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

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.

Keywords

urban green volume / LiDAR / remote sensing image / object-based method / automatic estimation

References

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

[2] 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

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

[4] 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

[5] 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

[6] 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

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

[8] 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)

[9] 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

[10] 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

[11] 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

[12] 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.

[13] 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

[14] 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

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

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

[17] 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

[18] Morancho A B (2003). A hedonic valuation of urban green areas. Landsc Urban Plan, 66(1): 35-41

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

[20] 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

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

[22] 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

[23] 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

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

[25] 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

[26] 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

[27] 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

[28] 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

[29] Véga C, Durrieu S (2011). zMulti-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

[30] 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

[31] 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

[32] 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

[33] 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

[34] 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

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

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

[37] 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)

[38] 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)

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 41001270 and 41071275), the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20100076120017), and the Fundamental Research Funds for the Central Universities of China. We thank Prof. Ronghuan Guo and Dr. Yan Feng from Shanghai Municipal Institute of Surveying & Mapping to provide the LiDAR data.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg