TOWARDS GLOBAL FOREST BIOMASS ESTIMATORS FROM TREE HEIGHT DATA

被引：3

作者：

Song, Qian ^{[1
]}

Albrecht, Conrad M. ^{[1
]}

Xiong, Zhitong ^{[2
]}

Zhu, Xiao Xiang ^{[1
,2
]}

机构：

[1] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany

[2] Tech Univ Munich, Data Sci Earth Observat, Munich, Germany

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

Tree biomass estimation; allometric equation; random forest models; Gaussian process regression; ALLOMETRIC EQUATIONS; ABOVEGROUND BIOMASS; VOLUME;

D O I：

10.1109/IGARSS46834.2022.9884904

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In order to estimate tree biomass, allometric equations take tree parameters such as tree height, wood density, circumference of trunk, and crown diameter as input parameters. Given that most of these quantities are challenging to be extracted from remote sensing data, we evaluate the option to approximate biomass by tree height only. We study our approach by evaluating linear regression, random forest, and Gaussian process regressor models when applied to the 2016 Jucker dataset. Results indicate that linear models fail to properly capture the relationship between biomass and tree height, but the Gaussian process regressor outperms the other two candidate models.

引用

页码：5652 / 5655

页数：4

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