Getting Started with GEDI Spaceborne Lidar for Ecosystem Applications
Source: Forest Inventory and Analysis plot locations (in orange) from the U.S. Forest Service lay underneath the GEDI vegetation height data to demonstrate the spatial coverage of GEDI footprints over the continental U.S. Credits.
The Global Ecosystem Dynamics Investigation (GEDI) mission is a space-based laser altimeter designed to measure vertical structure and topography of Earth’s forests with high resolution. GEDI plays a crucial role in assessing forest biomass, understanding carbon dynamics, and monitoring the impact of climate change and land use.
This four-module training series aims to build understanding of GEDI-optimized perspectives of vegetation measurements important for ecosystem management. Since GEDI’s first 3-dimensional waveform lidar sampling across the globe in April, 2019, several types of measurements and derived products have been generated to demonstrate spaceborne waveform lidar’s capability to capture forest structure and biomass. GEDI is an innovative sensor with strong calibration and validation strategies, opportunities to integrate other lidar and field data, and relevance for a multitude of vegetation mapping techniques with its multi-year, wall-to-wall coverage.
Several challenges remain regarding the end-to-end integration of GEDI into operational or professional ecosystem management. Challenges can occur when acquiring the datasets from data repositories, deciding on a data preparation strategy to choose from, and understanding data interpretation over a particular application context. NASA and other collaborators have invested much into data acquisition pipelines, and this training helps to address considerations of quality control as well as variability of the data characteristics over localized contexts.
Because GEDI is still so novel, every real-world application offers helpful perspectives on GEDI’s value to users. However, users need to be equipped to explore and adopt GEDI datasets in order to provide such feedback. In addition, advancing GEDI’s integration into ecological modelling, biodiversity studies, global forest management, assessing wildfire risk, and much more, requires integrating skills in remote sensing methods and modelling.
The hope is that these training resources will help facilitate the users’ exploration and integration of 3-dimensional information into their studies, starting with ecosystem applications.
Throughout the four modules, the training participants will learn about the goals and potential ecological applications of GEDI, and compare U.S. and global vegetation mapping case studies. Reference guides and product reviews will help the user prioritize datasets and implement processing best practices. Participants will be equipped with the theoretical foundations and metrics necessary to evaluate GEDI capabilities. The applied science recommendations draw from NASA-funded projects in Nepal, Brazil, Peru, and the U.S., with hands-on tutorials for anyone to adapt over user defined ecosystems or scenarios.
GEDI’s Potential for Improved Land Management
This training series gathers resources and examples of GEDI applications to help exemplify real-world scenarios where GEDI based information can support land management strategies.
Particular focus is placed on:
1) Easing knowledge building and access to relevant resources for understanding GEDI’s data products
2) Equipping users with starting points for conducting their own data explorations, data preparation, and analysis.
The training is meant to:
- Address the limited amount of resources containing contextually specific data acquisition pipelines, data subsetting, and analysis options using open-source resources.
- Facilitate adoption and integration of GEDI metrics that can improve land management strategies.
- Prepare users for future improvements, fused data products, and future satellite/sensor missions advancing spaceborne lidar technology.
- Invite and prepare users to join the community discovering and contributing examples of effective or limited GEDI use.
The intended audiences include end users interested in monitoring, managing, and understanding the impacts of disturbances of vegetated areas:
- Modules 1, 2, and 4 will be geared toward more technical teams, scientists, researchers, analysts, and land managers using geospatial tools to support ecosystem management decisions. For example, technical teams could support the analysis of ecosystem extent and health, carbon estimation, or conservation and restoration planning where GEDI information is crucial.
- Module 3, while also beneficial to technical and researcher/analyst teams, includes user-friendly tools accessible to policymakers, community members, and non-scientists to learn how to interpret and navigate more tools derived from the kinds of technologies and methods overviewed in Modules 1 and 2.
- Overarchingly, professional audiences can benefit from understanding how GEDI products can be best applied to decision-making in their areas of expertise such as land managers addressing topics in fire, water quality, and water conservation.
Learning Objectives
After completing this module, participants will be able to:
- Describe essential physical principles of lidar remote sensing and its sensitivity to biophysical parameters.
- Understand what the GEDI lidar mission has to offer, its theoretical background, data products, and the future iterations.
- Interpret GEDI Level 1-4 data products including the raw waveform data and vegetation metrics over various vegetation types
- Test recommended quality filtering and processing techniques.
- Search, select, and acquire lidar data with recommendations for testing and applying data quality processing methods.
- Generate and validate canopy height, biomass, and other structural analyses of ecosystems with field and real-world use examples.
- Understand and use standard GEDI wall-to-wall biomass products.
- Access the open-source OBIWAN API to generate and serve estimates of biomass change for a designated areas of interest.
