David J. Lary

Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles

Abstract: We demonstrate compact, low power, lightweight laser-based sensors for measuring trace gas species in the atmosphere designed specifically for electronic unmanned aerial vehicle (UAV) platforms. The sensors utilize non-intrusive optical sensing techniques to measure atmospheric greenhouse gas concentrations with unprecedented vertical and horizontal resolution (~1 m) within the planetary boundary layer. The sensors are developed to measure greenhouse gas species including carbon dioxide, water vapor and methane in the atmosphere. Key innovations are the coupling of very low power vertical cavity surface emitting lasers (VCSELs) to low power drive electronics and sensitive multi-harmonic wavelength modulation spectroscopic techniques. The overall mass of each sensor is between 1–2 kg including batteries and each one consumes less than 2 W of electrical power. In the initial field testing, the sensors flew successfully onboard a T-Rex Align 700E robotic helicopter and showed a precision of 1% or less for all three trace gas species. The sensors are battery operated and capable of fully automated operation for long periods of time in diverse sensing environments. Laser-based trace gas sensors for UAVs allow for high spatial mapping of local greenhouse gas

Estimating the global abundance of ground level presence of particulate matter (PM2.5).

With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.

Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft

A model aircraft equipped with a custom laser-based, open-path methane sensor was deployed around a natural gas compressor station to quantify the methane leak rate and its variability at a compressor station in the Barnett Shale. The open-path, laser-based sensor provides fast (10 Hz) and precise (0.1 ppmv) measurements of methane in a compact package while the remote control aircraft provides nimble and safe operation around a local source. Emission rates were measured from 22 flights over a one-week period. Mean emission rates of 14 ± 8 g CH4 s(-1) (7.4 ± 4.2 g CH4 s(-1) median) from the station were observed or approximately 0.02% of the station throughput. Significant variability in emission rates (0.3-73 g CH4 s(-1) range) was observed on time scales of hours to days, and plumes showed high spatial variability in the horizontal and vertical dimensions. Given the high spatiotemporal variability of emissions, individual measurements taken over short durations and from ground-based platforms should be used with caution when examining compressor station emissions. More generally, our results demonstrate the unique advantages and challenges of platforms like small unmanned aerial vehicles for quantifying local emission sources to the atmosphere.

Phytophthora megakarya and Phytophthora palmivora, Closely Related Causal Agents of Cacao Black Pod Rot, Underwent Increases in Genome Sizes and Gene Numbers by Different Mechanisms

Phytophthora megakarya (Pmeg) and Phytophthora palmivora (Ppal) are closely related species causing cacao black pod rot. Although Ppal is a cosmopolitan pathogen, cacao is the only known host of economic importance for Pmeg. Pmeg is more virulent on cacao than Ppal. We sequenced and compared the Pmeg and Ppal genomes and identified virulence-related putative gene models (PGeneM) that may be responsible for their differences in host specificities and virulence. Pmeg and Ppal have estimated genome sizes of 126.88 and 151.23u2009Mb and PGeneM numbers of 42,036 and 44,327, respectively. The evolutionary histories of Pmeg and Ppal appear quite different. Postspeciation, Ppal underwent whole-genome duplication whereas Pmeg has undergone selective increases in PGeneM numbers, likely through accelerated transposable element-driven duplications. Many PGeneMs in both species failed to match transcripts and may represent pseudogenes or cryptic genetic reservoirs. Pmeg appears to have amplified specific gene families, some of which are virulence-related. Analysis of mycelium, zoospore, and in planta transcriptome expression profiles using neural network self-organizing map analysis generated 24 multivariate and nonlinear self-organizing map classes. Many members of the RxLR, necrosis-inducing phytophthora protein, and pectinase genes families were specifically induced in planta. Pmeg displays a diverse virulence-related gene complement similar in size to and potentially of greater diversity than Ppal but it remains likely that the specific functions of the genes determine each species’ unique characteristics as pathogens.

