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

doi:10.1007/s11707-012-0339-6

Front Earth Sci

0, Vol.

Issue () : 43-54 https://doi.org/10.1007/s11707-012-0339-6

RESEARCH ARTICLE

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

Yan HUANG¹, Bailang YU¹(

), Jianhua ZHOU¹, Chunlin HU², Wenqi TAN², Zhiming HU¹, Jianping WU¹

1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; 2. Shanghai Landscape and City Appearance Administration Information Center, Shanghai 200040, China

Download: PDF(992 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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

Corresponding Author(s): YU Bailang,Email:blyu@geo.ecnu.edu.cn

Issue Date: 05 March 2013

Cite this article:

Yan HUANG,Bailang YU,Jianhua ZHOU, et al. Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images[J]. Front Earth Sci, 0, (): 43-54.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0339-6
https://academic.hep.com.cn/fesci/EN/Y0/V/I/43

Fig.1 Geographic location and airborne near-infrared images of the Lujiazui region, Shanghai, 2006

Fig.2 The hierarchical structure and essential attributes of vegetation objects

Fig.3 The procedure of urban green volume estimation

Fig.4 The nDSM of the Lujiazui region from airborne LiDAR data (2006)

Fig.5 Illustration of identifying individual tree objects by using airborne LiDAR data and near-infrared RS images. (a) original high resolution airborne near-infrared photograph; (b) original nDSM; (c) filtered nDSM; (d) high resolution airborne near-infrared photograph after extracting vegetation; (e) local maximum points (red crosses) based on filtered nDSM of urban vegetation; (f) segmented results of individual tree crown objects (green pseudo-circles)

Fig.6 Segmented tree crown area compared with interpreted tree crown area

Fig.7 The software tool for automatic estimation of urban green volume

Fig.8 The 3D-view of vegetation objects

Tab.1 The parameters and green equations of individual tree object

Fig.9 Estimated green volume compared with the green volume calculated by Zhou’s equation

Fig.10 The mean green volume in street blocks of the Lujiazui region

Fig.11 The mean green volume in grids (10 m × 10 m) of the Lujiazui region

1	Akbari H (2002). Shade trees reduce building energy use and CO₂ emissions from power plants. Environ Pollut , 116(Suppl 1): S119-S126 doi: 10.1016/S0269-7491(01)00264-0 pmid:11833899
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 doi: 10.1016/j.ufug.2010.01.007
3	Blaschke T (2010). Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens , 65(1): 2-16 doi: 10.1016/j.isprsjprs.2009.06.004
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 doi: 10.5589/m09-037
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 doi: 10.1109/TGRS.2011.2107744
7	Fang C F, Ling D L (2003). Investigation of the noise reduction provided by tree belts. Landsc Urban Plan , 63(4): 187-195 doi: 10.1016/S0169-2046(02)00190-1
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 doi: 10.1016/j.foreco.2006.01.014
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 doi: 10.1109/TGRS.2008.2001771
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 doi: 10.1016/S0034-4257(02)00056-1
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 . doi: 10.1117/12.397783
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 doi: 10.1109/36.921414
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 doi: 10.5589/m03-024
15	Lefsky M, McHale M (2008). Volume estimates of trees with complex architecture from terrestrial laser scanning. J Appl Remote Sens , 2(1): 023521 doi: 10.1117/1.2939008
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 doi: 10.1016/S0169-2046(03)00093-8
19	Naesset E (1997). Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ , 61(2): 246-253 doi: 10.1016/S0034-4257(97)00041-2
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 doi: 10.1016/S0034-4257(01)00243-7
21	Nilsson M (1996). Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sens Environ , 56(1): 1-7 doi: 10.1016/0034-4257(95)00224-3
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 doi: 10.1080/01431160802082155
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 doi: 10.1016/j.biombioe.2007.06.022
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 doi: 10.1016/S0168-1699(02)00121-7
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 doi: 10.1080/01431160903380623
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 doi: 10.1016/j.rse.2011.05.009
28	Tyrv?inen 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 doi: 10.1016/j.landurbplan.2006.03.003
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 doi: 10.1016/j.jag.2011.04.002
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 doi: 10.1016/j.rse.2003.11.005
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 doi: 10.1016/j.landurbplan.2010.08.004
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 doi: 10.1080/01431160802555846
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 doi: 10.1109/TGRS.2003.810682
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)

[1]	Boling YIN, Dongjie GUAN, Yuxiang ZHANG, He XIAO, Lidan CHENG, Jiameng CAO, Xiangyuan SU. How to accurately extract large-scale urban land? Establishment of an improved fully convolutional neural network model[J]. Front. Earth Sci., 2022, 16(4): 1061-1076.
[2]	Shengming TANG, Yun GUO, Xu WANG, Jie TANG, Tiantian LI, Bingke ZHAO, Shuai ZHANG, Yongping LI. Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019)[J]. Front. Earth Sci., 2022, 16(1): 75-89.
[3]	Sizhang TANG,Chaomin SHEN,Guixu ZHANG. Adaptive regularized scheme for remote sensing image fusion[J]. Front. Earth Sci., 2016, 10(2): 236-244.

Viewed

Full text

Abstract

Cited

Shared

Discussed