Date of Award
2024
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Integrated Geospatial Technology (MS)
Administrative Home Department
Department of Civil, Environmental, and Geospatial Engineering
Advisor 1
Tao Liu
Advisor 2
Jae Sung Kim
Committee Member 1
Parth Parimalbhai Bhatt
Abstract
Accurately estimating Aboveground Biomass Density (AGBD) is crucial for managing Earth's carbon cycle and informing climate strategies. NASA's GEDI mission advances global forest mapping, but traditional linear models often yield less reliable AGBD estimates. This study enhances AGBD estimation using deep learning models with NEON ground-truth data and simulated GEDI waveforms. We compared 1D CNNs, LSTMs, and pre-trained CNNs to traditional models. The ResNet152 model outperformed linear regression, achieving an R² of 0.68, demonstrating a 17% improvement. Our experiments also demonstrate the importance of large, diverse datasets, particularly for training deep learning models.
Recommended Citation
Mahaur, Ashish, "ABOVEGROUND BIOMASS DENSITY ESTIMATION USING DEEP LEARNING: INSIGHT FROM NEON GROUND-TRUTH DATA AND SIMULATED GEDI WAVEFORM", Open Access Master's Thesis, Michigan Technological University, 2024.
Included in
Forest Management Commons, Physical and Environmental Geography Commons, Remote Sensing Commons, Spatial Science Commons