Optics and Photonics for Sensing the Environment

25 June 2019 – 27 June 2019 San Jose McEnery Convention Center, San Jose, California United States


Optics and Photonics for Sensing the Environment

Optics and Photonics in Environment and Pollution Monitoring

  • Stand-off and remote sensing techniques to measure trace gases, particulates and aerosols (including LIDAR, DOAS and FTIR)
  • Novel laboratory techniques and methodologies towards disruptive sensing technology (e.g. frequency combs, photonic sensors on a chip, sensing networks and the Internet of things)
  • Satellite and aircraft-based monitoring (e.g. regional air quality, global warming, global carbon cycle, and carbon sources and sinks)
  • Optics and photonics for improved air and water quality, including transformative optical low-cost sensors

Optics and Photonics in Industrial Environments

  • Optics and photonics for renewable (wind, solar and biofuel) and petrogenic (fossil fuel) energies
  • Novel techniques for monitoring fugitive emissions (fossil fuels, nature gas, mining and nuclear power)
  • Imaging and monitoring of crops for increased efficiency and sustainability in agriculture
  • Optics and photonics for extreme environments, including combustion, propulsion and high-temperature flow (e.g. flames, explosions, plasmas and projectiles)



  • Ramon Alvarez, Environmental Defense FundUnited States 
    Applications of Mobile air Pollution Monitoring to Guide Environmental Policy
  • Douglas Baer, Los Gatos Research IncUnited States 
    Novel Gas Analyzers Based on Cavity Enhanced Laser Absorption Spectroscopy
  • David Bomse, Mesa Photonics, LLCUnited States 
    Laser Heterodyne Radiometry for Remote Sensing of Atmospheric Gases
  • Erol Cromwell, Pacific Northwest National LaboratoryUnited States 
    Lidar Cloud Detection with Fully Convolutional Networks
  • Jérôme Genest, Universite LavalCanada 
    Free-running Optical Frequency Combs for Remote Sensing
  • Bradley Gibson, NASA Jet Propulsion Laboratory 
    Miniaturized Ring-Down Spectroscopy with Etalon Cancellation for Planetary Science and Other Field Applications
  • Bernhard Lendl, Technische Universität WienAustria 
    Advances in mid-IR Cavity Enhanced Photoacoustic and Photothermal Trace Gas Sensing
  • James Scherer, Aeris Technologies 
    MIRA: A New, Ultrasensitive Middle Infrared Laser-Based "Lab in a Lunchbox"
  • Vasanthi Sivaprakasam, US Naval Research LaboratoryUnited States 
    Optical Characterization of Individual Aerosol Particles
  • Mark Zondlo, Princeton UniversityUnited States 
    Open-Path Ammonia Measurements on the NASA DC-8 Aircraft Using a Fiber-coupled, Quantum Cascade Laser



Adam Fleisher, National Inst of Standards & Technology, United States, USAChair

Dennis Killinger, University of South Florida, USAs, Chair

Michelle Bailey, National Institute of Standards and Technology, NIST, USA

Lukas Emmenegger, EMPA, Switzerland

Aleksandra Foltynowicz, Umeå University, Sweden

Melanie Ghysels-Dubois, Universite de Reims Champagne-Ardenne, France

Klapp Iftach, Agricultural Research Organization, ARO, Volcani Center, Israel

Jean-Pierre van Helden, Leibniz Institute for Plasma Science and Technology, INP-Greifswald, Germany

Jonas Westberg, Princeton University, United States


Plenary Session

Melba Crawford

Purdue University, USA

Multi-modality Remote Sensing Data Acquisition and Analysis for High Throughput Phenotyping

Sensing technologies ranging from RGB cameras to hyperspectral imaging and LiDAR are rapidly gaining popularity for field-based high throughput phenotyping applications on airborne and ground-based platforms.  In addition to direct measurements of traditional phenotypes such as height, these sensors potentially provide surrogate measurements for plant structural characteristics (e.g. leaf count and leaf area index) and chemistry (e.g. photosynthesis, and plant stress). Opportunities and challenges associated with acquisition, processing, and analysis of high resolution RGB, VNIR/SWIR hyperspectral data, and discrete return LiDAR data acquired from UAVs for plant breeding experiments focused on advancing sorghum varieties for biofuels will be outlined.  Results from multi-modality, multi-temporal predictive modeling of complex phenotypes such as biomass using data driven machine learning and biophysical models will also be presented in the context of feature extraction and learning with limited training data.  Opportunities to exploit transfer learning across scales will also be discussed.

About the Speaker

Dr. Melba Crawford holds the Chair of Excellence in Earth Observation at Purdue University, where she is the Associate Dean of Engineering for Research and a professor in the Schools of Civil Engineering and Electrical and Computer Engineering, and the Department of Agronomy.  Her research interests focus on development of methods for signal and image processing, and applications of these algorithms to remote sensing problems in defense, agriculture, and natural resource management.  She is currently co-leading a joint initiative between the Purdue colleges of agriculture and engineering in development of advanced sensing technologies and analysis methodology for wheeled and UAV platforms, focused on high throughput phenotyping for plant breeding.

Dr. Crawford is a Fellow of the IEEE, Past President of the IEEE Geoscience and Remote Sensing Society, an IEEE GRSS Distinguished Lecturer, and the current Treasurer of the IEEE Technical Activities Board. She was a member of the NASA EO-1 Science Validation team and served on the NASA Earth System Science and Applications Advisory Committee and the advisory committee to the NASA Socioeconomic Applications and Data Center (SEDAC).

Alex Gaeta

Columbia University

Chip-Based Comb Spectroscopy

The ability to generate optical frequency combs in microresonators at milliwatt power levels offers the promise for high-precision spectroscopic instruments in highly robust, compact, and portable platforms.

About the Speaker

Alex Gaeta received his Ph.D. in 1991 in Optics from the University of Rochester.  He joined the faculty in the Department of Applied Physics and Applied Mathematics at Columbia University in 2015, where he is the David M. Rickey Professor.  Prior to  this, he was a professor in the School of Applied and Engineering Physics at Cornell University for 23 years.  He has published more than 230 papers in quantum and nonlinear optics. He co-founded PicoLuz, Inc. and has served as the founding Editor-in-Chief of Optica since 2014.  He is a Fellow of the OSA, APS, and IEEE, and was awarded the 2019 Charles H. Townes Medal from the OSA.

Peter Russo

Analog Photonics

High Performance Optical Phased Array LiDAR

Integrated optical phased arrays provide an attractive solution to LiDAR sensors by enabling solid-state, small-form-factor systems fabricated on 300mm wafers. We present recent results including high-performance beam steering and long-range LiDAR up to almost 200m."

About the Speaker

Peter Russo is Director of LiDAR at Analog Photonics. He received his Bachelor of Science in Electrical Engineering from University of Maryland, College Park in 2008. After graduating, he joined BAE Systems as part of the Engineering Leadership Development Program, through which he also received his Master of Science in Electrical Engineering from University of New Hampshire. At BAE Systems, he served as principle investigator on several active electro-optical systems programs. In 2015, he joined Formlabs, a 3D-printing startup, as a member of the electro-optical team. In 2017, Mr. Russo joined Analog Photonics as the LiDAR Architect to develop and commercialize silicon-photonic, optical phased array LiDAR for use on autonomous vehicles in both the automotive and DoD markets.