Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding

被引:90
|
作者
Walter, James D. C. [1 ,2 ]
Edwards, James [1 ,2 ]
McDonald, Glenn [1 ]
Kuchel, Haydn [1 ,2 ]
机构
[1] Univ Adelaide, Sch Agr Food & Wine, Gien Osmond, SA, Australia
[2] Australian Grain Technol Pty Ltd, Roseworthy, SA, Australia
来源
关键词
wheat; phenomics; high throughput phenotyping; field phenotyping; plant breeding; YIELD; PLATFORM;
D O I
10.3389/fpls.2019.01145
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Above-ground biomass (AGB) is a trait with much potential for exploitation within wheat breeding programs and is linked closely to canopy height (CH). However, collecting phenotypic data for AGB and CH within breeding programs is labor intensive, and in the case of AGB, destructive and prone to assessment error. As a result, measuring these traits is seldom a priority for breeders, especially at the early stages of a selection program. LiDAR has been demonstrated as a sensor capable of collecting three-dimensional data from wheat field trials, and potentially suitable for providing objective, non-destructive, high-throughput estimates of AGB and CH for use by wheat breeders. The current study investigates the deployment of a LiDAR system on a ground-based high-throughput phenotyping platform in eight wheat field trials across southern Australia, for the nondestructive estimate of AGB and CH. LiDAR-derived measurements were compared to manual measurements of AGB and CH collected at each site and assessed for their suitability of application within a breeding program. Correlations between AGB and LiDAR Projected Volume (LPV) were generally strong (up to r = 0.86), as were correlations between CH and LiDAR Canopy Height (LCH) (up to r = 0.94). Heritability (H-2) of LPV (H-2 = 0.32-0.90) was observed to be greater than, or similar to, the heritability of AGB (H-2 = 0.12-0.78) for the majority of measurements. A similar level of heritability was observed for LCH (H-2 = 0.41-0.98) and CH (H-2 = 0.49-0.98). Further to this, measurements of LPV and LCH were shown to be highly repeatable when collected from either the same or opposite direction of travel. UDAR scans were collected at a rate of 2,400 plots per hour, with the potential to further increase throughput to 7,400 plots per hour. This research demonstrates the capability of LiDAR sensors to collect high-quality, non-destructive, repeatable measurements of AGB and CH suitable for use within both breeding and research programs.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] FOREST CANOPY HEIGHT ESTIMATION FROM CALIPSO LIDAR MEASUREMENT
    Lu, Xiaomei
    Hu, Yongxiang
    Lucker, Patricia L.
    Trepte, Charles
    27TH INTERNATIONAL LASER RADAR CONFERENCE (ILRC 27), 2016, 119
  • [32] SPECTRAL PROCEDURES FOR ESTIMATING CROP BIOMASS
    WANJURA, DF
    HATFIELD, JL
    TRANSACTIONS OF THE ASAE, 1985, 28 (03): : 922 - 927
  • [33] SAR RADARGRAMMETRY AND SCANNING LIDAR IN PREDICTING FOREST CANOPY HEIGHT
    Vastaranta, Mikko
    Holopainen, Markus
    Karjalainen, Mika
    Kankare, Ville
    Hyyppa, Juha
    Kaasalainen, Sanna
    Hyyppa, Hannu
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6515 - 6518
  • [34] A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America
    Sexton, Joseph O.
    Bax, Tyler
    Siqueira, Paul
    Swenson, Jennifer J.
    Hensley, Scott
    FOREST ECOLOGY AND MANAGEMENT, 2009, 257 (03) : 1136 - 1147
  • [35] Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR
    Brede, Benjamin
    Lau, Alvaro
    Bartholomeus, Harm M.
    Kooistra, Lammert
    SENSORS, 2017, 17 (10)
  • [36] Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning
    Pickstone, Brianna J.
    Graham, Hugh A.
    Cunliffe, Andrew M.
    SUSTAINABLE ENVIRONMENT, 2025, 11 (01):
  • [37] Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes
    Qiuli Yang
    Yanjun Su
    Tianyu Hu
    Shichao Jin
    Xiaoqiang Liu
    Chunyue Niu
    Zhonghua Liu
    Maggi Kelly
    Jianxin Wei
    Qinghua Guo
    ForestEcosystems, 2022, 9 (05) : 617 - 629
  • [38] Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes
    Yang, Qiuli
    Su, Yanjun
    Hu, Tianyu
    Jin, Shichao
    Liu, Xiaoqiang
    Niu, Chunyue
    Liu, Zhonghua
    Kelly, Maggi
    Wei, Jianxin
    Guo, Qinghua
    FOREST ECOSYSTEMS, 2022, 9
  • [39] Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data
    Oliveira, Pedro V. C.
    Zhang, Hankui K.
    Zhang, Xiaoyang
    REMOTE SENSING, 2024, 16 (14)
  • [40] Re-Estimating GEDI Ground Elevation Using Deep Learning: Impacts on Canopy Height and Aboveground Biomass
    Mitsuhashi, Rei
    Sawada, Yoshito
    Tsutsui, Ken
    Hirayama, Hidetake
    Imai, Tadashi
    Sumita, Taishi
    Kajiwara, Koji
    Honda, Yoshiaki
    REMOTE SENSING, 2024, 16 (23)