Kevin L. McIlhany

Retrieval of Substrate Bearing Strength from Hyperspectral Imagery during the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign

Hyperspectral imagery (HSI) derived from remote sensing can delineate surface properties of substrates such as type, moisture, and grain size. These are critical parameters that determine the substrate bearing strength. Although HSI only sees the surface layer, statistics can be derived that relate surface properties to the likely bearing strength of soils in particular regions. This information can be used to provide an initial map estimate on large scales of potential bearing strength. We describe an initial validation study at the Virginia Coast Reserve relating airborne HSI to in situ spectral and geotechnical measurements through a spectral-geotechnical lookup table (LUT).

Bathymetry Retrieval from Hyperspectral Imagery in the Very Shallow Water Limit: A Case Study from the 2007 Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

We focus on the validation of a simplified approach to bathymetry retrieval from hyperspectral imagery (HSI) in the very shallow water limit (less than 1–2 m), where many existing bathymetric LIDAR sensors perform poorly. In this depth regime, near infra-red (NIR) reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role. Our processing framework exploits two optimal regions where a simple model depending on bottom type and water depth can be applied in the very shallow limit. These two optimal spectral regions are at a local maximum in the near infra-red reflectance near 810 nm, corresponding to a local minimum in absorption, and a maximum in the first derivative of the reflectance near 720 nm. These two regions correspond to peaks in spectral correlation with bathymetry at these depths.

Optimizing mixing in lid-driven flow designs through predictions from Eulerian indicators

In this paper, we further develop the notion of Eulerian indicators (EIs) for predicting Lagrangian mixing behavior. We employ a two-dimensional “blinking” Stokes flow as a model for mixing in a three-dimensional, spatially periodic channel flow. Each blinking flow alternates two distinct velocity fields that were calculated using a lid-driven cavity model. A new EI termed mobility is introduced to measure how effectively the blinking velocity fields transport fluid throughout the domain. We also calculate the transversality for these flows, which is an EI measuring how much the velocity direction at each point in the domain changes when the velocity fields blink. For the studied flows, we show that although individually the mobility and transversality do not correlate well with mixing as measured by the decay of the variance of concentration, the product of mobility and transversality does correlate well with the decay of the variance of concentration and predicts which combinations of velocity fields wi...

Eulerian indicators under continuously varying conditions

In this paper, we extend the notion of Eulerian indicators (EIs) for predicting Lagrangian mixing behavior previously developed for blinking flows to the continuous time setting. We apply the EIs to a study of mixing in a kinematic model of a time-dependent double-gyre with five different time dependencies—sinusoidal, sawtooth, square wave, triangular, and noise (which is constructed so that it is also periodic in time). Each of the five velocity fields is described by two parameters; the strength of the time dependence (e) and the period (T). Based on a trajectory based quality of mixing diagnostic (Danckwerts’ normalized variance of concentration) we find that noisy time dependence has the largest region of good mixing in the parameter space and triangular time dependence has parameter values corresponding to the most complete and fastest mixing. These Lagrangian based predictions are confirmed by the EIs (product of the transversality and mobility). Although not every feature of the mixing behavior is ...

Very Shallow Water Bathymetry Retrieval from Hyperspectral Imagery at the Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

A number of institutions, including the Naval Research Laboratory (NRL), have developed look up tables for remote retrieval of bathymetry and in-water optical properties from hyperspectral imagery (HSI) [6]. For bathymetry retrieval, the lower limit is the very shallow water case (here defined as < 2m), a depth zone which is not well resolved by many existing bathymetric LIDAR sensors, such as SHOALS [4]. The ability to rapidly model these shallow water depths from HSI directly has potential benefits for combined HSI/LIDAR systems such as the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) [10]. In this study, we focused on the validation of a near infra-red feature, corresponding to a local minimum in absorption (and therefore a local peak in reflectance), which can be correlated directly to bathymetry with a high degree of confidence. Compared to other VNIR wavelengths, this particular near-IR feature corresponds to a peak in the correlation with depth in this very shallow water regime, and this is a spectral range where reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role.

Lagrangian and Eulerian analysis of transport and mixing in the three dimensional, time dependent Hill’s spherical vortex

In this paper, we extend the notion of Eulerian indicators (EIs), previously developed for two dimensional time dependent flows, to three dimensional time dependent flows, where the time dependence can be arbitrary. These are applied to a study of transport and mixing in the Hill’s spherical vortex subject to a linear strain rate field. We consider the axisymmetric case and the fully three dimensional case with different types of time dependence. We develop a Lagrangian characterization of transport and mixing appropriate for open three dimensional flows and we show that the EIs provide a detailed description of the flow structure that can be correlated with the Lagrangian transport and mixing results. The EIs yield results consistent with the dynamics of the Hill’s vortex flow characteristics, correlation with transverse shear, and anti-correlation with transversality.

High Dimensional Cluster Analysis Using Path Lengths

A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimension (

Remote Sensing Retrieval of Substrate Bearing Strength from Hyperspectral Imagery at the Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign

N_{_{D}}>3

Optimization and pose selection for a lindy hop partnered spin

). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering, however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-Of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.