Comparison of QRNN and QRF Models in Forest Biomass Estimation Based on the Screening of VIs Using an Equidistant Quantile Method

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成果类型：

期刊论文

作者：

Xu, Xiao;Zhang, Xiaoli;Shen, Shouyun;Zhu, Guangyu

通讯作者：

Shen, SY

作者机构：

[Xu, Xiao; Shen, Shouyun] Cent South Univ Forestry & Technol, Coll Landscape Architecture, Changsha 410004, Peoples R China.

[Xu, Xiao] Yunnan Univ Finance & Econ, Sch Logist & Management Engn, Kunming 650221, Peoples R China.

[Xu, Xiao; Shen, Shouyun] Hunan Big Data Engn Technol Res Ctr Nat Protected, Changsha 410004, Peoples R China.

[Zhang, Xiaoli] Southwest Forestry Univ, Minist Educ, Key Lab Southwest Mt Forest Resources Conservat &, Kunming 650233, Peoples R China.

[Zhu, Guangyu] Cent South Univ Forest & Technol, Forestry Coll, Changsha 410004, Peoples R China.

通讯机构：

[Shen, SY ] C

Cent South Univ Forestry & Technol, Coll Landscape Architecture, Changsha 410004, Peoples R China.

Hunan Big Data Engn Technol Res Ctr Nat Protected, Changsha 410004, Peoples R China.

语种：

英文

关键词：

vegetation indices;quantile regression (QR);quantile regression neural network (QRNN);quantile random forest (QRF);Pinus densata forests

期刊：

Forests

ISSN：

1999-4907

年：

2024

卷：

期：

页码：

782-

DOI：

10.3390/f15050782

基金类别：

This study was supported by the National Natural Science Foundation of China (grant number 32271874), the Yunnan Provincial Department of Education Science Research Fund Project (grant number 2023J0652), the State Forestry Administration Key Disciplines (forest human [2016] No. 21) and the Hunan Province Double First-Class Cultivation Disciplines (Hunan education [2018] No. 469).

机构署名：

本校为第一且通讯机构

院系归属：

林学院

风景园林学院

摘要：

The investigation of a potential correlation between the filtered-out vegetation index and forest aboveground biomass (AGB) using the conventional variables screening method is crucial for enhancing the estimation accuracy. In this study, we examined the Pinus densata forests in Shangri-La and utilized 31 variables to establish quantile regression models for the AGB across 19 quantiles. The key variables associated with biomass were based on their significant correlation with the AGB in different quantiles, and the QRNN and QRF models were constructed accordingly. Furthermore, the optimal quar...

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Comparison of QRNN and QRF Models in Forest Biomass Estimation Based on the Screening of VIs Using an Equidistant Quantile Method

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