Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information

Research output: Contribution to journalArticleScientificpeer-review

Standard

Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information. / Hyyppä, Juha; Virtanen, Juho-Pekka; Jaakkola, Anttoni; Yu, Xiaowei; Hyyppä, Hannu; Liang, Xinlian.

In: Forests, Vol. 9, No. 1, 6, 01.2018.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

APA

Vancouver

Author

Bibtex - Download

@article{9dd0165f1be047c999701d49946155f5,
title = "Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information",
abstract = "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.",
keywords = "laser radar, Remote sensing, Forestry, Kinect, DBH, Point cloud, Mobile laser scanning",
author = "Juha Hyypp{\"a} and Juho-Pekka Virtanen and Anttoni Jaakkola and Xiaowei Yu and Hannu Hyypp{\"a} and Xinlian Liang",
year = "2018",
month = "1",
doi = "10.3390/f9010006",
language = "English",
volume = "9",
journal = "Forests",
issn = "1999-4907",
publisher = "MDPI AG",
number = "1",

}

RIS - Download

TY - JOUR

T1 - Feasibility of Google Tango and Kinect for Crowdsourcing Forestry Information

AU - Hyyppä, Juha

AU - Virtanen, Juho-Pekka

AU - Jaakkola, Anttoni

AU - Yu, Xiaowei

AU - Hyyppä, Hannu

AU - Liang, Xinlian

PY - 2018/1

Y1 - 2018/1

N2 - 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.

AB - 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.

KW - laser radar

KW - Remote sensing

KW - Forestry

KW - Kinect

KW - DBH

KW - Point cloud

KW - Mobile laser scanning

U2 - 10.3390/f9010006

DO - 10.3390/f9010006

M3 - Article

VL - 9

JO - Forests

JF - Forests

SN - 1999-4907

IS - 1

M1 - 6

ER -

ID: 16791679