Disciplines: 

Geography, GIS, remote sensing, biology, computer science

Mentor: 

Marc Simard (334): marc.simard@jpl.nasa.gov; (818)354-6972

Kyle Cavanaugh (UCLA): KCavanaugh@geog.ucla.edu

Mentor URL: 

https://deltax.jpl.nasa.gov/ or

https://landscape.jpl.nasa.gov/

Background: 

The student will use remote sensing data from several optical, radar and lidar remote sensing instruments to monitor deforestation and afforestation of mangroves. The data are selected from the Google Earth Engine catalog as well as other sources, then formatted into a data cube to generate land cover classifications and monitor changes in land cover and biomass.

Description: 

The main objectives are to 1)detect hotspots of mangrove deforestation and afforestation; 2) map canopy height and above ground biomass estimates.

References: 

Thomas, N., Bunting, P., Lucas, R., Hardy, A., Rosenqvist, A. and Fatoyinbo, T. (2018) Mapping mangrove extent and change: A globally applicable approach, Remote Sensing, 10 (9), 1466.

Simard, M., Pinto, N., Fisher, J., Baccini, A., (2011), “Mapping forest canopy height globally with spaceborne lidar”, Journal of Geophysical Research, VOL. 116, G04021, 12 PP., 2011, doi:10.1029/2011JG001708.

Student Requirements: 

Python or GEE, GIS

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Landslides / Geomechanics/ Remote Sensing / Geodesy

Mentor: 

Dr. Alexander L. Handwerger (JIFRESSE/329A): alhandwerger@g.ucla.edu, alexander.handwerger@jpl.nasa.gov; 401-440-7711

Dr. Eric J. Fielding (329A): eric.j.fielding@jpl.nasa.gov

Mentor URL: 

https://science.jpl.nasa.gov/people/handwerger/

https://science.jpl.nasa.gov/people/Fielding/

Background: 

Population growth, urban expansion, and extreme weather are contributing more than ever to hazard vulnerability. Among the hazards driven by weather patterns, the interpretation and prediction of ground deformation due to landslides poses enormous challenges. It is recognized that natural hillslopes fail in multiple ways, sometimes moving slowly downslope, and at other times accelerating rapidly with fluid-like phenomenology. Such different modes of deformation coexist in the same geomorphic setting, affect portions of terrain proximal to one another, and may even be experienced by the same landslides at different times. Advances in remote sensing have transformed how we track the variables of interest for landslide hazard assessment, enabled by measurements at spatiotemporal resolutions that were unthinkable just a decade ago. In what ways can such advances improve our tools to predict the fate of landslides? If we were able to remotely sense the variation in displacement rates and gradients, could we identify the underlying physical causes of catastrophic landslides? Can knowledge of time-evolving surface kinematics and weather patterns be used to achieve early detection of future failures? This project is motivated by these questions.

Description: 

The JIFRESSE Summer Internship Program student will analyze satellite interferometric synthetic aperture radar (InSAR) time series to quantify the motion of active landslides along California State Highway 1 on the Big Sur coast, California. These data will be compared to local precipitation data to better understand how infiltrating water drives landslide activity. The main goal of the project is to capture the landslide motion history and to potentially identify precursory deformation patterns that may warn of catastrophic failure. This project will help us to better understand landslide mechanics and the environmental factors that drive their motion. Furthermore, this project will provide useful information for future satellite-based missions aimed at identifying and monitoring natural hazards, such as landslides.

References: 

Handwerger, A. L., Huang, M. H., Fielding, E. J., Booth, A. M., & Bürgmann, R. (2019). A shift from drought to extreme rainfall drives a stable landslide to catastrophic failure. Scientific Reports, 9(1), 1-12.

Lacroix, P., Handwerger, A. L., & Bièvre, G. (2020). Life and death of slow-moving landslides. Nature Reviews Earth & Environment, 1(8), 404-419.

Student Requirements: 

Background in Remote Sensing and/or Earth Sciences. Strong quantitative skills. Proficiency in Python, MATLAB, or other programming language is required.

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Atmospheric and climate science

Mentor: 

Baijun Tian (329E): Baijun.Tian@jpl.nasa.gov, 626-720- 7512

Prof. Yongkang Xue (UCLA Geography Dept): yxue@geog.ucla.edu, (310) 825-1122

Mentor URL: 

https://science.jpl.nasa.gov/people/Tian/

http://www.sscnet.ucla.edu/geog/faculty/yxue/

Background: 

The agricultural irrigation activities can potentially change surface and atmospheric thermodynamics and energy budget significantly and thus have a big impact on climate both regionally and globally (Wang, et al., 2016).

