Retrieval performance of mangrove tree heights using multiple machine learning regression models and UAV-LiDAR point clouds-Reference-Cited by-同舟云学术

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Retrieval performance of mangrove tree heights using multiple machine learning regression models and UAV-LiDAR point clouds

Published:2024-08-19 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Fu Bolin¹,Jiang Linhang¹,Yao Hang¹,Wei Yingying¹,Jia Mingming²,Sun Weiwei³,Yang Yanli⁴,Deng Tengfang¹

Affiliation:

1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of China

2. Northeast Institute of Geography and Agroecology, CAS, Changchun, People’s Republic of China

3. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, People’s Republic of China

4. College of Geography and Environmental Science, Hainan Normal University, Haikou, People’s Republic of China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangxi Zhuang Autonomous Region

Zhejiang Province ‘Pioneering Soldier’ and ‘Leading Goose’ R&D Project

Youth Scientist Project National Key R&D Program of China

Natural Science Foundation of Hainan Province

Guilin University of Technology Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2392851

Reference47 articles.

1. Salinity reduces site quality and mangrove forest functions. From monitoring to understanding

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

3. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping

4. The value of estuarine and coastal ecosystem services

5. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

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