Earth Science Informatics

, Volume 10, Issue 4, pp 457–470 | Cite as

LiDAR-based three-dimensional street landscape indices for urban habitability

  • Liang Cheng
  • Song Chen
  • Sensen Chu
  • Shuyi Li
  • Yi Yuan
  • Yu Wang
  • Manchun Li
Research Article
First Online:
Received:
Accepted:
  • 336 Downloads

Abstract

Streets are like the skeleton of a city. They not only guarantee urban traffic functions and facilitate business activities, but also perform an integral component of the urban landscape. Urban habitability, that is to judge whether a place is suitable for people to live, has been a popular topic for years. A mobile laser scanning (MLS) system can obtain close-range three-dimensional (3D) light detection and ranging (LiDAR) data of the sides and surfaces of urban streets. This study explored the possibility of analyzing urban street space landscapes based on MLS LiDAR, and proposed four LiDAR-based 3D street landscape indices for urban habitability: 3D green biomass, street enclosure, sunshine index, and landscape diversity index. Experiments performed in Jianye District of Nanjing (China) showed that these four street landscape indices accorded well with the actual situation and they reflected user perception of street space. Thus, the proposed indices could help us to assess urban landscape from a 3D perspective. To sum up, this study suggest a new type of data for landscape study, and provide an automatic information acquirement for urban habitability assessment.

Keywords

Ecology Street landscape MLS LiDAR Geographic information science 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 41622109, and Grant 41371017.