Description: 

The objective of this project is to analyze NASA Atmospheric Infrared Sounder (AIRS) tropospheric temperature and moisture profile data to better characterize and understand the impacts of irrigation on local atmospheric moist thermodynamic vertical structure. The analysis will help us to propose future Earth Venture Suborbital mission proposals related to irrigation.

References: 

Wang, J., Kessner, A. L., Aegerter, C., et al. (2016), A Multi-sensor View of the 2012 Central Plains Drought from Space, Frontiers in Environmental Science, 4(45), https://doi.org/10.3389/fenvs.2016.00045.

Student Requirements: 

STEM undergraduate or graduate students with good analytical and computer skills.

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Earth Sciences, air quality, remote sensing of atmospheric composition and landscape properties

Mentor: 

Pablo E. Saide, Department of Atmospheric & Oceanic Sciences (AOS), UCLA, Phone: 310 825 4432, Email: saide@atmos.ucla.edu, and

Kevin W. Bowman, JPL, Phone: 818 354 2995, Email: Kevin.W.Bowman@jpl.nasa.gov

Kazuyuki Miyazaki, JPL, Phone: 818 354 3266, Email: kazuyuki.miyazaki@jpl.nasa.gov>

Mentor URL: 

https://dept.atmos.ucla.edu/saide

https://science.jpl.nasa.gov/people/Bowman/

https://science.jpl.nasa.gov/people/Miyazaki/

Background: 

Smoke from biomass burning adversely affects human health, degrades atmospheric visibility, and plays an important role in climate radiative forcing (Reid et al., 2016, Jacobson et al., 2014). Last wildfire season was the most catastrophic on California's record, with 4 of the 5 largest fires in California's history happening simultaneously, producing unhealthy air quality in the entire west coast. One of the uncertainties in modeling of smoke impacts is the severity at which smoke will burn and how that translates into air pollutant emissions. A broad spectrum of satellite data exists characterizing surface properties of the landscape (e.g., Leaf Area Index, soil moisture, canopy height), fires (e.g., hotspots and fire radiative power) and smoke (e.g., trace gases and aerosols). A joint analysis of these retrievals, which are provided by JPL and other institutions, would inform statistical relationships that could be used in variety of applications going from air quality forecasts to climate predictions. These results also have the potential to inform the formulation of new satellite and aircraft missions that would retrieve these variables simultaneously and at high temporal and spatial resolutions.

Description: 

An undergraduate intern is needed to work on these topics during summer 2021 over the course of 10 weeks. The intern will gain experience on reading and analyzing surface property and atmospheric composition satellite data from VIIRS, MODIS, SMAP, TROPOMI, and CrIS using Google Earth Engine for the 2020 California mega fires in preparation for air quality model improvements. Deliverables include oral presentations and final written report. This work will benefit JPL to develop a widely useable analysis and visualization framework using NASA's satellite on Google Earth Engine and to interpret atmospheric composition variations withing the JPL's chemical reanalysis (Miyazaki et al., 2020)

References: 

-Jacobson, M. Z.: Effects of biomass burning on climate, accounting for heat and moisture fluxes, black and brown carbon, and cloud absorption effects, Journal of Geophysical Research: Atmospheres, 2014JD021861, 10.1002/2014jd021861, 2014.

-Miyazaki, K., Bowman, K., Sekiya, T., Eskes, H., Boersma, F., Worden, H., Livesey, N., Payne, V. H., Sudo, K., Kanaya, Y., Takigawa, M., and Ogochi, K.: Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005-2018, Earth Syst. Sci. Data, 12, 2223-2259, https://doi.org/10.5194/essd-12-2223-2020, 2020.

-Reid, C. E., Brauer, M., Johnston, F. H., Jerrett, M., Balmes, J. R., and Elliott, C. T.: Critical Review of Health Impacts of Wildfire Smoke Exposure, Environmental Health Perspectives, 124, 1334-1343, doi:10.1289/ehp.1409277, 2016

Student Requirements: 

Basic experience with Python or other programing language, willingness to learn about satellite data and wildfires

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Computer science, data analysis, physics, GNSS, political science

Mentor: 

Max Roberts (335F, Gravity Sensing Instruments)

t.maximillian.roberts@jpl.nasa.gov, 818-928-9671

Jacob Bortnik (Dept. of Atmospheric and Oceanic Sciences, UCLA)

 jbortnik@gmail.com

Mentor URL: 

Background: 

Global Navigation Satellite Systems (GNSS), such as GPS, are critical infrastructure for the United States and most of the world. Almost every major system is somehow dependent on timing and location provided by the very weak signals from these satellites, from military and aviation to shipping services and financial institutions (even your cellphone). As such, there is motivation for actors around the world to deny targeted regions access to GNSS signals (jamming), or even trick GNSS receivers into thinking they are somewhere they aren’t (spoofing). Understanding the distribution of these jamming/spoofing transmitters, and the nature of the signals they are emitting is of interest not only to the systems just mentioned, but also to the scientific community that uses these same signals for measurement of the atmosphere, ionosphere, and seismic activity.

