Miao X.,Missouri State University |
Miao X.,University of Texas at San Antonio |
Xie H.,University of Texas at San Antonio |
Ackley S.F.,University of Texas at San Antonio |
And 3 more authors.
Cold Regions Science and Technology | Year: 2015
High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice and melt ponds is time-consuming and labor-intensive. In this study, an object-based classification algorithm is developed to automatically extract sea ice and melt ponds efficiently from 163 aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the Arctic Pacific Sector. The photographs are selected from 599 cloud-free photographs based on their image quality and representativeness in the marginal ice zone (MIZ). The algorithm includes three major steps: (1) the image segmentation groups the neighboring pixels into objects according to the similarity of spectral and textural information; (2) the random forest ensemble classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice along ice edges), shadow, and ice/snow; and (3) the polygon neighbor analysis further separates melt ponds and submerged ice from the GSI according to their spatial relationships. The overall classification accuracy for the four general classes is 95.5% based on 178 ground reference objects. Furthermore, the producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection through 98 independent reference objects. For the 163 photos examined, a total of 19,438 melt ponds larger than 1m2 are detected, with a pond density of 867.2km-2, mean pond size of 32.6±0.03m2, and mean pond fraction of 0.06±0.006; a total of 42,468 ice floes are detected, with the mean floe size of 173.3±0.1m2 (majority in 1-30m2) and mean ice concentration of 46.1±0.5% (ranging from 18.6-98.6%). These results matched well with ship-based visual observations in the MIZ in the same area and time. The method presented in the paper can be applied to data sets of high spatial resolution Arctic sea ice photographs for deriving detailed sea ice concentration, floe size, and melt pond distributions over wider regions, and extracting sea ice physical parameters and their corresponding changes between years. © 2015 Elsevier B.V.
Perovich D.K.,CRREL |
Perovich D.K.,Dartmouth College |
Geophysical Research Letters | Year: 2012
There is an ongoing shift in the Arctic sea ice cover from multiyear ice to seasonal ice. Here we examine the impact of this shift on sea ice albedo. Our analysis of observations from four years of field experiments indicates that seasonal ice undergoes an albedo evolution with seven phases; cold snow, melting snow, pond formation, pond drainage, pond evolution, open water, and freezeup. Once surface ice melt begins, seasonal ice albedos are consistently less than albedos for multiyear ice resulting in more solar heat absorbed in the ice and transmitted to the ocean. The shift from a multiyear to seasonal ice cover has significant implications for the heat and mass budget of the ice and for primary productivity in the upper ocean. There will be enhanced melting of the ice cover and an increase in the amount of sunlight available in the upper ocean. © 2012 by the American Geophysical Union.
Frankenstein S.,CRREL |
Proceedings of the International Conference on Cold Regions Engineering | Year: 2015
In the North Atlantic Treaty Organization Reference Mobility Model, the bearing capacity of frozen soils is a function of vehicle weight and frozen layer thickness. This relationship is based on the failure criteria of elastic plates extended to vehicle bearing capacity of ice sheets. In practice, this is found to be very conservative. A new study to investigate different approaches is underway. As part of this study, we examine how inherent soil physical properties change as a function of ice content, temperature, and engineering soil type. The final step will be to determine how these parameterizations affect the different bearing capacity formulations and to compare them to field data. © ASCE.
Jones K.F.,CRREL |
Andreas E.L.,NorthWest Research Associates, Inc.
Quarterly Journal of the Royal Meteorological Society | Year: 2012
We compile measurements of sea spray droplet concentrations near the ocean surface for wind speeds from 0 to 28.8 m s -1. We plot each concentration distribution with the Andreas/Fairall spray generation function for that wind speed to display the production velocity distribution that is required for them to be compatible. The comparison shows that the equilibrium assumption is not consistent with this spray generation function. As an alternative, we try a spray concentration function from the literature and find that it represents the data well for moderate to high wind speeds. Using the compiled data, we then extend this concentration function to the very high wind speeds that generate spume droplets. This function has a stronger dependence on wind speed and a longer tail than the concentration function for jet and film droplets produced by bursting bubbles in whitecaps. To validate these concentration functions, we use a simple ice accretion model with weather data and icing observations from two offshore platforms. The results show that moderate-to-high wind speeds that generate film and jet droplets result in small ice accumulations. However, larger spume droplets created at very high wind speeds produce high icing rates. The spray generation function that is consistent with the equilibrium assumption has a median volume droplet radius characteristic of jet droplets for moderate and high wind speeds and a radius that is characteristic of spume droplets at very high speeds. © 2011 Royal Meteorological Society.
