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Project: Quantifying landscape-level variation in forest structure and dynamics with mobile laser scanning and drone photogrammetry

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Project title

Quantifying landscape-level variation in forest structure and dynamics with mobile laser scanning and drone photogrammetry

Mentor name

Helene Muller-Landau, mullerh@si.edu

Location

Barro Colorado Island and/or Gamboa, Panama

Project summary and objectives

Tropical forests vary widely in their structure and dynamics – and thus in their biomass carbon stocks and fluxes – even after controlling for forest age. Forest structure encompasses the horizontal and vertical distribution of vegetation, which is determined by the abundances and spatial arrangement of trees and lianas (woody vines) of different forms and sizes. Forest dynamics encompasses changes over time in this structure due to the growth (or shrinkage) and death of individual trees and lianas.

Traditionally, foresters and ecologists have investigated forest structure and dynamics using forest census plots, in which individual tree stems are mapped, identified to species, and measured in stem diameter. These data have yielded many insights into general patterns of variation in forest structure and dynamics in terms of the abundances of trees of different stem diameter, their diameter growth rates, and their mortality rates. However, these data enable only indirect and approximate estimation of forest biomass carbon stocks and fluxes, because trees vary widely in height and crown form, and thus there is wide variation in tree biomass among trees of a given diameter.

Laser scanning and photogrammetry now enable much more direct measurement of the spatial distribution of vegetation within forests, and thereby provide much more detailed information about forest structure and its change over time. Since 2014, we have been conducting repeat drone flights and photogrammetry to quantify canopy structure and dynamics on the Barro Colorado Island 50 ha plot and other focal sites in central Panama. In 2021, we acquired a handheld mobile laser scanner (Geoslam ZEB Horizon), and began using it to quantify understory forest structure, including the locations, sizes, and detailed 3D form of individual tree and liana trunks and branches. We focus intensive data collection in mapped plots, in which we can relate these data to tagged trees of known species identity. We are also collaborating to link these drone photogrammetry and laser scanning data to satellite remote sensing.

Our objectives are to:

  • Quantify spatial variation in forest structure and dynamics in central Panama at high spatial and temporal resolution over large areas.
  • Investigate how spatial variation in forest structure and dynamics relates to abiotic and biotic drivers including soils, climate, topography, stand age, liana abundance, and tree species composition, and how temporal variation in forest dynamics relates to climate variation.
  • Link laser scanning and drone photogrammetry data to tree census data within mapped plots, to quantify individual tree morphology, crown growth and damage, and mortality, and investigate how these vary with tree species identity, individual tree biotic neighborhood, and site characteristics.
  • Evaluate how stand-level forest structure and dynamics from laser scanning and drone photogrammetry relate to forest census data and to remote sensing products.

Ultimately, this research seeks to contribute to a mechanistic understanding of variation in tropical forest structure and dynamics that can inform better vegetation models and enable us to accurately predict forest responses to global change.

Mentorship goals

Mentor Goals and Intern benefits:

Interns will gain experience in tropical forest field research, collection and processing of mobile laser scanning and/or drone photogrammetry data, data analysis in R or Python, reading and discussing scientific literature, and oral presentation of scientific research to the lab group. Interested interns will have the opportunity to contribute to a scientific publication.

List of suggested readings

Park, J. Y., H. C. Muller-Landau, J. W. Lichstein, S. W. Rifai, J. P. Dandois, and S. A. Bohlman. 2019. Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images. Remote Sensing 11:1534.

Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot. Biogeosciences Discussion

Araujo R. F., J. Q. Chambers, C. H. S. Celes, H. C. Muller-Landau, A. P. F. de Santos, F. Emmert, G. H. P. M. Ribeiro, B. Oliva Gimenez, A. J. N. Lima, M. A. A. Campos, and N. Higuchi. 2020. Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics. PLOS ONE 15:e0243079.

Contact information

Provide us with your contact information to learn more about this opportunity. Tell us a bit about yourself and why are you interested in this project. By filling out this form I understand this is NOT an application for an internship at STRI.

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