- Understand how OBIWAN can be used to create baseline scenarios needed to identify additionality of forest carbon projects.
Co-developers, Contributors, & Acknowledgements
Please cite as:
Jiménez, S., Mayer, T., Pinto, N., Cooley, S., Healey, S., Christine Evans, Numata, I., Horn, K., West, D., Walker, K., Abramowitz, J., Cruz, S., Martin Arias, V., Pransky, L., Kruskopf, M., Yang, Z., Johnson, L., Fareed, N., d’Oliveira, M., … Billy Ashmall. (2025). NASA-EarthRISE/training_Getting_started_with_GEDI_spaceborne_lidar: v1.0.0 (First-release). Zenodo. https://doi.org/10.5281/zenodo.17353798
Co-developers & Contributors:
*Stephanie Jiménez 1, 2
*Timothy Mayer 1, 2
*Naiara Pinto 3
*Savannah S Cooley 9
*Sean Healey 4, 18
*Christine Evans 1, 2 *Izaya Numata 5
*Kevin Horn 1 *Diana West 1, 2 *Kaitlin Walker 1, 2
*Jacob Abramowitz 1, 2 *Sativa Cruz 9 *Vanesa Martin Arias 1, 2 Lena Pransky 1, 2
Meryl Kruskopf 1, 2 Zhiqiang Yang 9
Lucas Johnson 11
Nadeem Fareed 12
Marcus d’Oliveira 13
Willian Antonio Melo 14
Sonaira Souza Silva 14
Sidney Novoa 15
Karis Tenneson 16
Andrea Nicolau 16
Mark Cochrane 17
Alexandre Goberna 1, 19
Billy Ashmall 1, 19
Acknowledgements:
Emil Cherrington 1, 2
Kelsey Herndon 1, 2
Robert Kennedy 11
Michael Keller 3, 18
Laura Duncanson 6, 18
Andrew Neil Sagar 6
Ralph Dubayah 6, 18
Eric Anderson 1
Jake Ramthun 1, 2
Micky Maganini 1, 2
Phoebe Oduor 1, 2
Sarah Cox 1, 2
Kathleen Cutting 20
Daniel Irwin 1
Rodrigo Torres 21
Sandra Terran 21
Lorena Caiza 21
Caroline Salomao 22
Alvaro Paz 23
Osmar Yupanqui 15
Fernanda Lopez Ornelas 16
* Lead co-developers 1) NASA Earth Action, Marshall Space Flight Center (MSFC)
2) University of Alabama in Huntsville, Lab for Applied Sciences
3) NASA Jet Propulsion Laboratory (JPL) & California Institute of Technology
4) USDA Forest Service
5) South Dakota State University
6) University of Maryland
7) NASA Goddard Space Flight Center (GSFC)
8) NASA ROSES Applied Science Team (AST) for past NASA SERVIR program
9) NASA Ames Research Center & Bay Area Environmental Research Institute (BAERI)
10) NASA Applied Remote Sensing Training (ARSET)
11) Oregon State University
12) University of Florida
13) Embrapa-Acre
14) Universidade Federal do Acre
15) Conservación Amazónica (ACCA)
16) Spatial Informatics Group (SIG)
17) University of Maryland, Center for Environmental Science (UMCES)
18) NASA GEDI Mission Team
19) Universities Space Research Association
20) Amentum, Space Exploration Division
21) EcoCiencia
22) Imaflora
23) Alliance Bioversity International - CIAT
Navigation
The lesson content for each of the modules can be found in the tabs on the left hand side panel of the webpage. There are individual pages for supplementary resources, and pre and post surveys for participants to fill out. Tutorials and hands-on exercises are found in this Github repository but are also linked to the respective background page/tab.
Questions
If you have any questions about the material, please submit a question below! We are here to help.
All Modules and Topics Overview
Overview of topics covered in each module, respective format(s), and any technical requirements are listed below. Additional prerequisites and background information will be detailed within each module.
Module 1
Introduction to Full Waveform Lidar introduces lidar data, its physical principles, and its sensitivity to biophysical parameters, and explores several U.S.-based and global applications using different lidar sensors. Exercises will solidify participants’ understanding of waveform lidar capabilities in terrestrial applications.
