Medición de atributos forestales de especies de coníferas mediante fotogrametría digital con drones

Abstract

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 (R2adj), 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 R2adj ranging from 0.87 to 0.98, and RMSE of 1.64 and 0.61 m; M2 being the best. Regarding the estimation of AB and Vol, the RF-M6 model was the best, achieving an R2 of 0.77 and 0.77, and RMSE of 0.046 and 0.269, respectively. It is concluded that the photogrammetric 3D point cloud is an alternative for estimating forest variables at the tree level.

Plant phenotyping relevance

ドローンのデジタル写真測量による3D点群から、個体レベルの樹高・胸高断面積・材積を推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。

Code and data availability

The article describes field measurements of 80 conifer trees, drone photogrammetric point clouds, and model fitting in R, but contains no data availability statement, no public repository deposit, and no author code/workflow URL. Only generic tools (R, lidR, OpenDroneMap, Pix4Dcapture) are cited; no paper-specific phen

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