Description: 

Our group at JPL builds receivers that use GNSS signals to measure atmospheric parameters that significantly improve weather modelling and prediction. We have recently discovered that these same measurements can be used to detect terrestrial sources of RFI, likely from jammer/spoofer systems located around the world. With a 20- year data record and 12 spacecraft flying by 2025 (9 currently in orbit), there is a substantial dataset to be studied. This open-ended internship is an initial exploration of this new measurement which could include tasks such as characterization of these RFI sources, precise geolocation of transmitters, correlation of RFI events with known geopolitical drivers (conflicts, known military operations, etc.), and/or building out a robust infrastructure to handle the ingestion, visualization, and interpretation of real-time RFI data from spacecraft.

References: 

https://www.cosmic.ucar.edu/what-we-do/cosmic-2/

Student Requirements: 

Strong background in data analysis, python, and working in Linux environments.

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Atmospheric and Climate Science

Mentor: 

Drs. Xianan Jiang, xianan@ucla.edu; Hai Nguyen, hai.nguyen@jpl.nasa.gov; Hui Su, hui.su@jpl.nasa.gov

Mentor URL: 

http://climvar.org/jiang/

https://dus.jpl.nasa.gov/home/nguyen/

https://science.jpl.nasa.gov/people/Su/

Background: 

Seasonal mean moisture distribution over the Indo- Pacific region plays a crucial role in regulating large- scale organization of tropical convection, such as the Madden-Julian Oscillation (Jiang et al. 2020). Current generation climate models, however, have great difficulties in realistically simulating the moisture field.

Description: 

The objectives of this project are two-folded: 1) to improve simulations of moisture structure in climate models using the Machine Learning (ML) approach with training data sets from the latest high-resolution reanalysis and NASA satellite observations (e.g., temperature and moisture profiles from AIRS); 2) to investigate how improved moisture field in climate models can lead to improved simulations of tropical convective organization. This project will provide important guidance for climate model improvements and can help formulate future missions that target tropical convective organization.

References: 

Jiang, X., et al., 2020: Fifty Years of Research on the Madden-Julian Oscillation: Recent Progress, Challenges, and Perspectives. JGR - Atmosphere, 125, e2019JD030911, 10.1029/2019JD030911.

Student Requirements: 

STEM students with good analytical and computer skills.

Location: 

Remotely following UCLA and JPL guidelines

Disciplines: 

Precision measurements, applied physics, engineering, positioning-navigation-timing, chip-scale devices.

Mentor: 

JPL: Andrey B. Matsko (US 335E), andrey.b.matsko@jpl.nasa.gov, (818) 354-4944

Vladimir Iltchenko (US 335E), vladimir.s.iltchenko@jpl.nasa.gov

UCLA: Chee Wei Wong, cheewei.wong@ucla.edu, 310.825.6115

Mentor URL: 

UCLA: http://oqe.ee.ucla.edu/

Background: 

Recently advancements in precision sensing and laser optomechanics have enabled new transformative frontiers in chip-scale inertial navigation. With understanding of the laser noise and sensing sensitivities, we seek to drive down the noise to enable chip-scale gravimeters for gravity anomaly and field sensing measurements.

Description: 

We will examine the theory, numerical modeling and preliminary measurements towards a chip-scale nano- optomechanical gravimeter for CubeSat applications. This is supported through innovative cavity design, laser driving and readout capabilities. Fundamental modeling and measurements of noise spectra will be studied in this JPL-university JIFRESSE program.

References: 

[1] Huang and Flor Flores et al., Laser and Photonics Reviews 14, 1800329 (2020).

[2] Luan et al. Nature: Sci. Rep. 4, 6842 (2014).

[3] Liu et al. Phys. Rev. Lett. 110,153606 (2013).

[4] Tang et al. Microsystems & Nanoengineering 5, 45 (2019).

[5] Middlemiss et al. Nature 531, 614 (2016).

Student Requirements: 

Precision measurement expertise, laser physics, cavity optomechanics, noise metrology, programming and coding, and navigation.

Location: 

Remotely following UCLA and JPL guidelines