Resumen

ORDONEZ-PRADO, Casimiro et al. Measurement of forest attributes of coniferous species using digital drone photography. Ecosistemas y recur. agropecuarios [online]. 2025, vol.12, n.spe5, e4586.  Epub 20-Feb-2026. ISSN 2007-901X.  https://doi.org/10.19136/era.a12nv.4586.

The photogrammetric point cloud provides information that allows to estimate dendrometric and dasometric variables at the individual tree level with precision. The objective was to evaluate the potential of the geospatial point cloud generated by photogrammetry of aerial photographs captured by a low-cost drone in the estimation of dendrometric and dasometric variables in conifer species. With data on total height (At: m), basal area (AB: m2) and volume (Vol: m3) of 80 conifer trees measured in the field, linear (M1), exponential (M2), M1 with mixed effects (M3), M2 with mixed effects (M4), artificial neural networks (ANN-M5) and random forest (RF-M6) regression models were fitted to estimate At, AB and Vol based on height metrics (z), of the measured conifers, from the photogrammetric point cloud. The efficiency of the estimates was determined using the highest adjusted coefficient of determination (R2), the lowest root mean square error (RMSE), the Akaike Information Criterion (AIC), and Bias. The At was best estimated using the photogrammetric point cloud metrics, with R2 adj ranging from 0.87 to 0.98, and RMSE of 1.64 and 0.61; M2 being the best. Regarding the estimation of AB and Vol, the RF-M6 model was the best, achieving an R2 of 0.772 and 0.769, and RMSE of 0.046 and 0.564, respectively. It is concluded that the photogrammetric 3D point cloud is an alternative for estimating forest variables at the tree level.

Palabras llave : Photogrammetric cloud; metrics; parametric models; no parametric models; UAV.

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