Abstract

This study explores methods for estimating the approximate age of Olea europaea (Mehras variety) in Kathraba, Ay District, Al-Karak Governorate, Jordan. Traditional methods for age estimation are often imprecise and time-consuming. Although the Pannelli algorithm is well-established, this research integrates it with mathematical modeling, machine learning, and remote sensing techniques to enhance accuracy and practicality. Data on tree dimensions, environmental conditions, and growth traits were collected and analyzed. Results indicate a strong correlation between physical characteristics and estimated age.

Single-trunk trees had a maximum mean estimated age of approximately 1,025 years, with a trunk circumference of 13 meters. Multi-trunk trees showed a slightly higher mean estimated age of around 1,030.5 years, possibly due to the complexity of growth patterns rather than actual age differences. The highest estimated age reached 1,242 years based on basal stump perimeter (maxTD), while the lowest perimeter measurements were recorded below the trunk bio-fraction at height HPM.

A significant relationship was observed between base diameter, basal stump measurements, and cumulative branch dimensions with the perimeter (PM) of each trunk. To reduce variability, the arithmetic mean of four age estimates was used as the final age value.

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