Evaluating Structure-from-Motion Photogrammetry for Dendrometric Measurements of Trees in Agro-Sylvopastoral Landscapes

Abstract

Recent applications of Structure-from-Motion (SfM) photogrammetry in forestry have highlighted its robustness in tree mensuration. This study proposes an optimized image acquisition protocol for generating high-quality 3D point clouds of individual trees of Faidherbia albida (Delile) A.Chev and Acacia tortilis haynes ssp. raddiana (Savi) Brenan with varying sizes and forms in a Sahelian agrosilvopastoral system in Senegal. A measurement protocol adapted for estimating key dendrometric parameters was established, including tree height, crown diameter, and diameter at breast height (DBH, 1.30 m). A Mavic Pro drone and a TG-5 ground camera were used to photograph 20 individuals of each species. Acquired georeferenced images were processed using Metashape 1.5.1 trial version to produce dense 3D point clouds. Depending on tree isolation and height, models were reconstructed either from drone images alone or from a combination of aerial and ground images. The dendrometric parameters were estimated with high precision. DBH was measured with an nRMSE of 6% and Bias values of −1.3 and −0.04 for F. albida and A. tortilis, respectively. Total tree height was estimated with nRMSEs of 9% and 12%, and Bias values of −0.04 and 0.41. Crown diameter showed nRMSEs of 9% and 0.10%, with Bias values of 0.57 and 0.85, respectively. This study demonstrated the effectiveness of combining drone and ground-based pictures for accurate 3D reconstruction and measurement of dendrometric parameters on individual trees in Sahelian landscapes. The proposed protocol will be useful for assessing carbon sequestration and for developing climate change mitigation strategies in Sahelian ecosystems.

Code and data availability

The article describes SfM photogrammetry of 40 trees (drone and ground images, Metashape processing, dendrometric validation), but contains no data availability statement, no public repository deposit of images, point clouds, field measurements, or analysis code, and no author-provided URL for any paper-specific asset.

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