A handheld device for measuring the diameter at breast height of individual trees using laser ranging and deep-learning based image recognition

Abstract

Abstract Background Accurate and efficient measurement of the diameter at breast height (DBH) of individual trees is essential for forest inventories, ecological management, and carbon budget estimation. However, traditional diameter tapes are still the most widely used dendrometers in forest surveys, which makes DBH measurement time-consuming and labor-intensive. Automatic and easy-to-use devices for measuring DBH are highly anticipated in forest surveys. In this study, we present a handheld device for measuring the DBH of individual trees that uses digital cameras and laser ranging, allowing for an instant, automated, and contactless measurement of DBH. Results The base hardware of this device is a digital camera and a laser rangefinder, which are used to take a picture of the targeted tree trunk and record the horizontal distance between the digital camera and the targeted tree, respectively. The core software is composed of lightweight convolutional neural networks (CNNs), which includes an attention-focused mechanism for detecting the tree trunk to log the number of pixels between the edges. We also calibrated the digital camera to correct the distortion introduced by the lens system, and obtained the normalized focal length. Parameters including the horizontal distance between the digital camera and the targeted tree, number of pixels between the edges of the tree trunk, and normalized focal length were used to calculate the DBH based on the principles of geometrical optics. The measured diameter values, and the longitudes and latitudes of the measurement sites, were recorded in a text file, which is convenient to export to external flash disks. The field measurement accuracy test showed that the BIAS of the newly developed device was − 1.78 mm, and no significant differences were found between the measured diameter values and the true values (measured by the conventional tape). Furthermore, compared with most other image-based instruments, our device showed higher measurement accuracy. Conclusions The newly developed handheld device realized efficient, accurate, instant, and non-contact measurements of DBH, and the CNNs were proven to be successful in the detection of the tree trunk in our research. We believe that the newly developed device can fulfill the precision requirement in forest surveys, and that the application of this device can improve the efficiency of DBH measurements in forest surveys.

Keywords

• Forest inventory
• Tree measurement
• Digital camera
• Convolutional neural networks
• Spatial attention module