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http://dx.doi.org/10.13087/kosert.2019.22.6.1

Estimation Carbon Storage of Urban Street trees Using UAV Imagery and SfM Technique  

Kim, Da-Seul (Graduate School of Seoul National University)
Lee, Dong-Kun (Dept. of Landscape Architecture and Rural System Engineering, Seoul National University)
Heo, Han-Kyul (Interdisciplinary Program in Landscape Architecture, Seoul National University)
Publication Information
Journal of the Korean Society of Environmental Restoration Technology / v.22, no.6, 2019 , pp. 1-14 More about this Journal
Abstract
Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.
Keywords
Aerial Photogrammetry; RANSAC; Circle fitting; Tree classification; Carbon Sotrage; DBH;
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  • Reference
1 Bendig, J. . A. Bolten . S. Bennertz . J. Broscheit . S. Eichfuss and G. Bareth. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing 6(11) : 10395-10412.   DOI
2 Dandois, J. P. and E. C. Ellis. 2013. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 136 : 259-276.   DOI
3 Dorendorf, J. . A. Eschenbach . K. Schmidt and K. Jensen. 2015. Both tree and soil carbon need to be quantified for carbon assessments of cities. Urban Forestry and Urban Greening 14(3) : 447-455.   DOI
4 Fischler, M. a and R. C. Bolles. 1981. Random Sample Paradigm for Model Consensus: A Apphcatlons to Image Fitting with Analysis and Automated Cartography. Communications of the ACM 24(6) : 381-395.   DOI
5 Gibbs, H. K. . S. Brown . J. O. Niles and J. A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters 2(4).
6 Houghton, R. A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology 11(6) : 945-958.   DOI
7 Jingyun, F. . C. Anping . P. Changhui . Z. Shuqing and C. Longjun. 2001. Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292(June) : 2320-2322.   DOI
8 Allen, C. D. . A. K. Macalady . H. Chenchouni . D. Bachelet . N. McDowell . M. Vennetier . T. Kitzberger . A. Rigling . D. D. Breshears . E. H. (Ted) Hogg . P. Gonzalez . R. Fensham . Z. Zhang . J. Castro . N. Demidova . J. H. Lim . G. Allard . S. W. Running . A. Semerci and N. Cobb. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259(4) : 660-684.   DOI
9 Jo, H.-K. and T.-W. Ahn. 2012. Carbon Storage and Uptake by Deciduous Tree Species for Urban Landscape. Journal of the Korean Institute of Landscape Architecture 40(5) : 160-168. (In Korean with English summary)   DOI
10 Kim, K. T. . J. W. Cho and H. H. Yoo. 2011. Carbon Storage Estimation of Urban Area Using KOMPSAT-2 Imagery. Journal of the Korean society for geospatial information science (Bk 21) : 49-54. (In Korean with English summary)   DOI
11 Kolzenburg, S. . M. Favalli . A. Fornaciai . I. Isola . A. J. L. Harris . L. Nannipieri and D. Giordano. 2016. Rapid Updating and Improvement of Airborne LIDAR DEMs Through Ground-Based SfM 3-D Modeling of Volcanic Features. IEEE Transactions on Geoscience and Remote Sensing 54(11) : 6687-6699.   DOI
12 Li, W. . Z. Niu . H. Chen . D. Li . M. Wu and W. Zhao. 2016. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators 67 : 637-648.   DOI
13 Lefsky, M. A. . W. B. COHEN . G. G. PARKER and D. J. HARDING. 2002. Lidar Remote Sensing for Ecosystem Studies. BioScience 52(1) : 19.   DOI
14 Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints.pdf. International Journal of Computer Vision: 1-28.
15 Luyssaert, S. . E. D. Schulze . A. Borner . A. Knohl . D. Hessenmoller . B. E. Law . P. Ciais and J. Grace. 2008. Old-growth forests as global carbon sinks. Nature 455(7210) : 213-215.   DOI
16 Lv, H. . W. Wang . X. He . C. Wei . L. Xiao . B. Zhang and W. Zhou. 2018. Association of urban forest landscape characteristics with biomass and soil carbon stocks in Harbin City, Northeastern China. PeerJ 6 : e5825.   DOI
17 Pregitzer, K. S. and E. S. Euskirchen. 2004. Carbon cycling and storage in world forests: Biome patterns related to forest age. Global Change Biology 10(12) : 2052-2077.   DOI
18 McHale, M. R. . I. C. Burke . M. A. Lefsky . P. J. Peper and E. G. McPherson. 2009. Urban forest biomass estimates: Is it important to use allometric relationships developed specifically for urban trees? Urban Ecosystems 12(1) : 95-113.   DOI
19 Omasa, K. . G. Y. Qiu . K. Watanuki . K. Yoshimi and Y. Akiyama. 2003. Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees. Environmental Science and Technology 37(6) : 1198-1201.   DOI
20 Pouyat, R. V. . I. D. Yesilonis and D. J. Nowak. 2006. Carbon Storage by Urban Soils in the United States. Journal of Environment Quality 35(4) : 1566.   DOI
21 Remondino, F. . L. Barazzetti . F. Nex . M. Scaioni and D. Sarazzi. 2012. Uav Photogrammetry for Mapping and 3D Modeling - Current Status and Future Perspectives. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-1/(September) : 25-31.
22 Revelli, R. and A. Porporato. 2018. Ecohydrological model for the quantification of ecosystem services provided by urban street trees. Urban Ecosystems 21(3) : 489-504.   DOI
23 Solomon, A. M. . J. Wisniewski . S. Brown . M. C. Trexier . R. A. Houghton and R. K. Dixon. 1994. Carbon Pools and Flux of Global Forest Ecosystems. Science 263(5144) : 185-190.   DOI
24 Stoffberg, G. H. . M. W. van Rooyen . M. J. van der Linde and H. T. Groeneveld. 2010. Carbon sequestration estimates of indigenous street trees in the City of Tshwane, South Africa. Urban Forestry and Urban Greening 9(1) : 9-14.   DOI
25 Whittaker, R. H. and P. L. Marks. 1975. Methods of Assessing Terrestrial Productivty. Primary Productivity of the Biosphere: 55-118.
26 Zhao, Y. . Q. Hu . H. Li . S. Wang and M. Ai. 2018. Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data. Remote Sensing 10(11) : 1759.   DOI
27 Zhang, W. . J. Qi . P. Wan . H. Wang . D. Xie . X. Wang and G. Yan. 2016. An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing 8(6) : 1-22.