Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

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从高分辨率遥感影像确定森林成分的方法比较

几十年来，遥感图像一直被用于支持森林生态和管理。在现代，高空间分辨率图像分析技术和自动化工作流程的传播进一步加强了这种协同作用，导致对更复杂的局部尺度生态系统特征的研究。为了适当地为林业生态和管理决策提供信息，应采用最可靠和最有效的方法。出于这个原因，我们的研究将视觉解释与数字（自动）处理进行了比较，用于森林地块组成和个体树木识别。在这项调查中，我们定性和定量地评估了新英格兰复杂的混合物种森林中物种组的分类过程。该分析包括对三个高分辨率遥感影像来源的比较：谷歌地球、国家农业影像计划 (NAIP) 影像和无人机系统 (UAS) 影像。我们发现，尽管 UAS 图像空间分辨率（3.02 cm 平均像素大小）提供的细节水平提高了视觉解释结果（7.87-9.59%），但广义构图组的最高主题精度仍然只有 54.44%。我们对视觉解释不同成分类别的不确定性的定性分析揭示了错误标记的硬木成分（包括早期的演替类别）的持续存在以及无法始终如一地区分“纯”和“混合”林分。使用机器学习算法（包括分类和回归树、随机森林和支持向量机）对相同森林成分进行数字分类的结果在检测单个树木 (93.9%) 和标记它们 (59.62–70.48%) 方面产生了更高的准确度. 这些结果表明，数字化、自动化的分类使广义森林成分类别的总体准确度比视觉解释提高了 16.04%。其他包含多时相、多光谱或数据融合方法的研究为进一步扩大这一差距提供了证据。进一步细化个体树木检测、描绘、