Biomass Estimation and Uncertainty Quantification From Tree Height
We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameter estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual-tree levels employing a Gaussian proce...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10141562/ |
| _version_ | 1797774904102223872 |
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| author | Qian Song Conrad M. Albrecht Zhitong Xiong Xiao Xiang Zhu |
| author_facet | Qian Song Conrad M. Albrecht Zhitong Xiong Xiao Xiang Zhu |
| author_sort | Qian Song |
| collection | DOAJ |
| description | We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameter estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual-tree levels employing a Gaussian process regressor. In order to validate the proposed model, a set of 8342 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. The data used in this study are available at <uri>https://github.com/zhu-xlab/BiomassUQ</uri>. |
| first_indexed | 2024-03-12T22:27:56Z |
| format | Article |
| id | doaj.art-b639b1e58ce4429293c95c5c961fd163 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-12T22:27:56Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-b639b1e58ce4429293c95c5c961fd1632023-07-21T23:00:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01164833484510.1109/JSTARS.2023.327118610141562Biomass Estimation and Uncertainty Quantification From Tree HeightQian Song0Conrad M. Albrecht1Zhitong Xiong2https://orcid.org/0000-0002-3953-585XXiao Xiang Zhu3https://orcid.org/0000-0001-5530-3613Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Munich, GermanyChair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, GermanyChair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, GermanyWe propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameter estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual-tree levels employing a Gaussian process regressor. In order to validate the proposed model, a set of 8342 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. The data used in this study are available at <uri>https://github.com/zhu-xlab/BiomassUQ</uri>.https://ieeexplore.ieee.org/document/10141562/Above-ground biomass (AGB) estimationallometric equationGaussian process regressionmodel uncertaintytree height |
| spellingShingle | Qian Song Conrad M. Albrecht Zhitong Xiong Xiao Xiang Zhu Biomass Estimation and Uncertainty Quantification From Tree Height IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Above-ground biomass (AGB) estimation allometric equation Gaussian process regression model uncertainty tree height |
| title | Biomass Estimation and Uncertainty Quantification From Tree Height |
| title_full | Biomass Estimation and Uncertainty Quantification From Tree Height |
| title_fullStr | Biomass Estimation and Uncertainty Quantification From Tree Height |
| title_full_unstemmed | Biomass Estimation and Uncertainty Quantification From Tree Height |
| title_short | Biomass Estimation and Uncertainty Quantification From Tree Height |
| title_sort | biomass estimation and uncertainty quantification from tree height |
| topic | Above-ground biomass (AGB) estimation allometric equation Gaussian process regression model uncertainty tree height |
| url | https://ieeexplore.ieee.org/document/10141562/ |
| work_keys_str_mv | AT qiansong biomassestimationanduncertaintyquantificationfromtreeheight AT conradmalbrecht biomassestimationanduncertaintyquantificationfromtreeheight AT zhitongxiong biomassestimationanduncertaintyquantificationfromtreeheight AT xiaoxiangzhu biomassestimationanduncertaintyquantificationfromtreeheight |