In modeling forest stand growth and yield, crown width, a measure for stand density, is among the parameters that allows for estimating stand timber volumes. However, accurately measuring tree crown size in the field, in particular for mature trees, is challenging. This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle (UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas, USA. An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation (a young stand before canopy closure, a mature stand with a closed canopy). For each site, the images were split into two subsets: one for training and one for validation purposes. Three widely used object detection methods in deep learning, the Faster region-based convolutional neural network (Faster R-CNN), You Only Look Once version 3 (YOLOv3), and single shot detection (SSD), were applied to the training data, respectively. Each was used to train the model for performing crown recognition and crown extraction. Each model output was evaluated using an independent test data set. All three models were successful in detecting tree crowns with an accuracy greater than 93%, except the Faster R-CNN model that failed on the mature site. On the young site, the SSD model performed the best for crown extraction with a coefficient of determination (R²) of 0.92, followed by Faster R-CNN (0.88) and YOLOv3 (0.62). As to the mature site, the SSD model achieved a R² as high as 0.94, follow by YOLOv3 (0.69). These deep leaning algorithms, in particular the SSD model, proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy. For the purpose of forest inventory on loblolly pine plantations, using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.

在对林分生长和产量进行建模时，树冠宽度（衡量林分密度）是可用于估算林分木材体积的参数之一。然而，特别是对于成熟树木，在田间准确地测量树冠尺寸是有挑战性的。这项研究展示了一种将机器学习算法应用于无人机所获得的航空影像的新颖方法，以识别美国得克萨斯州东部的两个火炬松人工林中的树冠及其宽度。为每个种植园获取了从无人机捕获的航拍照片获得的正交镶嵌图像（冠层关闭前的幼林，冠层闭合的成熟林）。对于每个站点，将图像分为两个子集：一个用于训练，一个用于验证。深度学习中三种广泛使用的对象检测方法，基于快速区域的卷积神经网络（Faster R-CNN），仅查看一次版本3（YOLOv3）和单发检测（SSD）分别应用于训练数据。每个模型都用于训练模型以执行冠冕识别和冠冕提取。使用独立的测试数据集评估每个模型的输出。除Faster R-CNN模型在成熟站点上失败外，所有这三种模型都成功地检测出树冠，其准确度大于93％。在年轻的地方，SSD模型的确定系数最高，最适合冠冠的提取（每个模型都用于训练模型以执行冠冕识别和冠冕提取。使用独立的测试数据集评估每个模型的输出。除Faster R-CNN模型在成熟站点上失败外，所有这三种模型都成功地检测出树冠，其准确度大于93％。在年轻的地方，SSD模型的确定系数最高，最适合冠冠的提取（每个模型都用于训练模型以执行冠冕识别和冠冕提取。使用独立的测试数据集评估每个模型的输出。除Faster R-CNN模型在成熟站点上失败外，所有这三种模型都成功地检测出树冠，其准确度大于93％。在年轻的地方，SSD模型的确定系数最高，最适合冠冠的提取（R ²）为0.92，然后是Faster R-CNN（0.88）和YOLOv3（0.62）。至于成熟站点，SSD模型的R ²高达0.94，其次是YOLOv3（0.69）。这些深度学习算法，特别是SSD模型，被证明可以成功地识别树冠并以令人满意的精度估算树冠宽度。出于对火炬松人工林进行森林清查的目的，将无人机捕获的图像与SSD对象保留应用程序配合使用是一种经济有效的替代传统地面测量的方法。