect surficial (0-2 cm) sediment layers containing overwintering cells. Identification and enumeration via microscopy are aided by dilution, sieving, or density separation of sediment. Grow-out studies simulate environmental conditions triggering cell growth and provide evidence of overwintering cell viability. Lines of evidence supporting algaecide efficacy for preventative treatments include (1) field studies demonstrating scalability and efficacy of algaecides against benthic algae, (2) data suggesting similar sensitivities of overwintering and planktonic Microcystis cells to a peroxide algaecide, and (3) a mesocosm study demonstrating a decrease in HAB severity following preventative treatments. This review informs data needs, monitoring techniques, and potential efficacy of algaecides for preventative treatments of overwintering cells.

2 Mar 2022

Modernizing Environmental Signature Physics for Target Detection—Phase 3

Abstract: The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

22 Jul 2021

Modernizing Environmental Signature Physics for Target Detection

Abstract: The objective of this study was to determine the effect of environmental phenomonology on the ability to detect buried objects and to provide a predictive capability of when targets are best detectable with IR sensors. Jay Clausen presented this material at the ERDC RD20 Conference.

1 Oct 2020

STE Environmental Manager (STEEM) Demonstration Web Application

Abstract: This report provides a summary of the development of the Synthetic Training Environment (STE) Environmental Manager (STEEM) demonstration web application. The purpose of this web application is twofold: (1) demonstrate a web application that enables non-technical users to prepare, run, and manage the physics-based models used by the STE to simulate realistic environmental conditions and (2) show how technologies developed by the Engineered Resilient Systems (ERS) Research and Development Area can be used to rapidly create applications to support U.S. Army Engineer Research and Development Center (ERDC) programs like the STE. A full build-out of STEEM would leverage the following ERS-developed technologies: data services, model development environment tools, model coupling/interface API, simulation workflow manager, and scenario generation tools.

20 Aug 2020

PUBLICATION NOTICE: Spatial and Temporal Variance in the Thermal Response of Buried Objects

ABSTRACT:  Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center’s Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness.