ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 489–496, 2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 489–496, 2020

  03 Aug 2020

03 Aug 2020


M. Maimaitijiang1,2, V. Sagan1,2, H. Erkbol1,2, J. Adrian1,2, M. Newcomb3, D. LeBauer4, D. Pauli5, N. Shakoor6, and T. C. Mockler6 M. Maimaitijiang et al.
  • 1Geospatial Institute, Saint Louis University, 3694 West Pine Mall, St. Louis, MO 63108, USA
  • 2Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
  • 3United States Forest Service, Intermountain Region, Ogden, UT 84401, USA
  • 4Arizona Experiment Station, University of Arizona, Tucson, AZ 85721, USA
  • 5School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA
  • 6Donald Danforth Plant Science Center, St. Louis, MO 63132, USA

Keywords: Unmanned Aerial Vehicle (UAV), LiDAR, photogrammetry, canopy height, leaf area index (LAI), phenotyping

Abstract. Canopy height (CH) and leaf area index (LAI) provide key information about crop growth and productivity. A rapid and accurate retrieval of CH and LAI is critical for a variety of agricultural applications. LiDAR and RGB photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. The goal of this study is to investigate the potential of UAV LiDAR and RGB photogrammetry in estimating crop CH and LAI. To this end, a high resolution 32 channel LiDAR and RGB cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data at sorghum fields near Maricopa, Arizona, USA. A series of canopy structure metrics were extracted using LiDAR and RGB photogrammetry-based point clouds. Random Forest Regression (RFR) models were established based on the UAV-LiDAR and photogrammetry-derived metrics and field-measured LAI. The results show that both UAV-LiDAR and RGB photogrammetry demonstrated promising accuracies in CH extraction and LAI estimation. Overall, UAV-LiDAR yielded superior performance than RGB photogrammetry in both low and high canopy density sorghum fields. In addition, Pearson’s correlation coefficient, as well as RFR-based variable importance analysis demonstrated that height-based metrics from both LiDAR and photogrammetric point clouds were more useful than density-based metrics in LAI estimation. This study proved that UAV-based LiDAR and photogrammetry are important tool in sustainable field management and high-throughput phenotyping, but LiDAR is more accurate than RGB photogrammetry due to its greater canopy penetration capability.