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High technology to measure forest carbon stocks: How can advanced remote sensing improve biomass quantification in the Brazilian Amazon?

Author(s): Catherine Torres de Almeida

Unesp (São Paulo State University)

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Summary:
A case study on the estimation of aboveground biomass using advanced remote sensors (LiDAR and hyperspectral) in the Brazilian Amazon. Students explore remote sensing (RS) data and compare RS-based models to understand tropical carbon dynamics.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 2.0 - published on 23 Jul 2023 doi:10.25334/HRFQ-MB40 - cite this

Keywords

  1. climate change
  2. remote sensing
  3. hyperspectral
  4. tropical forests
  5. LiDAR data analysis
  6. carbon stocks

Alignments

  1. Resource Type
  2. Courses
  3. Course Level
  4. Bloom's Cognitive Level
  5. Vision and Change Core Competencies
  6. Vision and Change Core Concepts
  7. Four Dimensional Ecology Education (4DEE) Framework
  8. Class Type
  9. Class Size
  10. Audience
  11. Lesson Length
  12. Key Scientific Process Skills
  13. Pedagogical Approaches
  14. Assessment Type
Contents:
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Description

Overview of the Module:

The accurate estimation of aboveground biomass (AGB) is paramount for effective strategies in ecosystem conservation and climate change mitigation. However, estimating AGB at regional and local scales remains a challenge. The good news is that we have powerful tools at our disposal: advanced remote sensing technologies like Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI). These groundbreaking tools offer us a unique perspective on the diverse structure and functionality of forests, capturing details at a sub-meter resolution. By harnessing their synergistic potential, we can improve AGB estimates and contribute to a sustainable future.

In this case study, we will embark on a journey into the Brazilian Amazon, where we explore the potential of airborne LiDAR, HSI, and their synergistic combination. Through experiments with different regression methods, we seek to improve our predictions of AGB in this remarkable rainforest region.

Learning Objectives:

  • Discuss the importance of spatial scale in monitoring forest carbon stocks.
  • Describe two advanced remote sensing technologies (hyperspectral and LiDAR sensors) and explain their working mechanisms.
  • Compare models that predict aboveground biomass from different data sources. Interpret scientific figures to compare the performance of the models and discuss the possible factors that affect carbon stocks.
  • Interpret scientific figures to compare the performance of the models and discuss the possible factors that affect carbon stocks.

4DEE (see list for specifics: https://www.esa.org/4DEE/framework/)

  • Core Ecological Concepts: concepts of living aboveground biomass and carbon stocks, both in the scale of individual trees and landscapes.
  • Ecology Practices: measuring forest carbon stocks, from field measurements to remote sensing monitoring; comparing models to predict aboveground biomass; creating hypotheses; and interpreting scientific figures.
  • Human–Environment Interactions: Discuss the relationships among tropical forest carbon, human disturbances, and global climate change.
  • Cross-cutting Themes: Discuss the importance of spatial scale in monitoring forest carbon stocks and analyze the spatial variation of biomass due to anthropic or environmental factors.

Pedagogical Approaches: Tutorials and activities

Assessment Type and Interactive Data Tools: Questions, homework, interactive map

Access this Module on Gala: https://www.learngala.com/cases/remote-sensing-biomass

Translations available: Português (https://www.learngala.com/cases/sensoriamento-remoto-biomassa)

Support was provided by: A grant from the United States National Science Foundation (DBI-RCN-UBE 2120141).

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