Level: introductory to intermediate.
| Section | Topics | Format | Requirements | |||
|---|---|---|---|---|---|---|
| Introduction to GEDI Full Waveform Lidar for Terrestrial Applications | Fundamentals of Lidar Remote Sensing | Recorded lecture | Video and audio viewing | |||
| Introduction to GEDI Full Waveform Lidar for Terrestrial Applications | Full Waveform Lidar from the GEDI Mission | Recorded lecture | Video and audio viewing | |||
| Practical Applications with GEDI Full Waveform Data | Tutorial: A comprehensive Python notebook for processing and analyzing NASA GEDI L1B (Global Ecosystem Dynamics Investigation Level 1B) full waveform LiDAR data using Google Colab | Recorded demonstration and self-paced python tutorial | Google account and Drive | NASA EarthData Access Account | Python platform Google Colab notebooks | Github account |
Module 2
A Deep Dive into GEDI details the GEDI mission, its data products, and case study applications. Exploratory scripts provide starting points for participants to evaluate many of GEDI’s data products by incorporating recommended filtering and pre-processing strategies.
Level: introductory to intermediate.
| Section | Topics | Format | Requirements | |||
|---|---|---|---|---|---|---|
| Lasering in on the GEDI Mission | Why GEDI? | Self-paced lecture | ||||
| Lasering in on the GEDI Mission | Navigating the GEDI Ecosystem | Self-paced lecture | ||||
| Lasering in on the GEDI Mission | Tools for Navigating GEDI | Self-paced lecture | ||||
| Lasering in on the GEDI Mission | The Building Block of Analysis | Self-paced lecture | ||||
| Lasering in on the GEDI Mission | How GEDI Information Ecosystem Studies | Self-paced lecture | ||||
| From Ground to Canopy | GEDI’s Elevation and Relative Height Metrics Explained | Self-paced lecture | ||||
| From Canopy Layers to Vertical Profiles | Vegetation Structural Insights From GEDI | Self-paced lecture | ||||
| From Canopy Layers to Vertical Profiles | Tutorial: Exploring Forest Structure with GEDI L2B in the Southeast | Self-paced python tutorial | Google account and Drive | NASA EarthData Access Account | Python platform Google Colab notebooks | Github account |
| From Canopy Layers to Vertical Profiles | Tutorial: Comparing L2B PAI with high-resolution lidar in the Sewanee Domain, Tennessee | Self-paced python tutorial | Google account and Drive | NASA EarthData Access Account | Python platform Google Colab notebooks | Github account |
| Above Ground Biomass with GEDI | GEDI’s Biomass Estimation Approach | Self-paced lecture | ||||
| Above Ground Biomass with GEDI | Tutorial: Exploring Biomass with GEDI L4A and L4B in the Southeast | Self-paced python tutorial | Google account and Drive | NASA EarthData Access Account | Python platform Google Colab notebooks | Github account |
Module 3
Biomass Change with OBIWAN discusses downstream GEDI derived products for carbon and biomass monitoring with Online Biomass Inference using Waveforms and iNventory (OBIWAN) Application Programming Interface (API). Participants will learn how biomass products are derived and their advantages and limitations. This module shows participants how to create dashboards and reporting systems for any geography covering any period from 1985 to the present.
Level: introductory to intermediate.
| Section | Topics | Format | Requirements | |||
|---|---|---|---|---|---|---|
| From Estimating Biomass with GEDI to Estimating Biomass Change with OBIWAN API | GEDI mission biomass estimation: theory and products | Recorded lecture | Video and audio viewing | |||
| From Estimating Biomass with GEDI to Estimating Biomass Change with OBIWAN API | OBIWAN: estimating biomass change with GEDI and Landsat time series | Recorded lecture | Video and audio viewing | |||
| Customize OBIWAN through its API | Tutorial: Using the OBIWAN API in Alabama | Recorded demonstration of python and Google Earth Engine tutorials | Video and audio viewing | Google account and Drive | Github account | Optional Google Earth Engine Account |
Module 4
Localized Forest Biomass Estimation will have advanced applications focused on GEDI derived products for monitoring forest disturbances and carbon dynamics. Dr. Izaya Numata (South Dakota State University) will present methods for forest biomass estimation using field, airborne, and satellite lidar (GEDI) data developed in Acre, Brazil. Participants will understand the advantages, limitations, and considerations for applying such methodology on their own.
Level: Intermediate to advanced.
| Section | Topics | Format | Requirements | ||
|---|---|---|---|---|---|
| Forest Biomass Estimation Using Data from Field, Airborne and Spaceborne Lidar | Learn to develop local forest biomass model using field and remote sensing data and its application for AGB mapping | Self-paced lecture | |||
| Calibrating Field, Airborne and Spaceborne Lidar | Tutorial: Exercise1) GEDI waveform simulation with airborne lidar using rGEDI. Exercise 2) Development of local AGB model with field AGB and simulated ALS Relative Height metrics in R. Exercise 3) Above ground biomass change mapping in Google Earth Engine. | Self-paced RStudio and Google Earth Engine tutorials | Github account | RStudio, RTools, rGEDI, and rGEDIsimulator package and program installations | Google Earth Engine Account |