Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor.
Announcements for external summer programs are listed here.
New for 2021: Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Detecting Tree Mortality in the Tropics|
|Disciplines:||Computer Science, Earth Science|
|Mentor URL:||https://science.jpl.nasa.gov/people/Podest/ (opens in new window)|
|Background:||The worlds forests are responsible for sequestering about one third of the carbon dioxide that results from human activities. The uncertainty associated with tree mortality rates and their drivers in the tropics is a source of associated uncertainty in the global carbon budget. Tropical forests currently account for approximately 40% of global terrestrial biomass carbon stocks and about half of the current terrestrial carbon sink. These ecosystems have a natural biomass carbon residence time averaging 50-100 years, reflecting tree mortality rates of ~1-2% per year. However, tropical tree mortality rates have increased in recent decades in the tropics, a pattern hypothesized to be due to increasing temperatures, vapor pressure deficits, drought, fire, and associated environmental and biotic change, and which, if sustained, has substantial implications for global carbon budgets. Unfortunately, patterns and mechanisms of tropical tree mortality remain poorly understood. Our limited understanding of tree mortality has long been recognized as a major weakness of our understanding of tropical forest dynamics, one that prevents models from accurately reproducing spatial patterns in forest biomass and severely limits our ability to project tropical forest responses to global change. Spaceborne data hold great promise, but there are no established methods to estimate tree mortality rates in diverse tropical forests from satellite remote sensing. Very high resolution remote sensing data have the potential to be game changers with respect to our ability to quantify tree mortality because they can resolve individual trees and potentially identify tree mortality events over large areas at fine spatial and temporal scales.|
|Description:||This project entails developing either a time series or a machine learning algorithm using very high resolution optical (from Planet) and synthetic aperture radar (SAR) data (from Cosmo SkyMed) to identify tree mortality in tropical forests and classify the associated mode of death as either (1) fallen or (2) standing dead tree. The first part will consist in rigorously assessing both time series analysis and machine learning approaches. Then, develop a workflow to assemble time series Planet and Cosmo SkyMed data. The algorithms will be trained and validated using tree mortality data for a 50 ha plot of seasonally dry tropical forest on Barro Colorado Island (BCI) in Panama, where monthly aerial drone photogrammetry and annual ground census data are available for the last 5 years. The accuracy of the algorithms will be further assessed over other areas in Panama where tree mortality data is available. In addition, the algorithms will be evaluated according to how accuracy varies with tree characteristics, forest type, season, and spatial and temporal resolutions.|
|Student Requirements:||Skills: Python, Jupyter Notebooks|
|Location / Safety:||Project building and/or room locations: . Student will need special safety training: .|
This AO can be done under the following programs:
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