1Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China.
2College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, Guangdong 510642, PR China
3Suining County Runqi Investment Co. Ltd., Xuzhou, Jiangsu 221200, PR China.
4Jiangsu Vocational College of Agriculture and Forestry, Zhenjiang, Jiangsu 212400, PR China
| Received 25 Apr 2023 |
Accepted 28 Aug 2023 |
Published 22 Sep 2023 |
With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.
