Effects of Flight and Smoothing Parameters on the Detection of Taxus and Olive Trees with UAV-Borne Imagery

Abstract

Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires detailed pre-flight planning in order to generate the desired 3D-products needed for ITD. In this study, we aimed to find the most optimal flight parameters (flight altitude and image overlap) and processing options (smoothing window size) for the detection of taxus trees in Belgium. Next, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece. We found that the processing parameters had a larger effect on the accuracy and precision of ITD than the flight parameters. In particular, a smoothing window of 3 × 3 pixels performed best (F-scores of 0.99) compared to no smoothing (F-scores between 0.88 and 0.90) or a window size of 5 (F-scores between 0.90 and 0.94). Furthermore, the results show that model transferability can still be a bottleneck as it does not capture management induced characteristics such as the typical crown shape of olive trees (F-scores between 0.55 and 0.61).

Plant phenotyping relevance

UAV画像の飛行・処理条件とマーカー制御セグメンテーションを開発・検証し、樹冠の個体検出精度と他樹種への転用性を評価しており、植物画像計測手法が研究の中心である。

Code and data availability

The paper reports UAV-based individual tree detection for taxus and olive trees but provides no public dataset, imagery, code, or model deposit. The Data Availability Statement reads 'Not applicable.' The only URLs mentioned (rLiDAR and ForestTools CRAN packages) are generic third-party R libraries used as tools, not作者

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