Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information

Juha Hyyppä, Juho-Pekka Virtanen, Anttoni Jaakkola, Xiaowei Yu, Hannu Hyyppä, Xinlian Liang

Research output: Contribution to journalArticleScientificpeer-review

54 Citations (Scopus)
199 Downloads (Pure)


In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context.
Original languageEnglish
Article number6
Number of pages14
Issue number1
Early online date21 Dec 2017
Publication statusPublished - Jan 2018
MoE publication typeA1 Journal article-refereed


  • laser radar
  • Remote sensing
  • Forestry
  • Kinect
  • DBH
  • Point cloud
  • Mobile laser scanning


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