An Investigation on the Development of Green Index Assessment Methods for Urban Landscapesdscapes

Document Type : Analytic Review

Authors
A. Arjmandtabar
R. Rostami
Department of architecture, Sari Branch, Islamic Azad University, Sari, Iran
Abstract


Introduction

In the mid-20th century, the rapid growth of urban population followed by high construction density have ever increasingly attracted the urban planners and designers toward considering the subject matter of urban vegetation. This has been seen as a tool to not only control adverse environmental phenomena, but also to enhance the psycho-environmental qualities for the citizens by providing the urban landscapes, with adequate levels of greenness at the same time. In this respect, numerous methods and techniques have been developed to assess and estimate the quantity and function of green spaces. During the past decades, urban scientists have tried to formulate different green indices in different situations to understand the impact of vegetation on the functions of the urban landscapes, with each index following a particular objective and methodology. The present paper is aimed at introducing different green indices and their development paths and assessment methods. Moreover, the pros and cons of each index in terms of its effect on the urban landscape are is further discussed. Finally, it is concluded that three-dimensional objective methods are superior to mental methods as well as two-dimensional objective methods.

Methodology:

Once finished with assessing the green indices based on ground surveys, such as the “green coverage ratio”, development of remote sensing technology could smooth the way toward assessing numerous green indices via different approaches by the researchers. The “Normalized Difference Vegetation Index (NDVI)” was used as a binary indicator of green and non-green areas (in terms of vegetation) by combining the near-infrared and visible light bands; providing a reference index for the assessment of other indices. Assessment of other indices, such as the “Green Index”, “Proximity to Green Space Index”, and “Urban Neighborhood Green Index”, became possible by taking a 2D view with the help of NDVI and image processing of satellite images via object-based, rather than pixel-based methods; this change of procedure led to more accurate evaluation of the studied scenes. However, particular indices such as “Green View Index” and “Google Street View”, that were intended to sense urban visual greenness recognized the previous indices as not being consistent with the citizens’ views on the ground, and hence, used images taken by visible light-sensing digital cameras combined with special software (e.g. Adobe Photoshop) to measure the greenness level. Next, considering the inevitable vertical growth of the cities, some researchers started to formulate indices for measuring the visible greenness from the floors of high-rise urban buildings. For instance, the “Floor Green View Index” makes use of the remote sensing technology and multi-spectra images as well as digital 3D models of the surface and buildings to obtain the topography and 3D morphology of the study area, which respectively leads in determining the viewpoint of each floor. The “Building Visual Green Index” uses the remote sensing technology, multispectral images, and an analytic model of the viewshed, the blind zone in the ArcGIS, and eCognition software, to evaluate the visible green space from each floor compared to that of other floors. Finally, all of the above-mentioned indices were evaluated in an objective approach, some with 2D view provided by the satellite images, while the others were based on the 3D view of the citizens.

Results and discussion:

Although the use of NDVI compare to the ground surveys for the assessment of vegetation greenness has led major change in the accuracy of measurement, but its inherent 2D view to the greenness and ignorance of the distribution of green spaces across urban areas of different heights and construction densities made the index serve as no more than a reference for assessing other indices in the urban landscape studies. This situation further ended up incorporating the green indices focused on the assessment of visible greenness to the citizens into computer-assisted software tools (e.g. ArcGIS) and image processing algorithms. It can be stipulated that, combination of the high-resolution satellite images with software tools and digital models provides the urban researchers with brilliant opportunities for the assessment of green indices.

Conclusion:

The most important and fundamental factor contributing to the revolution of green index assessment during the studied period has been the replacement of ground surveys by the remote sensing technology and high-resolution images, imposing significant influences on the accuracy of the obtained green indices. Respecting the importance of the accuracy of the green indices, the objective methods with 3D views seem to present larger potentials thanks to the absence of common problems in the subjective methods (observer’s judgment effect and possible perception issues); while assessing the greenness based on the citizens’ views rather than a solely 2D aerial view from the top, could producing produce closer estimations to the actual greenness. In the meantime, the accuracy and precision of various 3D objective methods depend on the researcher’s viewpoint, and the success of such methods in achieving their set targets is affected by particular limitations. Accordingly, complementary studies are required to address the existing limitations, and hence, achieve even more accurate greenness indices that can be assessed for various applications, including urban planning, urban design, landscape design, etc.