References

  1. Aimaiti Y, Kasimu A, Jing G (2016) Urban landscape extraction and analysis based on optical and microwave ALOS satellite data. Earth Sci Inf 9(4):425–435CrossRefGoogle Scholar
  2. Alexander C, Ishikawa S, Silverstein M (1977) A pattern language: towns, buildings, construction. Oxford University Press, OxfordGoogle Scholar
  3. Alonzo M, Bookhagen B, Roberts DA (2014) Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens Environ 148:70–83CrossRefGoogle Scholar
  4. Antrop M, Stobbelaar DJ, Mansvelt JDV (2000) Background concepts for integrated landscape analysis. Agric Ecosyst Environ 77(1–2):17–28CrossRefGoogle Scholar
  5. Arriaza M, Cañas-Ortega JF, Cañas-Madueño JA, Ruiz-Aviles P (2004) Assessing the visual quality of rural landscapes. Landsc Urban Plan 69(1):115–125CrossRefGoogle Scholar
  6. Badland H, Schofield G (2005) Transport, urban design, and physical activity: an evidence-based update. Transp Res Part D: Transp Environ 10(3):177–196CrossRefGoogle Scholar
  7. Carmona M (2010) Public places - urban spaces: the dimensions of urban design. ArchitecturalGoogle Scholar
  8. Clay GR, Smidt RK (2004) Assessing the validity and reliability of descriptor variables used in scenic highway analysis. Landsc Urban Plan 66(4):239–255CrossRefGoogle Scholar
  9. Daniel TC (2001) Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landsc Urban Plan 54(1–4):267–281CrossRefGoogle Scholar
  10. Ersoy E, Jorgensen A, Warren PH (2015) Measuring the spatial structure of urban land uses. The case of Sheffield, UK. J Environ Prot Ecol 16(1):393–401Google Scholar
  11. Garré S, Meeus S, Gulinck H (2009) The dual role of roads in the visual landscape: a case-study in the area around Mechelen (Belgium). Landsc Urban Plan 92(2):125–135CrossRefGoogle Scholar
  12. Gerstenberg T, Hofmann M (2016) Perception and preference of trees: a psychological contribution to tree species selection in urban areas. Urban For Urban Green 15:103–111CrossRefGoogle Scholar
  13. Hummel S, Hudak AT, Uebler EH, Falkowski MJ, Megown KA (2011) A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur national forest. J For 109(5):267–273Google Scholar
  14. Jacobs AB (1993) Great streets. The MIT Press, BostonGoogle Scholar
  15. Jochem A, Höfle B, Rutzinger M (2011) Extraction of vertical walls from mobile laser scanning data for solar potential assessment. Remote Sens 3(4):650–667CrossRefGoogle Scholar
  16. Kiray ZD, Yildizci AC (2014) Impact of landscape architectural practices on the environment. J Environ Prot Ecol 15(2):565–573Google Scholar
  17. Liang X, Hyyppä J, Kukko A, Kaartinen H, Jaakkola A, Yu X (2014) The use of a mobile laser scanning system for mapping large forest plots. IEEE Geosci Remote Sens Lett 11(9):1504–1508CrossRefGoogle Scholar
  18. Liu B, Fan R (2014) Quantitative analysis of the visual attraction elements of landscape space. J Nanjing For Univ 38(4):149–152Google Scholar
  19. Lukač N, Žalik B (2013) GPU-based roofs' solar potential estimation using LiDAR data. Comput Geosci 52:34–41CrossRefGoogle Scholar
  20. Lukač N, Žlaus D, Seme S, Žalik B, Štumberger G (2013) Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data. Appl Energy 102:803–812CrossRefGoogle Scholar
  21. MacFaden SW, O’Neil-Dunne JP, Royar AR, Lu JW, Rundle AG (2012) High-resolution tree canopy mapping for new York City using LiDAR and object-based image analysis. J Appl Remote Sens 6(1):063567Google Scholar
  22. Morar T, Radoslav R, Spirdon LC, Pacurar L (2014) Assessing pedestrian accessibility to green space using GIS. Transylvanian Rev Adm Sci 42E:116–139Google Scholar
  23. Mullaney K, Lucke T, Trueman SJ (2015) A review of benefits and challenges in growing street trees in paved urban environments. Landsc Urban Plan 134:157–166CrossRefGoogle Scholar
  24. Ozkan UY (2014) Assessment of visual landscape quality using IKONOS imagery. Environ Monit Assess 186(7):4067CrossRefGoogle Scholar
  25. Stamps AE (2005) Enclosure and safety in urbanscapes. Environ Behav 37(1):102–133CrossRefGoogle Scholar
  26. Swatantran A, Dubayah R, Roberts D, Hofton M, Blair JB (2011) Mapping biomass and stress in the sierra Nevada using LiDAR and hyperspectral data fusion. Remote Sens Environ 115(11):2917–2930CrossRefGoogle Scholar
  27. Swimmer E, Whiteman J, Taintor R (1999) Byway beginnings: understanding, inventorying, and evaluating a byway's intrinsic qualities. National Scenic Byways Program Publication, Washington DCGoogle Scholar
  28. Wang Y, Cheng L, Chen Y, Wu Y, Li M (2016) Building point detection from vehicle-borne LiDAR data based on voxel group and horizontal hollow analysis. Remote Sens 8(5):419CrossRefGoogle Scholar
  29. World Health Organization (2010) Urban planning, environment and health: from evidence to policy action http://www.euro.who.int/__data/assets/pdf_file/0004/114448/E93987.pdf?ua=1. Accessed 22 Apr 2012
  30. Yang B, Fang L, Li J (2013) Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens 79(5):80–93CrossRefGoogle Scholar
  31. Yao T, Yang X, Zhao F, Wang Z, Zhang Q, Jupp D, Lovell J, Culvenor D, Newnham G, Ni-Meister W, Schaaf C, Woodcock C, Wang J, Li X, Strahler A (2011) Measuring forest structure and biomass in New England forest stands using echidna ground-based LiDAR. Remote Sens Environ 115(11):2965–2974CrossRefGoogle Scholar
  32. Yokoya N, Nakazawa S, Matsuki T, Iwasaki A (2014) Fusion of hyperspectral and LiDAR data for landscape visual quality assessment. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2419–2425CrossRefGoogle Scholar
  33. Yu S, Wu B, Tan W, Yue W, Hu C, Wu J, Yu B (2015) Estimation of 3D urban forest green volume using VLS data and high-resolution remote sensing images. Sci Surv Mapp 40(9):82–87Google Scholar
  34. Zhou J, Sun T (1995) Study on remote sensing model of three-dimensional green biomass and the estimation of environmental benefits of greenery. J Remote Sens 10(3):162–174Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingChina
  2. 2.Collaborative Innovation Center for the South Sea StudiesNanjing UniversityNanjingChina
  3. 3.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing UniversityNanjingChina
  4. 4.Department of Geographic Information ScienceNanjing UniversityNanjingPeople’s Republic of China
  5. 5.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

Personalised recommendations