An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height

Abstract

ABSTRACT 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.