Department of Forest and Rangeland Stewardship, Colorado State University, 1472 Campus Delivery, Fort Collins, CO, 80523, USA.
United States Department of Agriculture Forest Service, Rocky Mountain Research Station, 240 W Prospect Rd, Fort Collins, CO, 80526, USA.
Environ Monit Assess. 2024 May 9;196(6):530. doi: 10.1007/s10661-024-12703-1.
Increasingly, dry conifer forest restoration has focused on reestablishing horizontal and vertical complexity and ecological functions associated with frequent, low-intensity fires that characterize these systems. However, most forest inventory approaches lack the resolution, extent, or spatial explicitness for describing tree-level spatial aggregation and openings that were characteristic of historical forests. Uncrewed aerial system (UAS) structure from motion (SfM) remote sensing has potential for creating spatially explicit forest inventory data. This study evaluates the accuracy of SfM-estimated tree, clump, and stand structural attributes across 11 ponderosa pine-dominated stands treated with four different silvicultural prescriptions. Specifically, UAS-estimated tree height and diameter-at-breast-height (DBH) and stand-level canopy cover, density, and metrics of individual trees, tree clumps, and canopy openings were compared to forest survey data. Overall, tree detection success was high in all stands (F-scores of 0.64 to 0.89), with average F-scores > 0.81 for all size classes except understory trees (< 5.0 m tall). We observed average height and DBH errors of 0.34 m and - 0.04 cm, respectively. The UAS stand density was overestimated by 53 trees ha (27.9%) on average, with most errors associated with understory trees. Focusing on trees > 5.0 m tall, reduced error to an underestimation of 10 trees ha (5.7%). Mean absolute errors of bole basal area, bole quadratic mean diameter, and canopy cover were 11.4%, 16.6%, and 13.8%, respectively. While no differences were found between stem-mapped and UAS-derived metrics of individual trees, clumps of trees, canopy openings, and inter-clump tree characteristics, the UAS method overestimated crown area in two of the five comparisons. Results indicate that in ponderosa pine forests, UAS can reliably describe large- and small-grained forest structures to effectively inform spatially explicit management objectives.
日益增多的干旱针叶林恢复工作侧重于重建与这些系统频繁发生的低强度火灾相关的水平和垂直复杂性以及生态功能。然而，大多数森林清查方法缺乏分辨率、范围或空间明确性，无法描述历史森林中树木水平的空间聚集和空隙特征。无人航空系统（UAS）运动结构（SfM）遥感具有创建空间明确的森林清查数据的潜力。本研究评估了 SfM 估计的树木、丛和林分结构属性在 11 个不同的油松主导林分中的准确性，这些林分采用了四种不同的造林处方进行处理。具体而言，UAS 估计的树木高度、胸径（DBH）和林分水平的冠层覆盖率、密度以及树木、树丛和冠层空隙的个体树木指标，与森林调查数据进行了比较。总体而言，所有林分中的树木检测成功率都很高（F 分数为 0.64 至 0.89），除了林下树木（<5.0 米高）外，所有大小类别的平均 F 分数均大于 0.81。我们观察到平均高度和 DBH 误差分别为 0.34 米和-0.04 厘米。UAS 林分密度平均高估了 53 株/公顷（27.9%），大多数误差与林下树木有关。专注于高度>5.0 米的树木，将误差减少到低估了 10 株/公顷（5.7%）。树干基面积、树干二次平均直径和冠层覆盖率的平均绝对误差分别为 11.4%、16.6%和 13.8%。虽然在个体树木、树木丛、冠层空隙和丛间树木特征的茎图测量和 UAS 衍生指标之间没有发现差异，但在五个比较中的两个中，UAS 方法高估了树冠面积。结果表明，在油松林中，UAS 可以可靠地描述大、小粒度的森林结构，有效地为空间明确的管理目标提供信息。