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Subjects

اسدی، ریحانه؛ شهابیان، پویان (1396). "ارزیابی قابلیت پیاده محوری در محدوده ایستگاه مترو تجریش با روش -QFD و ANP." برنامه‌ریزی و آمایش فضا. ۲۱ (۱) :۲۵۳-۲۷۸.
-Asadi R, Shahabian P. 2017. Planning and Assessing the Walkability of Tajrish Metro stations by ANP & QFD. MJSP. 21 (1) :253-278 [in Persian]
- تقوایی، علی اکبر؛ معروفی، سکینه (1389). "تاثیر فضاهای شهری بر ارتقاء کیفیت محیط با تاکید بر نقش مساجد". فصلنامه مدیریت شهری. 25: 234-219.
-Taghvaei, A., Marofi, S. (2010). the Impact of Urban Spaces on Improving Quality of the Environment, Emphasizing the Role of Mosques. Journal of Urban Management, 25, pp. 219-234. [in Persian]
- دویران، اسماعیل؛ غایب لو، سیما (1397). "سنجش کیفی وضعیت پایداری ایمنی در پارک های شهری (مطالعه موردی: پارک های ناحیه ای و منطقه ای شهر رشت)". برنامه ریزی و آمایش فضا. (4) 22 : 169-140.
-Daviran S, ghayebloo S. Quality Assessment of Safety Sustainability in Urban Parks (Case Study: District and Zonal Parks in Rasht City). MJSP. 2019; 22 (4) :140-169 [in Persian]
- شمس الدینی، علی (1396). "قابلیت‌سنجی کارایی داده‌های لیدار و اپتیک به منظور استخراج پارامترهای ساختاری جنگل". برنامه‌ریزی و آمایش فضا. (۲)۲۱ :۱۱۹-۱۴۵.
-Shamsoddini, A. 2017. LiDAR and optical data capability assessment for plantation structural parameter estimation Assessment of LiDAR and optical data capability in the estimation of structural parameters of plantations. MJSP. 21 (2) :119-145 [in Persian]
- Aoki, Y. 1991. Evaluation methods for landscapes with greenery. Landscape Res. 16, 3–6. http://dx.doi.org/10.1080/01426399108706344
- Arbogast, K. L., Kane, B. C., Kirwan, J. L., & Hertel, B. R. 2009. Vegetation and outdoor recess time at elementary schools: What are the connections? Journal of Environmental Psychology, 29(4), 450–456.
- Asgarzadeh, M., Lusk, A., Koga, T., Hirate, K. 2012. Measuring oppressiveness of streetscapes Landscape and Urban Planning. 107 (1), 1–11.
- Balram, S., Dragi´cevi´c, S. 2005. Attitudes toward urban green spaces: integrating questionnaire survey and collaborative GIS techniques to improve attitude measurements. Landsc. Urban Plan. 71 (2), 147–162.
- Bertram, Ch., Rehdanz, K. 2015. The role of urban green space for human well-being. Ecological Economics. 120, 139-152.
- Camacho-Cervantes, M., Schondube, J.E., Castillo, A., MacGregor-Fors, I. 2014. How do people perceive urban trees? Assessing likes and dislikes in relation to the trees of a city. Urban Ecosystems, 17(3), 230–244.
- Chen, W. Y., & Wang, D. T. 2013. Urban forest development in China: Natural endowment or socioeconomic product? Cities, 6(35), 62–68.
- Chiesura, A. 2004. The role of urban parks for the sustainable city. Landscape and Urban Planning, 68(1), 129–138.
- C.J. Tucker, J.Gatlin, S.R.Schneider, and M.A.Kuchinos. 1982. Monitoring large scale vegetation dynamics in the Nile delta and river valley from NOAA AVHRR data, in Proc. Conference on Remote Sensing of Arid and Semi-Arid Lands, Cairo, Egypt, p. 973.
- de la Barrera, F., Reyes-Paecke, S., Banzhaf, E. 2016. Indicators for green spaces in contrasting urban settings. Ecological Indicators.
- Downs, R.M., Stea, D. 1977. Maps in Minds: Reflections on Cognitive Mapping. Harper & Row, New York.
- Ellaway, A., Macintyre, S., Bonnefoy, X. 2005. Graffiti, greenery, and obesity in adults: secondary analysis of European cross sectional survey. Br. Med. J. 331, 611–612.
- F.Kogan. 1987. Vegetation index for area1 analysis of crop conditions, in Proc. 18th Conference on Agricultural and Forest Meteorology, AMS, W.Lafayette, p. 103.
- Getz, D.A., Karow, A., Kielbaso, J.J. 1982. Inner city preferences for trees and urban forestry programs. J. Arboric. 8, 258–263.
- Gorman, J. 2004. Residents’ opinions on the value of street trees depending on tree location. J. Arboric. 30, 36–44.
