Song, Qian und Albrecht, Conrad M und Xiong, Zhitong und 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, Seiten 4833-4845. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2023.3271186. ISSN 1939-1404.
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Offizielle URL: https://ieeexplore.ieee.org/document/10141562
Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/195067/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Zusätzliche Informationen: | article available as preprint on arXiv: http://arxiv.org/abs/2305.09555 | ||||||||||||||||||||
Titel: | Biomass Estimation and Uncertainty Quantification from Tree Height | ||||||||||||||||||||
Autoren: |
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Datum: | 31 Mai 2023 | ||||||||||||||||||||
Erschienen in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 16 | ||||||||||||||||||||
DOI: | 10.1109/JSTARS.2023.3271186 | ||||||||||||||||||||
Seitenbereich: | Seiten 4833-4845 | ||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||
ISSN: | 1939-1404 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Above-ground biomass estimation, model uncertainty, allometric equation, tree height, Gaussian process regression. | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||
Hinterlegt von: | Albrecht, Conrad M | ||||||||||||||||||||
Hinterlegt am: | 23 Mai 2023 13:06 | ||||||||||||||||||||
Letzte Änderung: | 11 Sep 2023 17:33 |
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