The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts.

中文翻译：

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使用无人机系统 (UAS) 估算原始森林属性和稀有群落特征：丰富常规森林资源

进行森林清查的技术已经建立了几个世纪的土地管理和保护。然而，近几十年来，遥感、计算机视觉和数据科学领域引人注目的新工具和方法为提高这些抽样设计的有效性和理解能力提供了创新途径。现在，在无人机系统 (UAS) 和先进的图像处理技术的帮助下，我们从未如此接近以实地清单规模绘制森林地图。我们的研究在新罕布什尔州对复杂的混合物种森林进行，使用自然彩色 UAS 图像来估计单个树木的直径（胸高直径 (dbh)）以及每公顷基础面积 (BA/ha) 的林分水平估计，二次平均直径 (QMD)，每公顷树木数 (TPH)，和使用数字摄影测量的立密度指数 (SDI)。为了加强我们对这些森林的了解，我们还评估了 UAS 绘制大型树木（即直径 > 40 厘米）存在的地图的能力。我们通过两种方式评估了 UAS 数字摄影测量识别大树的熟练程度：(1) 使用 UAS 估计的 dbh 和 40 cm 大小阈值和 (2) 使用随机森林监督分类和光谱、纹理和几何的组合特征。我们基于 UAS 的树木直径估计报告平均误差为 19.7% 至 33.7%。在林分层面，BA/ha 和 QMD 分别被高估了 42.18% 和 62.09%，而 TPH 和 SDI 被低估了 45.58% 和 3.34%。然而，当仅考虑大于 9 公顷的林分时，林分级别对 BA/ha 的高估下降至 14.629%。大树的整体分类，使用随机森林监督分类实现了85%的整体准确率。这些方法的效率和有效性为当地土地管理者提供了更好地了解其森林生态系统的机会。未来对单个树冠检测和描绘的研究，特别是对于共同优势树或被抑制的树，将进一步支持这些努力。