Superior Clone Selection in a Eucalyptus Trial Using Forest Phenotyping Technology via UAV-Based DAP Point Clouds and Multispectral Images

Abstract

The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability (Rc) of the traits were all above 0.9, and the individual repeatability values (Ri) were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.