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Biomass Estimation and Uncertainty Quantification from Tree Height

Song, Qian and Albrecht, Conrad M and Xiong, Zhitong and Zhu, Xiao Xiang (2023) Biomass Estimation and Uncertainty Quantification from Tree Height. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 4833-4845. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2023.3271186. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/document/10141562


We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set of 8,342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass-height-diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model.

Item URL in elib:https://elib.dlr.de/195067/
Document Type:Article
Additional Information:article available as preprint on arXiv: http://arxiv.org/abs/2305.09555
Title:Biomass Estimation and Uncertainty Quantification from Tree Height
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Albrecht, Conrad MUNSPECIFIEDhttps://orcid.org/0009-0009-2422-7289UNSPECIFIED
Date:31 May 2023
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 4833-4845
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Above-ground biomass estimation, model uncertainty, allometric equation, tree height, Gaussian process regression.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Albrecht, Conrad M
Deposited On:23 May 2023 13:06
Last Modified:11 Sep 2023 17:33

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