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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.

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Project:  Extracting Vegetation Structure Information from LiDAR to Drive Radar Models
Disciplines:  Computer Science, Earth Science
Mentor:  Erika Podest, (JPL), Erika.Podest@jpl.nasa.gov, Phone: (818) 354-6086
Mentor URL:  https://science.jpl.nasa.gov/people/Podest/  (opens in new window)
Background:  Detailed information on vegetation is fundamental to ecosystem studies. Structure and form are essential factors in vegetation functional traits (e.g. specific leaf area, wood density, tree height), structural traits (e.g. biomass) and climate sensitivity (e.g. changes in mortality or growth). Vegetation structure is an important biodiversity indicator providing the physical environment that generates, supports, and maintains forest biodiversity and is useful for informing biodiversity monitoring and conservation efforts of species. Biodiversity is dependent on (1) landscape-scale structure, which is the spatial heterogeneity of an area composed of interacting habitat patches, and (2) local-scale structure such as vertical structure, which is the bottom to top configuration or complexity of above-ground vegetation. Fauna species presence or absence depends on both landscape (community) and local scale habitat structure.
Radar remote sensing is one way to assess vegetation structure because the signal (depending on frequency) can penetrate through the canopy and interact with different vegetation components, depending on their size and orientation. Radar models are used in order to understand the information content from radar remote sensing datasets as related to vegetation functional and structural traits. Radar models of vegetation commonly characterize canopy constituents as shapes (e.g. cylinders, discs) for which radar scattering processes can be effectively modeled to understand the signal interaction with individual vegetation components and assemblages of these components. Parameterization of radar models based on these considerations requires detailed vegetation biometry collected in situ to characterize vegetation structure. These data have often been difficult and time-consuming to collect, especially for forest canopies with complex architecture. However, airborne and spaceborne LiDAR data provide information on vegetation structure that can be used to parametrize radar models.
Description:  The focus of this SURF effort is to use airborne and spaceborne LiDAR datasets in conjunction with ground measurements of vegetation structure to develop analytical tools through machine learning and other approaches to automatically derive vegetation structure parameters that can be used to drive radar models. Datasets are openly available for a variety of regions including the NSF National Ecological Observatory Network (NEON). Regions of focus include temperate forest (Harvard Forest, Massachusetts), and tropical forest (Barro Colorado Island, Panama).
References:  https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13121
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12301
https://www.cabdirect.org/cabdirect/abstract/20123405343
https://www.ace-lab.ca/assets_b/Guo et al. 2017 Ecological Informatics.pdf

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176871
Student Requirements:  Computer Science
Python, Jupyter Notebooks
Location / Safety:  Project building and/or room locations: . Student will need special safety training: .
Programs:  This AO can be done under the following programs:

  Program    Available To
       SURF    both Caltech and non-Caltech students 

Click on a program name for program info and application requirements.


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