A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.

Li, Minghui; Yan, Enping; Zhou, Hui; Zhu, Jiaxing; Jiang, Jiawei; Mo, Dengkui

Li, Minghui; Yan, Enping; Zhou, Hui; Zhu, Jiaxing; Jiang, Jiawei; Mo, Dengkui.

Affiliation

Li M; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China.
Yan E; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China.
Zhou H; College of Forestry, Central South University of Forestry and Technology, Changsha, China.
Zhu J; Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China.
Jiang J; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China.
Mo D; College of Forestry, Central South University of Forestry and Technology, Changsha, China.

Front Plant Sci ; 13: 1006795, 2022.

Article de En | MEDLINE | ID: mdl-36212293

Mots clés

cliff; close-range photogrammetry; structure from motion; unmanned aerial vehicle; vegetation cover

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Front Plant Sci Année: 2022 Type de document: Article Pays d'affiliation: Chine Pays de publication: Suisse

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