Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect – a lightweight depth sensor – for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual urban trees (n = 51) were recorded from one viewing angle at a distance of 1 m to 5 m away using the various Field of View (FOV) settings on the depth sensor, from which resultant point clouds provided DBH estimates using a circle-fitting approach. The optimal capture method was observed at a distance of 2 m using the binned Near Field of View (NFOV) setting. Root Mean Square Error (RMSE) of DBH using this method was 8.43 cm; however, after removing trees with irregular or non-circular stems, this improved to 3.53 cm. Variations in ambient light were observed to have little effect on DBH estimates. The results of this study suggest when in an outdoor environment, the Azure Kinect should be used at a distance no greater than 3 m away, using the binned NFOV sensor setting, for DBH estimates.

与常规方法相比，由于可用的准确和快速的估算，森林和城市树木清单测量越来越多地采用遥感（RS）技术。这项研究的重点是评估Microsoft Azure Kinect（一种轻量级的深度传感器）的准确性和在室外测量乳房高度（DBH）时的茎干直径的潜在应用。个别城市树木（n使用深度传感器上的各种视场（FOV）设置从1个视角在1 m至5 m的距离处记录一个= 51），由此得出的点云通过圆拟合方法提供了DBH估计。使用合并的近视场（NFOV）设置，在2 m的距离处观察到了最佳捕获方法。使用此方法的DBH均方根误差（RMSE）为8.43厘米；但是，在去除具有不规则或非圆形茎的树木后，该长度提高到3.53厘米。观察到环境光的变化对DBH估计值影响很小。这项研究的结果表明，在室外环境中，应使用合并的NFOV传感器设置，在不超过3 m的距离内使用Azure Kinect进行DBH估算。