- Gupta, K., Kumar, P., Pathan, S.K., Sharma, K.P. 2012. Urban neighborhood green index – A measure of green spaces in urban areas. Landscape and Urban Planning 105 (2012) 325-335.
- Hall, B., Lee Kerr, M. 1991. Green index, A state-by-state guide to the nation’s environmental health. Washington, D. C: Island press.
- Hoenher, C.M., Brennan-Ramirez, L.K., Elliot, M.B., Handy, S.L., Brownson, R.C. 2005. Perceived and objective environmental measures and physical activity among urban adults. American Journal of - Preventive Medicine, 28(2S2), 105–116.
- Huang, S.L., Chen, C.W. 1970. A system dynamics approach to the simulation of urban sustainability. WIT Trans. Ecol. Environ. 34. http://dx.doi.org/10.2495/ECO990021.
- Huang, SL., Chen, CW. 1970. A system dynamics approach to the simulation of urban sustainability© 1999 WIT Press, www.witpress.com, ISSN 1743-3541
- Jacobs, A.B. 1997. Keynote: looking, learning, making. Places 1, 4–7.
- J.P. Malingreau. 1986. Global vegetation in dynamics, satellite observations over Asia, Int. J. Remote Sens., #7, 1121-1146.
- Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680.
- Kinerson, R.S. 1975. Relationships between plant surface area and respiration in loblolly pine. J. Appl. Ecol. 965–971. http://dx.doi.org/10.2307/2402102.
- Kogan, F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 15 (11), 91–100.
- Krellenberg, K., Welz, J., & Reyes-Päcke, S. 2014. Urban green areas and their potential for social interaction–A case study of a socio-economically mixed neighbourhood in Santiago de Chile. Habitat International, 44, 11–21.
- Lang, S., Schopfer, E., Holbling, D., Blaschke, TH. 2008. Quantifying and qualifying urban green by integrating remote sensing, GIS, and social science methods. Use of Landscape Sciences for the Assessment of Environmental Security, 93–105.
- Li, D., & Sullivan, W. C. 2016. Impact of views to school landscapes on recovery from stress and mental fatigue. Landscape and Urban Planning, 148(2), 149–158.
- Li, X., Meng, Q., Li, W., Zhang, Ch., Jancso, T., Mavromatis, S. 2014. An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery. Annals of GIS. 20:3, 193-203
- Li, X., Zhanga, Ch., Li, W., Ricardb, R., Mengc, Q., Zhanga, W. 2015. Assessing street-level urban greenery using Google Street View and a modified green view index, Urban Forestry & Urban Greening 14 (2015) 675–685.
- Lin, H. 2008. “Method of Image Segmentation on HighResolution Image and Classification for Land -Covers.” Fourth International Conference on Natural Computation, Jinan, October 18–20, 563–566. IEEE.
- Lindal, P., Hartig, T. 2015. Effects of urban street vegetation on judgments of restoration likelihood, Urban Forestry & Urban Greening 14 (2015) 200–209.
- Long, Y., Tang, J., 2018. Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing. Landscape and Urban Planning. www.elsevier.com/locate/landurbplan
- Long, Y., Liu, L. 2017. How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View. https://doi.org/10.1371/journal.pone.0171110 html
- Long, Y., & Ye, Y. 2016. Human-scale urban form: Measurements, performances, and urban planning & design interventions. South Architecture, 8(5), 39–45.
- Lu, Y. 2018. The association of urban greenness and walking behavior: Using google street view and deep learning techniques to estimate residents’ exposure to urban greenness. International Journal of Environmental Research and Public Health, 15, 1576.
- Lu, Y., Sarkar, C., & Xiao, Y. 2018. The effect of street-level greenery on walking behavior: Evidence from Hong Kong. Social Science and Medicine, 208, 41–49.
- Manley, B. 2001. Statistics for Environmental Science and Management. Chapman and Hall/CRC, Boca Raton, Florida.
