Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China-Reference-Cited by-同舟云学术

Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China

Published:2024-02-13 Issue: Volume: Page:1-20
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Jin Hungil¹²,Zhao Yinghui¹^ORCID,Pak Unil³,Zhen Zhen¹^ORCID,So Kumryong²⁴^ORCID

Affiliation:

1. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China

2. Faculty of Forest Science, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

3. Center for Ecological Research, Northeast Forestry University, Harbin, China

4. College of Forestry Economics and Management, Northeast Forestry University, Harbin, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2311261

Reference85 articles.

1. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

2. Support Vector Machines for Classification

3. The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

4. Bai, L. Z., Y. X. Shu, and Y. X. Guo. 2020. “Estimating Aboveground Biomass of Urban Trees by High Resolution Remote Sensing Image: A Case Study in Hengqin, Zhuhai, China.” Paper presented at the IOP conference series: Earth and Environmental Science, Chengdu City, China, July 18–20. 569 (1): 12053.

5. Estimating aboveground biomass of urban trees by high resolution remote sensing image: a case study in Hengqin, Zhuhai, China

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