Health assessment of plantations based on LiDAR canopy spatial structure parameters-Reference-Cited by-同舟云学术

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Health assessment of plantations based on LiDAR canopy spatial structure parameters

Published:2022-04-10 Issue:1 Volume:15 Page:712-729
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Meng Pengyu¹^ORCID,Wang Hong¹^ORCID,Qin Shuhong²^ORCID,Li Xiuneng²^ORCID,Song Zhenglin¹^ORCID,Wang Yicong¹,Yang Yi¹,Gao Jay³^ORCID

Affiliation:

1. School of Hydrology and Water resources, Hohai University, Nanjing, People’s Republic of China

2. School of Earth Science and Engineering, Hohai University, Nanjing, People’s Republic of China

3. School of Environment, Faculty of Science, University of Auckland, Auckland, New Zealand

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2022.2059114

Reference56 articles.

1. Engaging Stakeholders on Forest Health: A Model for Integrating Climatic, Ecological, and Societal Indicators at the Watershed Scale

2. From The Cover: Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy

3. UAV-based canopy textures assess changes in forest structure from long-term degradation

4. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data

5. A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship

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3. Detecting urban tree canopy using convolutional neural networks with aerial images and LiDAR data;Journal of Plant Diseases and Protection;2024-02-13

4. Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass;Ecological Informatics;2023-12

5. Ecological restoration evaluation of afforestation in Gudao Oilfield based on multi-source remote sensing data;Ecological Engineering;2023-12

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