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

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.