Forest above-ground biomass estimation using NASA GEDI lidar waveforms and global tree allometry

Author

Ian Grant, CUNY Hunter CollegeFollow

Date of Award

Fall 1-3-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Geography

First Advisor

Dr. Wenge Ni-Meister

Second Advisor

Dr. Shipeng Sun

Academic Program Adviser

Shipeng Sun

Abstract

Above-ground forest biomass plays a crucial role in global carbon cycles, yet accurately estimating biomass at global scales remains challenging. This thesis addresses two key challenges in processing NASA’s Global Ecosystem Dynamics Investigation (GEDI) space-borne lidar data to estimate above-ground biomass density (AGBD): accounting for global variation in forest structure and developing robust physical interpretations of lidar returns. The first component of the thesis analyzes global patterns of tree allometry using the Tallo tree allometry dataset, examining relationships between tree dimensions across biomes, continents, and plant functional types. This analysis reveals consistent allometry across continents for some biomes (e.g., tropical rainforests) but substantial geographic variation for others (e.g., temperate broadleaf forests). Results demonstrate that leaf type rather than phenology drives much of the observed variation in height- diameter relationships, with site-level allometry correlating with forest height at scales of 250-300 km. The second component of the thesis implements the Ni-Meister Biomass Index Model (NMBIM), a physics-based framework for calculating AGBD from GEDI waveforms, on NASA’s Multi-mission Algorithm and Analysis Platform (MAAP). A modular Python-based processing pipeline was developed to handle GEDI’s high data volumes in a cloud computing environment. The implementation was used to test NMBIM results against GEDI L4A AGBD estimates in the continental United States with US Forest Service field data as ground truth. NMBIM with biome- specific allometry outperforms both the standard GEDI L4A product and NMBIM with constant allometry when validated against US Forest Service ground measurements (R² = 0.77 vs 0.76 and 0.75, respectively), with particular improvement in western conifer forests. These findings demonstrate that combining a physics-based lidar model with empirically-derived allometric parameters can improve global biomass estimation while providing insights into structural variation in global forests.

Recommended Citation

Grant, Ian, "Forest above-ground biomass estimation using NASA GEDI lidar waveforms and global tree allometry" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/1274