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<p>Yield forecasting is a critical challenge in modern olive cultivation, where canopy volume is a primary determinant of production in irrigated orchards. This study evaluates different methodologies for estimating olive tree crown volume to predict yield. Terrestrial Laser Scanning (TLS), considered the most accurate method, was used as a reference to benchmark traditional geometric models (ellipsoid and cylinder) and a remote sensing approach based on Google Earth imagery. The study was conducted on an irrigated ‘Arbequina’ orchard in Almeria, Spain. For TLS, three calculation algorithms were analyzed (Cross-sections, Cyclone, and CloudCompare), with the cross-sectional method (V<sub>SEC</sub>) selected as the control due to its statistical robustness. Results indicated that the traditional ellipsoidal model significantly underestimated volume (-28%), while the cylindrical model slightly overestimated it (+ 8%). Conversely, the proposed Google Earth method, multiplying the satellite-derived projected area by canopy height, demonstrated high accuracy, with a negligible bias (+ 0.57 m<sup>3</sup>) compared to the control method, TLS Cross-sections; this means 2.3%. Furthermore, a production coefficient of 1.5&#xa0;kg of olives per m<sup>3</sup> of canopy volume was established for this orchard. We conclude that while TLS provides superior detail, the Google Earth based method offers a rapid, cost-effective, and sufficiently accurate alternative for yield forecasting in large scale olive groves.</p>

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Assessment of olive tree crown volumes using google earth imagery: a comparison with terrestrial laser scanning (TLS)

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Antonio J. Zapata-Sierra,
  • M. A. Montero-Rodríguez,
  • Carmen Marín-Buzón,
  • Antonio M. Pérez-Romero,
  • Francisco Manzano-Agugliaro

摘要

Yield forecasting is a critical challenge in modern olive cultivation, where canopy volume is a primary determinant of production in irrigated orchards. This study evaluates different methodologies for estimating olive tree crown volume to predict yield. Terrestrial Laser Scanning (TLS), considered the most accurate method, was used as a reference to benchmark traditional geometric models (ellipsoid and cylinder) and a remote sensing approach based on Google Earth imagery. The study was conducted on an irrigated ‘Arbequina’ orchard in Almeria, Spain. For TLS, three calculation algorithms were analyzed (Cross-sections, Cyclone, and CloudCompare), with the cross-sectional method (VSEC) selected as the control due to its statistical robustness. Results indicated that the traditional ellipsoidal model significantly underestimated volume (-28%), while the cylindrical model slightly overestimated it (+ 8%). Conversely, the proposed Google Earth method, multiplying the satellite-derived projected area by canopy height, demonstrated high accuracy, with a negligible bias (+ 0.57 m3) compared to the control method, TLS Cross-sections; this means 2.3%. Furthermore, a production coefficient of 1.5 kg of olives per m3 of canopy volume was established for this orchard. We conclude that while TLS provides superior detail, the Google Earth based method offers a rapid, cost-effective, and sufficiently accurate alternative for yield forecasting in large scale olive groves.