High-resolution imagery acquired from an unmanned platform to estimate biophysical and geometrical parameters of olive trees under different irrigation regimes

Abstract

The experiments were conducted in a fully-productive olive orchard (cv. Frantoio) at the experimental farm of University of Pisa at Venturina (Italy) in 2015 to assess the ability of an unmanned aerial vehicle (UAV) equipped with RGB-NIR cameras to estimate leaf area index (LAI), tree height, canopy diameter and canopy volume of olive trees that were either irrigated or rainfed. Irrigated trees received water 4–5 days a week (1348 m3 ha-1), whereas the rainfed ones received a single irrigation of 19 m3 ha-1 to relieve the extreme stress. The flight altitude was 70 m above ground level (AGL), except for one flight (50 m AGL). The Normalized Difference Vegetation Index (NDVI) was calculated by means of the map algebra technique. Canopy volume, canopy height and diameter were obtained from the digital surface model (DSM) obtained through automatic aerial triangulation, bundle block adjustment and camera calibration methods. The NDVI estimated on the day of the year (DOY) 130 was linearly correlated with both LAI and leaf chlorophyll measured on the same date (R2 = 0.78 and 0.80, respectively). The correlation between the on ground measured canopy volumes and the ones by the UAV-RGB camera techniques yielded an R2 of 0.71–0.86. The monthly canopy volume increment estimated from UAV surveys between (DOY) 130 and 244 was highly correlated with the daily water stress integral of rainfed trees (R2 = 0.99). The effect of water stress on the seasonal pattern of canopy growth was detected by these techniques in correspondence of the maximum level of stress experienced by the rainfed trees. The highest level of accuracy (RMSE = 0.16 m) in canopy height estimation was obtained when the flight altitude was 50 m AGL, yielding an R2 value of 0.87 and an almost 1:1 ratio of measured versus estimated canopy height.

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

“…This background consists mostly of bare soil, shadows, background vegetation (such as weeds and dwarf shrubs), and other ground targets (such as well houses and other artificial objects). The remote sensing image data from UAVs that are used in most existing studies that derive the parameters of fruit trees not only contain the red band, green band, and blue band, but also contain the Near Infrared (NIR) band [23][24][25]. The NIR band can be used to effectively distinguish vegetation in an image.…”

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

“…Several authors have also tackled the estimation of plant growth-related traits by data fusion from different sensors [11][12][13][14] for the computation of crop surface models based on image mosaicing methods [15][16][17][18]. Other approaches rely on the computation of individual aerial images.…”

Opportunities of Uavs in Orchard Management

“…With the help of UAVs, canopy NDVI showed good correlation with SWP without average pixels calculation in an orchard level. In addition, the prediction result also provides a potential for water stress quantification (Zhao et al 2017 (Caruso et al 2019).…”