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Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images

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Abstract

Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional (3D) information from airborne Light Detection and Ranging (LiDAR) data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions: individual trees volume and grassland volume. The method consists of following steps: generating and filtering the normalized digital surface model (nDSM), extracting the nDSM of urban vegetation based on the Normalized Difference Vegetation Index (NDVI), locating the local maxima points, segmenting the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume.

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References

  • Akbari H (2002). Shade trees reduce building energy use and CO2 emissions from power plants. Environ Pollut, 116(Suppl 1): S119–S126

    Article  Google Scholar 

  • Awal M A, Ohta T, Matsumoto K, Toba T, Daikoku K, Hattori S, Hiyama T, Park H (2010). Comparing the carbon sequestration capacity of temperate deciduous forests between urban and rural landscapes in central Japan. Urban For Urban Green, 9(3): 261–270

    Article  Google Scholar 

  • Blaschke T (2010). Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens, 65(1): 2–16

    Article  Google Scholar 

  • Blaschke T, Strobl J (2001). What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. Zeitschrift für Geoinformationssysteme, 6(1): 12–17

    Google Scholar 

  • Chen Y, Gillieson D (2009). Evaluation of Landsat TM vegetation indices for estimating vegetation cover on semi-arid rangelands: a case study from Australia. Can J Rem Sens, 35(5): 435–446

    Article  Google Scholar 

  • Dalponte M, Bruzzone L, Gianelle D (2011). A system for the estimation of single-tree stem diameter and volume using multireturn LIDAR data. IEEE Trans Geosci Rem Sens, 49(7): 2479–2490

    Article  Google Scholar 

  • Fang C F, Ling D L (2003). Investigation of the noise reduction provided by tree belts. Landsc Urban Plan, 63(4): 187–195

    Article  Google Scholar 

  • Feng Y, Guo R, Cheng Y (2008). Research on three dimentional city model reconstruction based on LiDAR. Geomatics & Spatial Information Technology, 31(4): 8–11 (in Chinese)

    Google Scholar 

  • Hall R J, Skakun R S, Arsenault E J, Case B S (2006). Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. For Ecol Manage, 225(1–3): 378–390

    Article  Google Scholar 

  • Hecht R, Meinel G, Buchroithner M F (2008). Estimation of urban green volume based on single-pulse LiDAR data. IEEE Trans Geosci Rem Sens, 46(11): 3832–3840

    Article  Google Scholar 

  • Hudak A T, Lefsky M A, Cohen W B, Berterretche M (2002). Integration of lidar and Landsat ETM plus data for estimating and mapping forest canopy height. Remote Sens Environ, 82(2–3): 397–416

    Article  Google Scholar 

  • Hyyppa J, Hyyppa H, Inkinen M, Schardt M, Ziegler M (2000). Forest inventory based on laser scanning and aerial photography. In: Proceedings of SPIE, Laser Radar Technology and Applications V, 4035: 106–573

    Article  Google Scholar 

  • Hyyppa J, Kelle O, Lehikoinen M, Inkinen M (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans Geosci Rem Sens, 39(5): 969–975

    Article  Google Scholar 

  • Leckie D, Gougeon F, Hill D, Quinn R, Armstrong L, Shreenan R (2003). Combined high-density lidar and multispectral imagery for individual tree crown analysis. Can J Rem Sens, 29(5): 633–649

    Article  Google Scholar 

  • Lefsky M, McHale M (2008). Volume estimates of trees with complex architecture from terrestrial laser scanning. J Appl Remote Sens, 2(1): 023521

    Article  Google Scholar 

  • Magnussen S, Eggermont P, LaRiccia V N (1999). Recovering tree heights from airborne laser scanner data. For Sci, 45(3): 407–422

    Google Scholar 

  • Means J E, Acker S A, Fitt B J, Renslow M, Emerson L, Hendrix C J (2000). Predicting forest stand characteristics with airborne scanning lidar. Photogramm Eng Remote Sensing, 66(11): 1367–1371

    Google Scholar 

  • Morancho A B (2003). A hedonic valuation of urban green areas. Landsc Urban Plan, 66(1): 35–41

    Article  Google Scholar 

  • Naesset E (1997). Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ, 61(2): 246–253

    Article  Google Scholar 

  • Naesset E, Okland T (2002). Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens Environ, 79(1): 105–115

    Article  Google Scholar 

  • Nilsson M (1996). Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sens Environ, 56(1): 1–7

    Article  Google Scholar 

  • Ozdemir I (2008). Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. Int J Remote Sens, 29(19): 5643–5655

    Article  Google Scholar 

  • Persson A, Holmgren J, Soderman U (2002). Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sensing, 68(9): 925–932

    Google Scholar 

  • Popescu S C (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass Bioenergy, 31(9): 646–655

    Article  Google Scholar 

  • Popescu S C, Wynne R H, Nelson R F (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Comput Electron Agric, 37(1–3): 71–95

    Article  Google Scholar 

  • Takahashi T, Awaya Y, Hirata Y, Furuya N, Sakai T, Sakai A (2010). Stand volume estimation by combining low laser-sampling density LiDAR data with QuickBird panchromatic imagery in closed-canopy Japanese cedar (Cryptomeria japonica) plantations. Int J Remote Sens, 31(5): 1281–1301

    Article  Google Scholar 

  • Tonolli S, Dalponte M, Neteler M, Rodeghiero M, Vescovo L, Gianelle D (2011). Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps. Remote Sens Environ, 115(10): 2486–2498

