Increased focus on restoring forest structural variation and spatial pattern in dry conifer forests has led to greater emphasis on forest monitoring strategies that can be summarized across scales. To inform restoration objectives with data sources that can characterize individual trees, groups of trees, and the entire stand, different remote sensing strategies such as aerial and terrestrial light detection and ranging (LiDAR) have been explored. Unfortunately, high equipment and operational costs of aerial systems, along with limited spatial extent of terrestrial scanners, have restricted widespread adoption of these technologies for repeated forest monitoring. This study investigates applications of unmanned aerial system (UAS) imagery for Structure from Motion derived modeling of individual tree and stand-level metrics. Specifically, we evaluate how flight parameters impact UAS extracted height and imputed DBH accuracies against field stem-mapped values. In total, 30 UAS image datasets collected from combinations of three altitudes, two flight patterns, and five camera orientations were assessed. Tree heights were extracted using a variable window function that searched UAS-derived canopy height models, while DBH was sampled from point cloud slices at 1.32–1.42 m using a least squares circle fitting algorithm. The sample trees were then filtered against National Forest Inventory data from the study region to ensure reasonable matching of extracted heights and diameters. The matched values were used to create a height to diameter relationship for predicting missing DBH values. Extracted and imputed tree values were compared against stem-mapped values to determine tree commission and omission rates, the accuracy and precision of extracted tree height, DBH, as well as overstory and understory stand density. Finding that, 1) tree extraction accuracy and correctness was maximized (F-score = 0.77) for nadir crosshatch UAS flight designs; 2) extracted tree height R² with stem-mapped values was high (R² ≥ 0.98) for all UAS flight parameters, but the quality (mean error = 0.79 cm) and quantity (~10% of all trees) of extracted DBH values was maximized for lower altitude, nadir crosshatch acquisitions; 3) the distribution of predicted DBH values most closely matched field observed values for off-nadir crosshatch flight designs; 4) using either off-nadir or crosshatch flight designs at lower altitudes maximized correlation (r > 0.70) and accuracy (basal area within 2 m² ha⁻¹) of stand density estimates. This study demonstrates a novel UAS-based inventory strategy for estimating individual tree structural attributes (i.e., location, height, and DBH) in dry conifer forests, without the need for in situ field observations.

越来越关注恢复干燥针叶林的森林结构变化和空间格局，导致更加重视可以跨尺度总结的森林监测策略。为了使用能够表征单个树木、树木组和整个林分的数据源告知恢复目标，已经探索了不同的遥感策略，例如空中和地面光检测和测距 (LiDAR)。不幸的是，空中系统的高设备和运营成本，以及地面扫描仪的空间范围有限，限制了这些技术在重复森林监测中的广泛采用。本研究调查了无人机系统 (UAS) 图像在结构中的应用，该结构来自单个树木和林分指标的运动派生建模。具体来说，我们评估了飞行参数如何影响 UAS 提取的高度并根据字段茎映射值估算 DBH 精度。总共评估了从三个高度、两个飞行模式和五个相机方向的组合收集的 30 个 UAS 图像数据集。使用可变窗函数提取树高，该函数搜索 UAS 派生的冠层高度模型，而 DBH 是使用最小二乘圆拟合算法从 1.32-1.42 m 的点云切片中采样的。然后根据来自研究区域的国家森林清单数据过滤样本树木，以确保提取的高度和直径的合理匹配。匹配的值用于创建高度与直径的关系，以预测缺失的 DBH 值。将提取和估算的树木值与茎映射值进行比较，以确定树木的佣金和遗漏率、提取的树木高度、DBH 的准确性和精确度，以及林上林分和林下林分密度。发现，1）树提取的准确性和正确性最大化（F-score = 0.77）用于最低点剖面线 UAS 飞行设计；2）提取的树高R²与茎-映射值是高（R ²  ≥0.98）对于所有UAS飞行参数，但质量（平均误差R = 0.79厘米）和数量（〜所有树木的10％）萃取DBH值被最大化以较低的高度，最低点交叉线收购；3) 预测 DBH 值的分布与非天底剖面线飞行设计的现场观测值最匹配；4) 在较低高度使用偏离天底或交叉影线的飞行设计最大化相关性 ( r  > 0.70) 和准确度（2 m ²  ha ^-1内的基础面积）) 的林分密度估计。本研究展示了一种新的基于 UAS 的清单策略，用于估计干燥针叶林中的单个树木结构属性（即位置、高度和 DBH），而无需现场实地观察。