News Article | February 27, 2017
This excerpt is from the SPIE Press book Discrimination of Subsurface Unexploded Ordnance. Buried unexploded ordnance (UXO) poses a persistent, challenging, and expensive cleanup problem. Whether on military practice lands or at the sites of past conflicts, many dropped bombs and fired projectiles failed to explode when they penetrated the ground, thus affecting the accessibility of millions of acres at thousands of sites in the US alone. Globally, the problem is even more extensive and dauntingly diverse. The cleanup of UXO sites is particularly challenging because detection must be extraordinarily reliable and remediation extremely careful. Beyond the challenges of problematic terrain, the sheer diversity of possible ordnance types compounds the difficulties inherent in the fuzziness of what practicable sensors provide. In most locations where some ordnance did not explode, many more items have indeed detonated; clutter is abundant, and its sensor responses are often similar to comparably sized UXO. Necessarily conservative practices to date ensure an enormous false alarm rate, and thus cleanup costs are very high. Against this background, recent developments provide a heartening story; the particulars are engaging, and there is a happy ending. Spanning the last ten or fifteen years, the narrative proceeds over a continuum of all aspects of the problem: [excerpt from Chapter 1: The Problem and Its Nature] Section 1.1 Discrimination, Inverse Problems, and What Follows For the most part, one cannot make discrimination decisions by examining recorded signals alone; they typically vary greatly as a function of the sensor–object configuration, which is inherently unknown. Instead, data must be related to a model of the sensor–object interaction. It is via such a model that one can infer underlying parameters that are not just functions of individual signals. Overall, the three essential constituents of the task are: Specifically for the discrimination portion of this task, a complete system requires the following essential elements: In terms of the dynamics of development, there is a great deal of interaction amongst the first three items, such that no one of them really precedes or follows the other. Available instruments require that modelers confront specific kinds of sensor–object interactions, along with the way in which those interactions are reported. This fact effectively defines or at least constrains the modeler’s task. Models and the feasibility of extracting their parameters from data may simultaneously direct developments in instrumentation. The previous list inherently concerns inverse problems, from the most focused level (i.e., how can the field data be treated to infer the specific quantities of interest?) to the broadest level (i.e., is whatever caused this signal an UXO?). To illuminate matters in this domain, let us treat the following general equation as posing, alternatively, a forward problem, a direct inverse solution, and a general inverse problem: where an uppercase bold letter (A) indicates a matrix or tensor, with prominent exceptions to be noted. A lowercase bold letter indicates a vector, e.g., of sources q producing the vector of data d. The specifics of A derive from the relevant physics, geometrical configuration, boundary, or other conditions; and the matrix incorporates some parameters , , …. In a causal view, the entities on the left produce the observations (data, output) on the right. In the forward problem, everything on the left side of Eq. (1.1) needed to obtain the output d is known, including the source strengths q as well as all geometry, applied conditions, etc., that produce the structure and parameters in A. One need only turn the computational crank (multiplication) to produce the result. Many inversion approaches, particularly those based on optimization searches, rely on the repeated execution of forward calculations using prospective parameters and/or q values. Along with direct inverse solutions, this forward calculation is what most engineers and scientists were taught in general physics and math courses. At the outset of the direct inverse solution, A and d are known, but q is unknown. Assuming that the problem has been properly formulated, the measurements taken, and the system structured so that A has no problematic features (something of a leap, as will be seen), the relation in Eq. (1.1) can be inverted mechanically. That is, one may bring to bear a straightforward algorithm with a set of reliable, codified steps that will produce the solution q. Loosely speaking, the causes in the forward problem are known, and the result is computed; in the direct inverse solution, the result is known, and the causes are calculated. In general, it is advisable to reduce at least parts of general inversion calculations to well-posed direct solutions. As shown below, exploiting even the direct inverse calculation may be more fraught than it initially seems. A general inverse problem is distinct from the two previous calculations and is also harder to define precisely. The data d are known, at least to some degree of certainty. The essential question is: what caused this data? The form of the left side of Eq. (1.1) may not be as accommodating as in the direct