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Updated: May 8, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Published on: February 9, 2024

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical

Li Xu¹, Jinge Yu¹, Qingtai Shu¹

¹Faculty of College of Soil and Water Conservation, Southwest Forestry University, Kunming, China.

Frontiers in Plant Science

|December 26, 2024

Summary

Estimating forest biomass using GEDI LiDAR data requires careful selection of footprint density. Reducing GEDI footprint density improves biomass prediction accuracy for spruce-fir forests.

Keywords:

GEDI biomass inverse distance weighting spaceborne LiDAR spruce-fir

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Area of Science:

Forestry
Remote Sensing
Ecology

Background:

Accurate forest biomass estimation is crucial for assessing forest quality and understanding the carbon cycle.
Spaceborne LiDAR data, such as from the Global Ecosystem Dynamics Investigation in Space (GEDI), offers potential for regional biomass assessment.
Discontinuous GEDI footprint data presents a challenge for continuous surface mapping.

Purpose of the Study:

To investigate the impact of GEDI footprint density on biomass estimation accuracy.
To develop a method for mapping forest biomass using GEDI data and ground-truth plots.
To provide a methodological reference for optimizing GEDI footprint selection.

Main Methods:

Inverse distance weighted interpolation was used to map GEDI echo indexes to the surface.
The influence of varying numbers of GEDI footprints on interpolation was analyzed.
Random forest algorithm was employed to estimate spruce-fir biomass using GEDI parameters and 138 sample plots.

Main Results:

Higher GEDI footprint density resulted in denser distribution and more pronounced stripe phenomena.
Biomass prediction accuracy improved with decreased GEDI footprint density, with optimal results using footprints extracted every 100 shots.
Estimated spruce-fir biomass ranged from 51.33 to 179.83 t/hm², with an average of 101.98 t/hm².

Conclusions:

The number and distribution of GEDI footprints significantly impact interpolation accuracy for surface information.
Optimizing GEDI footprint selection is essential for reliable forest biomass and vertical structure parameter derivation.
This study offers a methodological framework for selecting appropriate GEDI footprint densities for ecosystem studies.