Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Thematic Layering in GIS01:30

Thematic Layering in GIS

35
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
35
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Climate impacts from North American boreal forest fires.

Nature geoscience·2026
Same author

Nine changes needed to deliver a radical transformation in biodiversity measurement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Maps of forest vertical structure for Colombia, a megadiverse country.

Scientific data·2025
Same author

The contribution of other effective area-based conservation measures (OECMs) to protecting global biodiversity.

Nature communications·2025
Same author

Airborne imaging spectroscopy surveys of Arctic and boreal Alaska and northwestern Canada 2017-2023.

Scientific data·2025
Same author

Reply to: Causal claims, causal assumptions and protected area impact.

Nature·2025

Related Experiment Video

Updated: Jun 17, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

Multi-resolution gridded maps of vegetation structure from GEDI.

Patrick Burns1, Christopher R Hakkenberg2, Scott J Goetz2

  • 1School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA. Patrick.Burns@nau.edu.

Scientific Data
|August 14, 2024
PubMed
Summary

Global Ecosystem Dynamics Investigation (GEDI) lidar data were used to create detailed maps of 3D vegetation structure. These maps enhance understanding of climate, carbon, and habitat dynamics.

More Related Videos

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

379
Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
12:03

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

6.1K

Related Experiment Videos

Last Updated: Jun 17, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

379
Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
12:03

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

6.1K

Area of Science:

  • Earth Observation
  • Ecology
  • Forestry

Background:

  • Three-dimensional vegetation structure mapping is crucial for ecosystem studies.
  • Global Ecosystem Dynamics Investigation (GEDI) provides extensive lidar data.

Purpose of the Study:

  • To generate large-extent, analysis-ready maps of 36 vegetation structure metrics.
  • To provide data at multiple spatial resolutions (1, 6, and 12 km).

Main Methods:

  • Aggregated over 7 billion GEDI lidar shots.
  • Utilized 8 statistics (mean, median, standard deviation, etc.) to grid data.
  • Quantified uncertainty using bootstrapping and validated with airborne laser scanning data.

Main Results:

  • Produced gridded rasters of 36 vegetation structure metrics.
  • Demonstrated higher accuracy in mid-latitudes due to data density.
  • Central tendency statistics were more accurate than variability statistics.

Conclusions:

  • The GEDI-derived vegetation structure maps are valuable for global environmental monitoring.
  • Accuracy is influenced by geographic location and vegetation density.
  • Understanding data limitations is key for effective application.