inversion. Some constituents of A (e.g., , , …) may themselves be unknown and may be part of the solution being sought, in addition to q. For example, the parameters may correspond to such items as source position, material properties, or geometrical orientations. It is much more difficult to codify this kind of inverse calculation than in the other two instances. Pitfalls abound, depending on the specific formulation and the computational measures taken. Groping searches may be required, essentially guessing likely answers (left side of the equation) and performing repeated forward calculations based on those values. Analysts try to zero in on source and parameter values that work best according to some measure of agreement between calculated and observed d values, while also satisfying any conditions and constraints. It is tempting then to treat this best result as an approximation of “true” input and parameter values, but a number of issues contribute to ambiguity here. Substantially different sets of possible sources and parameters could result in reasonable approximations of the same data. Optimization searches may get stuck around local error minima. The inevitable noise and error in the system may make it unclear how well a global error minimum has been identified. Successes notwithstanding, much work remains to be done, if only because formidable settings abound, including rugged, vegetated terrain, wetlands, and underwater sites. For more information about this challenging yet vital field, read the full Tutorial Text Discrimination of Subsurface Unexploded Ordnance. -Kevin O’Neill received a B.A. magna cum laude from Cornell University and a M.A., M.S.E., and Ph.D. from Princeton University. After a National Science Foundation Postdoctoral Fellowship with the Thayer School of Engineering, Dartmouth College, and at the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL), he became a Research Civil Engineer with CRREL. His research has focused on porous media transport phenomena and geotechnically relevant electromagnetics. He has been a Visiting Fellow with the Department of Agronomy, Cornell University, and a Visiting Scientist with the Center for Electromagnetic Theory and Applications, Massachusetts Institute of Technology. Since 1984, he has been an adjunct faculty member with the Thayer School of Engineering, Dartmouth College. His current research interests include electromagnetic remote sensing of surfaces, layers, and buried objects in particular, such as unexploded ordnance.
News Article | January 15, 2017
The US Army has a new target and it's due to be seized next month. But unlike the usual suspects of military forces, these subjects did not violate the law - in fact, they're one of the government's subject for protection: animals and nature. The Department of Defense are currently in an effort to develop biodegradable ammunition that eventually turn into plants. The empty shells from fired bullets and ammunition contain components that do not completely biodegrade after a hundred or more years. During such time, a tree could already be an enormous line of defense against natural disasters as effects of climate change. This initiative will allow for future proving grounds and training grounds worldwide to become a haven for green trees more than green tanks and camouflage uniforms. They opened a solicitation in the Small Business Innovation Research program's website for "Biodegradable Composites with Embedded Seeds for Training Ammunition" in November last year and is due for February 8 this year. In an effort to combat the problems arising from the waste materials left from empty shells of the bullets used in training, the government is seeking proposals from business that can manufacture bullets with biodegradable components. Currently, the components in the training rounds take hundreds or more years to biodegrade with some posing risks to the environment where they are left off. These ammo are used for training not only in the United States proving grounds but also in other military training camps around the world. The winning contractor will be joining forces with the the US Army Corps of Engineers' Cold Regions Research and Engineering Laboratory (CRREL). As stated in the proposal, the bioengineered seeds that only germinate until they have touched the ground have already been developed and demonstrated by the CRREL. These seeds will be embedded to the biodegradable composites. The winning contractor will be going through phases of developing the process or producing the biodegradable composites, manufacturing prototypes to demonstrate feasibility for industrial use and shall produce prototypes for ballistic examinations. The final phase is to the coordination between the contractor and the PEO Ammunition and other ammunition prime contractors for transitioning to use of Army training. The joint effort to revolutionize training rounds into less hazardous weapon - the fact this ammo could still kill doesn't completely remove the risks - by growing environmentally friendly plants that remove soil contaminants and consume the wastes of this project. To submit a proposal, visit the open solicitation from the Department of Defense. © 2017 Tech Times, All rights reserved. Do not reproduce without permission.