Abstract. Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50 % ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures.

中文翻译：

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使用来自无人机消费级相机的叶子上和叶子上的图像在林地中检测和描绘单个树冠

摘要。使用遥感数据提取有关单个树木的信息对于支持森林环境中的生态和商业应用至关重要。无人机 (UAV) 上的消费级相机获取的数据提供了一种经济实惠的高空间分辨率图像选项，可用于提取树木级别的森林结构信息。这项工作的目的是研究无人机时间序列数据的潜力和准确性，以自动检测和描绘整个林地的树冠。工作流程（以分步方式呈现）涉及基于源自无人机摄影测量点云的数字高程模型构建冠层高度模型 (CHM)。修改了基于流域的方法以自动检测和描绘树冠，基于 CHM 和来自无人机正射镶嵌的亮度信息。通过将其结果与手动描绘的树冠进行比较来评估所提出方法的准确性。结果显示总体准确度为 63%，其中针叶树种的描述更准确（高达 80%），而阔叶树种的准确度较低（<50%）。需要继续研究以提高自动化单个树冠检测和描绘的可信度，尤其是在复杂的森林结构上。而阔叶树种返回的准确度较低（<50 %）。需要继续研究以提高自动化单个树冠检测和描绘的可信度，尤其是在复杂的森林结构上。而阔叶树种返回的准确度较低（<50 %）。需要继续研究以提高自动化单个树冠检测和描绘的可信度，尤其是在复杂的森林结构上。