Low-altitude Terrestrial Spectroscopy from a Pushbroom Sensor

Hyperspectral cameras sample many different spectral bands at each pixel, enabling advanced detection and classification algorithms. However, their limited spatial resolution and the need to measure the camera motion to create hyperspectral images makes them unsuitable for nonsmooth moving platforms such as unmanned aerial vehicles UAVs. We present a procedure to build hyperspectral images from line sensor data without camera motion information or extraneous sensors. Our approach relies on an accompanying conventional camera to exploit the homographies between images for mosaic construction. We provide experimental results from a low-altitude UAV, achieving high-resolution spectroscopy with our system.

Survey on the estimation of mutual information methods as a measure of dependency versus correlation analysis

In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, we use a piece-wise constant Bayesian methodology using a general Dirichlet prior. In this estimation method, we use a two-stage approach where we approximate the probability distribution first and then calculate the marginal and joint entropies. Here, we demonstrate the performance of this Bayesian approach versus the others for computing the dependency between different variables. We also compare these with linear correlation analysis. Finally, we apply MI and correlation analysis to the identification of the bias in the determination of the aerosol optical depth (AOD) by the satellite based Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground based AErosol RObotic NETwork (...

The Ignite Distributed Collaborative Scientific Visualization System

We describe the Ignite Distributed Collaborative Scientific Visualization System (IDCVS), a system which permits real-time interaction and visual collaboration around large data sets, with an initial emphasis on scientific data. The IDCVS offers such a collaborative environment, with real-time interaction on any device between users separated across the wide area. It provides seamless interaction and immediate updates even under heavy load and when users are widely separated: the design goal was to fetch a data set consisting of 30,000 points from a server and render it within 150ms, for a user anywhere in the world, and reflect changes made by a user in one location to all other users within a bound provided by network latency. The system was demonstrated successfully on a significant worldwide air pollution data set, with values on 10, 25, 50, and 100km worldwide grids, monthly over an 18-year period. It was demonstrated on a wide variety of clients, including laptop, tablet, and smartphone.

Using Remote Control Aerial Vehicles to Study Variability of Airborne Particulates

Airborne particulates play a significant role in the atmospheric radiative balance and impact human health. To characterize this impact, global-scale observations and data products are needed. Satellite products allow for this global coverage but require in situ validations. This study used a remote-controlled aerial vehicle to look at the horizontal, vertical, and temporal variability of airborne particulates within the first 150 m of the atmosphere. Four flights were conducted on December 4, 2014, between 12:00 pm and 5:00 pm local time. The first three flights flew a pattern of increasing altitude up to 140 m. The fourth flight was conducted at a near-constant altitude of 60 m. The mean PM2.5 concentration for the three flights with varying altitude was 36.3 μg/m3, with the highest concentration occurring below 10 m altitude. The overall vertical variation was very small with a standard deviation of only 3.6 μg/m3. PM2.5 concentration also did not change much throughout the day with mean concentrations for the altitude-varying flights of 35.1, 37.2, and 36.8 μg/m3. The fourth flight, flown at a near-constant altitude, had a lower concentration of 23.5 μg/m3.

Holistics 3.0 for Health

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.

The Ignite Distributed Collaborative Visualization System

Sushil Bhojwani Matt Hemmings Dan Ingalls Jens Lincke University of Victoria University of Victoria CDG, SAP Hasso Plattner Institute sushilsrk@gmail.com mhemming@uvic.ca danhhingalls@gmail.com Jens.Lincke@hpi.de Robert Krahn David Lary Rick McGeer Glenn Ricart CDG, SAP UT Dallas CDG/US Ignite US Ignite robertkrahn@gmail.com dlary@utdallas.edu rick@mcgeer.com glenn.ricart@us-ignite.org Marko Roder Yvonne Coady Ulrike Stege CDG, SAP University of Victoria University of Victoria markoroeder@cdglabs.org ycoady@uvic.ca ustege@uvic.ca