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FIRST TEST OF LOW-COST SINGLE-BOARD COMPUTER BASED CAMERA FOR CLOSE-RANGE FOREST PHOTOGRAMMETRY
Abstract
Close-range photogrammetry (CRP) represents very perspective technology for many fields. It can provide results comparable to terrestrial laser scanning, and the hardware is still much cheaper. Our research group is developing low-cost CRP device based on Raspberry Pi 3 computer. Raspberry Pi 3 is a single-board computer with relatively high computing power and low price. In connection with a touchscreen, Pi Camera module v2.1 (Sony IMX219, 8Mpx) and powerbank it becomes a portable device for imagery. Continuous shooting based image collection (1 image per second) was carried out using regular RGB camera and NoIR (no infrared filter) camera at a test plot. Images of the same area were collected by DSLR camera Canon EOS 70D (20.2 megapixels) as a reference to our low-cost solution. The images were processed into a form of dense point cloud by Pix4D software package, and the diameters were estimated by DendroCloud software (gis.tuzvo.sk/dendrocloud/) using an optimal circle fitting method. On-line visualisation of the point clouds is available at maps.tuzvo.sk/sgem2017/. Diameters at three different heights above ground (0.65m, 1.3m and 2m) were estimated for a sample of four trees. Reference diameters were measured by a calliper. The root mean square error (RMSE) and relative RMSE of diameter estimation for regular RGB were 2.13cm and 6.9%; 2.54cm and 8.3% for NoIR camera. The RMSE and relative RMSE for diameters derived from point cloud created using Canon images were 1.18cm and 3.8% respectively. Results of our first test showed that Raspberry Pi camera solution has a great potential for the use in forest inventory. Future research will focus on testing developed device on larger plots and the development of a multi-camera system based on single-board computers. This work was supported by the Slovak Research and Development Agency under the contract No. DS-2016-0040 (Benchmarking of close-range photogrammetry methods for forestry applications, gis.tuzvo.sk/benchcrp/).
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References12
X. Liang, A. Jaakkola, Y. Wang, J. Hyyppä, E. Honkavaara, J. Liu, and H. Kaartinen, “The use of a hand-held camera for individual tree 3D mapping in forest sample plots,” Remote Sens., vol. 6, no. 7, pp. 6587–6603, 2014.
M. Forsman, N. Börlin, and J. Holmgren, “Estimation of Tree Stem Attributes Using Terrestrial Photogrammetry,” Forests, vol. 7, no. 61, pp. 261–265, 2016.
T. Mikita, P. Janata, and P. Surový, “Forest stand inventory based on combined aerial and terrestrial close-range photogrammetry,” Forests, vol. 7, no. 8, pp. 1–14, 2016.
P. Surový, A. Yoshimoto, and D. Panagiotidis, “Accuracy of reconstruction of the tree stem surface using terrestrial close-range photogrammetry,” Remote Sens., vol. 8, no. 2, pp. 1–13, 2016.
X. Liang, V. Kankare, J. Hyyppä, Y. Wang, A. Kukko, H. Haggrén, X. Yu, H. Kaartinen, A. Jaakkola, F. Guan, M. Holopainen, and M. Vastaranta, “Terrestrial laser scanning in forest inventories,” ISPRS J. Photogramm. Remote Sens., vol. 115, pp. 63–77, 2016.
M. Koreň, M. Mokroš, and T. Bucha, “Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods,” Int J Appl Earth Obs Geoinf., vol. 63, pp. 122–128, 2017.
L. M. Moskal and G. Zheng, “Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest,” Remote Sens., vol. 4, no. 12, pp. 1–20, Dec. 2011.
H. Huang, Z. Li, P. Gong, X. Cheng, N. Clinton, C. Cao, W. Ni, and L. Wang, “Automated methods for measuring DBH and tree heights with a commercial scanning lidar,” Photogramm. Eng. Remote Sensing, vol. 77, no. 3, pp. 219–227, 2011.
J. G. Henning and P. J. Radtke, “Ground-based Laser Imaging for Assessing Three-dimensional Forest Canopy Structure,” Photogramm. Eng. Remote Sens., vol. 72, no. 12, pp. 1349–1358, 2006.
S. Fleck, N. Obertreiber, I. Schmidt, M. Brauns, H. F. Jungkunst, and C. Leuschner, Terrestrial lidar measurements for analysing canopy structure in an old-growth forest, vol. XXXVI. 2007.
E. Upton and G. Halfacree, Raspberry Pi user guide. John Wiley & Sons, 2014.
M. Koreň, DendroCloud: point cloud processing software for forestry, version 1.45. Zvolen: http://gis.tuzvo.sk/dendrocloud/download/dendrocloud_1_45.pdf, 2017.
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