Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging

Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle...

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Main Authors: Zhaoying Song, Federico Tomasetto, Xiaoyun Niu, Wei Qi Yan, Jingmin Jiang, Yanjie Li
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2022-01-01
Series:Plant Phenomics
Online Access:http://dx.doi.org/10.34133/2022/9783785
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author Zhaoying Song
Federico Tomasetto
Xiaoyun Niu
Wei Qi Yan
Jingmin Jiang
Yanjie Li
author_facet Zhaoying Song
Federico Tomasetto
Xiaoyun Niu
Wei Qi Yan
Jingmin Jiang
Yanjie Li
author_sort Zhaoying Song
collection DOAJ
description Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h2). The results showed a promising correlation between UAV and ground truth data with a range of R2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h2 for all traits ranges from 0.13 to 0.47, where site influenced the h2 value of slash pine trees, where h2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.
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spelling doaj.art-3d0df0938e0f4837bd8552e2dfd2eedd2022-12-22T03:02:28ZengAmerican Association for the Advancement of Science (AAAS)Plant Phenomics2643-65152022-01-01202210.34133/2022/9783785Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based ImagingZhaoying Song0Federico Tomasetto1Xiaoyun Niu2Wei Qi Yan3Jingmin Jiang4Yanjie Li5Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province, China; College of Landscape and Travel, Agricultural University of Hebei, Baoding, ChinaAgResearch Ltd., Christchurch 8140, New ZealandCollege of Landscape and Travel, Agricultural University of Hebei, Baoding, ChinaAuckland University of Technology, Auckland 1010, New ZealandResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province, ChinaResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province, ChinaTraditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h2). The results showed a promising correlation between UAV and ground truth data with a range of R2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h2 for all traits ranges from 0.13 to 0.47, where site influenced the h2 value of slash pine trees, where h2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.http://dx.doi.org/10.34133/2022/9783785
spellingShingle Zhaoying Song
Federico Tomasetto
Xiaoyun Niu
Wei Qi Yan
Jingmin Jiang
Yanjie Li
Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
Plant Phenomics
title Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
title_full Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
title_fullStr Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
title_full_unstemmed Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
title_short Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging
title_sort enabling breeding selection for biomass in slash pine using uav based imaging
url http://dx.doi.org/10.34133/2022/9783785
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AT weiqiyan enablingbreedingselectionforbiomassinslashpineusinguavbasedimaging
AT jingminjiang enablingbreedingselectionforbiomassinslashpineusinguavbasedimaging
AT yanjieli enablingbreedingselectionforbiomassinslashpineusinguavbasedimaging