- Mathieu, R., Freeman, C., Aryal, J. 2007. Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape Urban Plann. 81 (3), 179–192. http://dx.doi.org/10.1016/j.landurbplan.2006.11.
- Mensah, C. A. 2014. Destruction of urban green spaces: A problem beyond urbanization in Kumasi city (Ghana). American Journal of environmental Protection. 3(1): 1-9.
- Motohka, T., Nasahara, K.N., Oguma, H., Tsuchida, S. 2010. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2 (10), 2369–2387.
- Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115 (5), 1145–1161.
- Nowak, D. J., & Greenfield, E. J. 2012. Tree and impervious cover in the United States. Landscape and Urban Planning, 107(1), 21–30.
- Pauleit, S. (Ed.). 2004. An ecological approach to Greenstructure Planning. COST Action C11, University of Manchester.
- Qiu, L., Nielsen, A. B. 2015. Are Perceived Sensory Dimensions a Reliable Tool for Urban Green Space Assessment and planning?. Landscape Reaserch, 40:7, 834- 854, DOI: 10.1080/01426397.2015.1029445
- Schiewe, J. 2002. “Segmentation of High Resolution Remotely Sensed Data Concepts, Applications and Problems.” In Joint ISPRS Commission IV Symposium: Geospatial Theory, Processing and Applications, July 9–12. CDROM.
- Schöpfer, E., Lang, S., Blaschke, T. 2005. A “GREEN INDEX” INCORPORATING REMOTE SENSING AND CITIZEN’S PERCEPTION OF GREEN SPACE. http://www.stadtentwicklung.berlin.de/agenda21/de/service/ download/Agendaentwurf21April04.pdf
- Shen, G. Q., Wan, C. 2015. Encouraging the use of urban green space: The mediating role of attitude, perceived usefulness and perceived behavioural control. Habitat International. 50. 130-139.
- Smardon, R.C. 1988. Perception and aesthetics of the urban environment: review of the role of - vegetation. Landscape Urban Plann. 15 (1–2), 85–106. http://dx.doi.org/10.
- Taylor, B., Fernando, P., Bauman, A., Williamson, A., Craig, J., Redman, S. 2011. Measuring the quality of public open space using Google Earth. Am. J. Prev. Med. 40 (2), 105–112
- Tschense, H. 1998. Environmental quality goals and standards as a basis and way to Agenda 21 for Leipzig. In: Breuste, J., H. Feldmann, Uhlmann, O. (Eds.), Urban Ecology, Berlin: Springer, pp. 43-48.
- Tucker, C.J., Sellers, P.J., 1986. Satellite remote sensing of primary production, Int. J. Remote Sensing, #7, 1395-1416.
- Van Herzele, A., Wiedemann, T. 2003. A monitoring tool for the provision of accessible and attractive urban green spaces. Landsc. Urban Plan. 63, 109–126.
- Wang, W., Lin, Z., Zhang, l., Yu, T., Ciren, P., Zhu, Y. 2018. Building visual green index: A measure of visual green spaces for urban building. Urban Forestry & Urban Greening. https://doi.org/10.1016/j.ufug.2018.04.004
- Wolf, K. L. 2005. Business district streetscapes, trees, and consumer response. Journal of Forestry, 103(8), 396–400.
- Wolf, K.L., 2009. Strip malls, city trees, and community values. Arboric. Urban For. 35, 33–40.
- Yang, J., Zhao, L., Mcbride, J., Gong, P., 2009. Can you see green? Assessing the visibility of urban forests in cities, Landscape and Urban Planning 91 (2009) 97–104
- Yao, Y., Zhu, X., Xu, Y., Yang, H., Wu, X., Li, Y., Zhang, Y. 2012. Assessing the visual quality of green landscaping in rural residential areas: the case study of Changzhou, China. Environ. Monit. Assess. 184, 951–967.
- Ye, Y., Richards, D., Lu, Y., Song, X., Zhuang, Y., Zeng, W., Zhong, T. 2018. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landscape and Urban Planning.
- Yu, B., Yu, S., Song, W., Wu, B., Zhou, J., Huang, Y., Wu, J., Zhao, F., Mao, W. 2016. View-based greenery: A three-dimensional assessment of city buildings’ green visibility using Floor Green View Index, Landscape and Urban Planning 152 (2016) 13–26.
The Journal of Spatial Planning and Geomatics
Volume 24, Issue 2
June 2020
Pages 43-84