    Article  Google Scholar 

  • Tyrväinen L, Makinen K, Schipperijn J (2007). Tools for mapping social values of urban woodlands and other green areas. Landsc Urban Plan, 79(1): 5–19

    Article  Google Scholar 

  • Véga C, Durrieu S (2011). Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: application to a mountainous forest with heterogeneous stands. Int J Appl Earth Obs Geoinf, 13(4): 646–656

    Article  Google Scholar 

  • Weng Q, Lu D, Schubring J (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ, 89(4): 467–483

    Article  Google Scholar 

  • Yu B L, Liu H X, Wu J P, Hu Y J, Zhang L (2010). Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landsc Urban Plan, 98(3–4): 210–219

    Article  Google Scholar 

  • Yu B L, Liu H X, Wu J P, Lin W M (2009a). Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston. Int J Remote Sens, 30(17): 4359–4385

    Article  Google Scholar 

  • Yu B L, Liu H X, Zhang L, Wu J P (2009b). An object-based two-stage method for a detailed classification of urban landscape components by integrating airborne LiDAR and color infrared image data: a case study of downtown Houston. In: Proceedings of IEEE 2009 Joint Urban Remote Sensing Event, Shanghai

  • Zhang K, Chen S, Whitman D, Shyu M, Yan J, Zhang C (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Trans Geosci Rem Sens, 41(4): 872–882

    Article  Google Scholar 

  • Zhou J H (2001). Theory and practice on database of three-dimensional vegetation quantity. Acta Geogr Sin, 56(1): 14–23 (in Chinese)

    Google Scholar 

  • Zhou J H, Sun T Z (1995). Study on remote sensing model of threedimensional green biomass and the estimation of environmental benefits of greenery. Remote Sensing of Environment China, 10(3): 162–174 (in Chinese)

    Google Scholar 

  • Zhou T G, Luo H, Guo D (2005). Remote sensing image-based quantitative study on urban spatial 3D green quantity virescence three-dimension quantity. Acta Ecol Sin, 25(3): 415–420 (in Chinese)

    Google Scholar 

  • Zhu J (2008). The value of green space vegetation quantity in afforest planning and design. Chinese Agricultural Science Bulletin, 24(8): 360–363 (in Chinese)

    Google Scholar 

Download references

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Authors and Affiliations

  1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Yan Huang, Bailang Yu, Jianhua Zhou, Zhiming Hu & Jianping Wu

  2. Shanghai Landscape and City Appearance Administration Information Center, Shanghai, 200040, China

    Chunlin Hu & Wenqi Tan

Authors
  1. Yan Huang
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  2. Bailang Yu
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  3. Jianhua Zhou
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  4. Chunlin Hu
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  5. Wenqi Tan
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  6. Zhiming Hu
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  7. Jianping Wu
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Corresponding author

Correspondence to Bailang Yu.

Additional information

Yan Huang received the B. S. degree in Geographical Information System from Chengdu University of Technology, Chengdu, China, in 2010. She is currently a Ph.D. student in Cartography and Geographical Information System at Department of Geography, East China Normal University, Shanghai, China. Her current research interests focus on object-oriented analysis for remotely sensed images and LiDAR.

Bailang Yu received the B. S. degree and Ph.D. degree from East China Normal University, Shanghai, China in 2002 and 2009, respectively. He is currently an Associate Professor of Key Laboratory of Geographic Information Science (Ministry of Education) and Department of Geography, East China Normal University. His research interests include object-oriented analysis for remotely sensed images, LiDAR, Urban Remote Sensing, and GIS (Geographical Information System). His recent research projects have been funded by the National Natural Science Foundation of China, the Specialized Research Fund for the Doctoral Program of Higher Education, and the Fundamental Research Funds for the Central Universities of China.

Jianhua Zhou received the B. E. degree in Aerial Photogrammetry from Wuhan Institute of Surveying and Mapping, Wuhan, China in 1975 and the M. S. degree in Cartography and Geographical Information System from East China Normal University, Shanghai, China in 2000. She is currently an Associate Professor of Key Laboratory of Geographic Information Science (Ministry of Education) and Department of Geography, East China Normal University. Her research interests include quantitatively ecological remote sensing, remote sensing image analysis, and spatial data mining.

Chunlin Hu received the M. S. degree in Cartography and Geographical Information System from East China Normal University, Shanghai, China in 2004. She is currently an Engineer of Shanghai Landscape and City Appearance Administration Information Center.

Wenqi Tan received the M. S. degree in Cartography and Geographical Information System from East China Normal University, Shanghai, China in 2005. She is currently an Engineer of Shanghai Landscape and City Appearance Administration Information Center.

Zhiming Hu received the M. S. degrees in Cartography and Geographical Information System from East China Normal University, Shanghai, China in 2012. His research interests include mobile GIS and LiDAR.

Jianping Wu received the B. S. degree from Nanjing University, Nanjing, China in 1983, the M. S. degree from Peking University, Beijing, China, in 1986, and the Ph.D. degree from East China Normal University, Shanghai, China, in 1996. He is currently a Professor of Key Laboratory of Geographic Information Science (Ministry of Education) and Department of Geography, East China Normal University, China. His research interests include Geographical Information System Development and Remote Sensing Application.

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Huang, Y., Yu, B., Zhou, J. et al. Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images. Front. Earth Sci. 7, 43–54 (2013). https://doi.org/10.1007/s11707-012-0339-6

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  • DOI: https://doi.org/10.1007/s11707-012-0339-6

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