Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling.

Area of Science:

• Agricultural Science
• Remote Sensing
• Forestry

Background:

• Tree pruning is crucial for crop yield, pest control, and soil management but involves labor-intensive field measurements.
• Traditional methods for assessing pruning impacts are often inconsistent due to tree geometry and require extensive fieldwork.

Purpose of the Study:

• To develop and validate an innovative procedure for multi-temporal 3D monitoring of pruned trees using UAV technology and OBIA.
• To quantify the impacts of three distinct pruning strategies (traditional, adapted, mechanical) on olive tree dimensions over time.

Main Methods:

• Utilized unmanned aerial vehicle (UAV) technology to capture multi-temporal 3D images of olive trees before, after, and one year post-pruning.
• Applied advanced object-based image analysis (OBIA) to automatically identify trees and compute 3D dimensions (projected canopy area, tree height, crown volume).

Main Results:

• The UAV-OBIA procedure accurately computed 3D tree dimensions for hundreds of trees across three time points.
• Adapted pruning was the most aggressive (38.95% area, 42.05% volume reduction), followed by traditional pruning (33.02% area, 35.72% volume reduction).
• Mechanical pruning caused the greatest height decrease (12.15%), while annual growth varied based on pruning severity and type.

Conclusions:

• Combining UAV imagery and OBIA offers an efficient, accurate method for assessing pruning impacts on numerous trees with minimal field effort.
• Pruning type and severity significantly influence foliage loss and subsequent canopy growth, providing insights for tailored management.
• This technology generates valuable geospatial data for precision agriculture, enabling site-specific crop management and yielding economic and environmental advantages.