Picture description: A student using a spectrometer to measure the spectral reflectance of a leaf in the field in the Barro Colorado Nature Monument in Panama.

Project title

Exploring the potential for automated species identification of tropical plants from hyperspectral and 3D scanning data

Mentor's name(s)

Helene Muller-Landau, Senior Scientist

Co-mentor's name(s)

Joe Wright, Senior Scientist

Location of internship. Will mentor be at this location?

Barro Colorado Island, Panama. The mentor(s) will be at these locations.

Project summary

Tropical forests account for a majority of terrestrial carbon stocks and biodiversity, and it is thus critical to understand how they are being affected by global climate change. Their high plant diversity offers the potential for high resilience to anthropogenic global change because species vary widely in their responses to environmental variation and the most negatively affected species will invariably become less common. However, this very diversity presents a tremendous challenge to our ability to understand tropical forest function today and to predict how it will respond to global change, as it means interspecific variation is critical. We currently lack the abundant, species-specific data needed to quantify this interspecific variation and capture it in models. Ground-based studies are small-scale and contain inadequate sample sizes of most plant species, and remote sensing data provide large sample sizes but cannot currently distinguish individual species in diverse tropical forests.
Technological advances in hyperspectral imaging, laser scanning, and artificial intelligence now offer the potential for automated species identification of individual plants using remote sensing, smartphones, and/or other tools. This would enable large-scale, species-specific data collection in tropical forest and empower a wider range of researchers, students, and citizen scientists to contribute. However, to realize this potential we need to collect high-quality training data and analyze these data to develop appropriate machine learning algorithms and determine which types or combinations of data are most useful for species identification.
This project aims to build the basis for automated species identification of woody plants in diverse tropical forests. The specific objectives are:
(1) collect and publish a multidimensional high-quality training dataset including hyperspectral reflectance and three-dimensional structure of whole plants, leaves, bark, fruits and flowers of > 150 woody plant species in central Panama;
(2) quantify within-species variation and between-species distinctiveness in these data;
(3) evaluate the ability to distinguish taxa for each data type and combination of types, and identify the most promising approaches for automated species identification.
The successful completion of the proposed research will lay the groundwork for large-scale collection of taxon-specific data encompassing many co-occurring plant species in tropical forests. Findings on the utility of spectrometer reflectance will be of particular importance for efforts to scale up tropical biodiversity monitoring from large plots like Smithsonian ForestGEO to larger landscapes with airborne and space-borne hyperspectral sensors.

Mentorship goals

The intern will have the opportunity to gain experience in tropical forest field data collection, spectrometer measurements, 3D scanning with laser scanners and photogrammetry, data management using R and GitHub, statistical analyses and figure preparation using the R programming language, reviewing scientific literature, scientific discussions in English, working in a team with lab members and collaborators from diverse backgrounds, preparation of scientific presentations, preparation of data publications, and the scholarly publishing process. The intern will have the opportunity to contribute to and potentially lead a scholarly manuscript, with support from the mentors.
This opportunity is particularly well suited for candidates seeking more research experience prior to graduate school.

Intern’s role, time commitment and expected products

This project has funding for two full-time interns over 12 months (24 person-months total). The expectation is that one intern will focus more on spectrometer measurements and the other more on 3D scanning, and that both will have the opportunity to gain experience with both sets of techniques. Each position may be filled by one candidate for 12 months, or by successive interns for shorter periods (e.g., 6 months and 6 months, or 3 months and 9 months). Start dates are flexible between March and June 2024. In general we seek interns who can commit to at least 6 months, but we will consider appointments as short as 3 months for candidates who can start in March or April 2024.
Each intern will be awarded a stipend of US$1250/month, as well as support for round-trip travel to Panama in the case of non-Panamanian candidates.
The ideal candidate has a bachelor’s degree in a relevant field, ability to conduct tropical forest field work in rugged terrain, strong organizational skills, ability to work well with team members from diverse backgrounds, strong quantitative skills including programming experience, strong English oral and written communication skills, and good Spanish communication skills. For full consideration, please email a (1) CV, (2) an unofficial undergraduate transcript (with an explanation of the grading scheme if from a non-US university), (3) a scientific writing sample, preferably in English but can be in Spanish (e.g., undergraduate thesis, lab report, research paper), (4) a sample of code you have written with commenting (preferably in R or Python, but can be another language), and (5) a cover letter describing your qualifications, interest in the position, potential start dates, and contact information for 3 references to mullerh@si.edu.
Expected products: Each intern is expected to contribute importantly to and therefore be an author on one or more data publications, and will also have the opportunity to contribute as a coauthor (or potentially lead author) on a scholarly manuscript for publication in a scientific journal.

Regularly held occasions for group discussions, attendance at lectures, career counseling, and other educational and experiential opportunities for interns

The intern will participate in weekly lab meetings of the Muller-Landau lab, and will have the opportunity to attend weekly seminars on BCI (Thursdays) and at Tupper (Tuesdays), as well as STRI training opportunities (e.g., periodic offerings in GIS, R programming). The intern will meet weekly with the primary advisor (Helene Muller-Landau) and will also have the opportunity to informally interact with other STRI staff scientists, employees, visiting scientists, fellows, and interns, including over shared meals on BCI.

List of suggested readings

Muller-Landau, H. C., K. C. Cushman, E. E. Arroyo, I. Martinez Cano, K. J. Anderson-Teixeira, and B. Backiel. 2021. Patterns and mechanisms of spatial variation in tropical forest productivity, woody residence time, and biomass. New Phytologist, 229: 3065-3087. https://doi.org/10.1111/nph.17084

Baldeck, C. A., G. P. Asner, R. E. Martin, C. B. Anderson, D. E. Knapp, J. R. Kellner, and S. J. Wright. 2015. Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy. Plos One, 10. https://doi.org/10.1371/journal.pone.0118403

Lee, C. K. F., G. Song, H. C. Muller-Landau, S. Wu, S. J. Wright, K. C. Cushman, R. F. Araujo, S. Bohlman, Y. Zhao, Z. Lin, Z. Sun, P. C. Y. Cheng, M. K.-P. Ng, and J. Wu. 2023. Cost-effective and accurate monitoring of flowering across multiple tropical tree species over two years with a time series of high-resolution drone imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 201: 92-103. https://doi.org/10.1016/j.isprsjprs.2023.05.022

Calders, K., J. Adams, J. Armston, H. Bartholomeus, S. Bauwens, L. P. Bentley, J. Chave, F. M. Danson, M. Demol, M. Disney, R. Gaulton, S. M. Krishna Moorthy, S. R. Levick, N. Saarinen, C. Schaaf, A. Stovall, L. Terryn, P. Wilkes, and H. Verbeeck. 2020. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sensing of Environment, 251: 112102.https://doi.org/10.1016/j.rse.2020.112102

Allen, M. J., S. W. D. Grieve, H. J. F. Owen, and E. R. Lines. 2023. Tree species classification from complex laser scanning data in Mediterranean forests using deep learning. Methods in Ecology and Evolution, 14: 1657-1667. https://doi.org/10.1111/2041-210X.13981