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Establishment of an Improved Floor Area Ratio with High-Resolution Satellite Imagery

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Abstract

An Improved Floor Area Ratio (IFAR) which can describe the intensity of urban land development was proposed and estimated by means of remote sensing technique. The paper intent to conduct a quick and high-precision estimation of the index based on building shadows in high-resolution images. Firstly, the spectral characteristics of shadows were analyzed to establish a ratio band SR which would enhance shadow information. The object-oriented method was used to achieve the extraction of shaded areas. Secondly, GIS method was used to estimate the shadow length and building height. The inspection of the building height between the estimation result and the filed measured data showed an accuracy of 90%. Finally, the gross floor area of each building was calculated by multiplying the storeys with the basement area. Thiessen polygons were built by assuming the centroid of the basement area as a point. The IFAR of each building was the ratio of the gross floor area to the corresponding polygon. The IFAR have more abundant connotations by taking more information into consideration. The remote sensing tool can well extract the proposed index accurately on a very large scale.

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References

  • Arévalo, V., González, J., & Ambrosio, G. (2008). Shadow detection in colour high-resolution satellite images. International Journal of Remote Sensing, 29, 1945–1963.

    Article  Google Scholar 

  • Chen, J. W., & Han, X. P. (2005). Semiautomatic extraction of floor area ratio based on construction shadow in high resolution remote sensing image. Geomatics and Information Science of Wuhan University, 30, 580–587.

    Google Scholar 

  • Cheng, F., & Thiel, K. H. (1995). Delimiting the building heights in a city from the shadow in a panchromatic SPOT-image—Part 1. Test of forty-two buildings. International Journal of Remote Sensing, 16, 409–415.

    Article  Google Scholar 

  • Cui, Z. D., Tang, Y. Q., Yan, X. X., Yan, C. L., Wang, H. M., & Wang, J. X. (2010). Evaluation of the geology-environmental capacity of buildings based on the ANFIS model of the floor area ratio. Bulletin of Engineering Geology and the Environment, 69, 111–118.

    Article  Google Scholar 

  • Dobesova, Z., & Krivka, T. (2012). Walkability index in the urban planning: A case study in Olomouc city. In J. Burian (Ed.), Advances in spatial planning (pp. 179–197). InTech: Rijeka, Croatia.

    Google Scholar 

  • Etemadnia, H., & Etemadnia, M. R. (2003). Automatic image shadow identification using LPF in homomorphic processing system. In C. Sun, H. Talbot, S. Ourselin & T. Adriaansen (Eds.), Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, 10-12 December 2003 (pp. 429–438). Macquarie University, Sydney, Australia.

  • Gevers, T., & Smeulders, A. W. (1999). Color-based object recognition. Pattern Recognition, 32, 453–464.

    Article  Google Scholar 

  • Giridharan, R., Ganesan, S., & Lau, S. (2004). Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy and Buildings, 36, 525–534.

    Article  Google Scholar 

  • Han, X. P., Xu, J. G., & Fu, X. M. (2005). A study on estimating urban FAR based on high-resolution satellite images. Remote Sensing Information, 2, 24–28.

    Google Scholar 

  • Jain, M., & Siedentop, S. (2014). Is spatial decentralization in national capital region delhi, India effective? An intervention-based evaluation. Habitat International, 42, 30–38.

    Article  Google Scholar 

  • Kono, T., Kaneko, T., & Morisugi, H. (2010). Necessity of minimum floor area ratio regulation: A second-best policy. The Annals of Regional Science, 44, 523–539.

    Article  Google Scholar 

  • Li, Y., Sasagawa, T., & Gong, P. (2004). A system of the shadow detection and shadow removal for high resolution city aerial photo. In XXth ISPRS Congress, 2004 (pp. 12–23).

  • Li, J. Y., Zhang, L., Wu, B. F., & Ma, X. H. (2007). Study on extracting building density and floor area ratio based on high resolution image. Remote Sensing Technology and Application, 22, 209–313.

    Google Scholar 

  • Liu, J. H., Fang, T., & Li, D. R. (2011). Shadow detection in remotely sensed images based on self-adaptive feature selection. IEEE Transactions on Geoscience and Remote Sensing, 49, 5092–5103.

    Article  Google Scholar 

  • Liu, W., & Yamazaki, F. (2010). In shadow extraction and correction from quickbird images. In IEEE international geoscience and remote sensing symposium (IGARSS), 25–30 July 2010 (pp. 2206–2209).

  • Liu, W., & Yamazaki, F. (2012). Object-based shadow extraction and correction of high-resolution optical satellite images. IEEE Journal of Applied Earth Observations and Remote Sensing, 5, 1296–1302.

    Article  Google Scholar 

  • Lu, D. S. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27, 1297–1328.

    Article  Google Scholar 

  • Pan, X. Z., Zhao, Q. G., Chen, J., Liang, Y., & Sun, B. (2008). Analyzing the variation of building density using high spatial resolution satellite images: The example of Shanghai City. Sensors-Basel, 8, 2541–2550.

    Article  Google Scholar 

  • Pu, Z. (2008). Shadow detection and removal based on object-oriented method in high spatial resolution remote sense image. Remote Sensing Technology and Application, 23(6), 735–738.

    Google Scholar 

  • Sarabandi, P., Yamazaki, F., Matsuoka, M., & Kiremidjian, A. (2004). Shadow detection and radiometric restoration in satellite high resolution images. In Proceedings of IGARSS-2004, September 2004, Anchorage, Alaska, CDROM 2004. New York: IEEE.

  • Shettigara, V. K., & Sumerling, G. M. (1998). Height determination of extended objects using shadows in SPOT images. Photogrammetric Engineering and Remote Sensing, 64, 35–43.

    Google Scholar 

  • Srivanit, M. (2013). Integrated urban thermal environment assessment methods for multi-scale spatial planning. Japan: Saga University.

    Google Scholar 

  • Srivanit, M., & Kazunori, H. (2011a). Estimating spatial disaggregation of urban thermal responsiveness on summer diurnal range with a numerical modeling approach in Bangkok, Thailand. World Academy of Science, Engineering and Technology, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 5(12),722–731.

  • Srivanit, M., & Kazunori, H. (2011b). The influence of urban morphology indicators on summer diurnal range of urban climate in Bangkok metropolitan area, Thailand. International Journal of Civil and Environmental Engineering, 11(5), 34–46.

    Google Scholar 

  • Suzuki, A., Shio, A., Arai, H., & Ohtsuka, S. (2000). Dynamic shadow compensation of aerial images based on color and spatial analysis. In 15th International conference on pattern recognition, 2000. Proceedings (pp. 317–320). IEEE.

  • Tsai, V. J. (2006). A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Transactions on Geoscience and Remote Sensing, 44, 1661–1671.

    Article  Google Scholar 

  • Wang, S. G., Guo, Z. J., & Li, D. R. (2003). Shadow compensation of color aerial images. Geomatics and Information Science of Wuhuan University, 28, 514–516.

    Google Scholar 

  • Zha, Y. (2001). A study on remote sensing methods in estimating urban built-up volume ratio based on aerial photographs. Progress in Geography, 4, 378–383.

    Google Scholar 

  • Zhang, Y. (2002). Problems in the fusion of commercial high-resolution satelitte as well as Landsat 7 images and initial solutions. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34, 587–592.

    Google Scholar 

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Authors and Affiliations

  1. School of Geology and Geometics, Tianjin Chengjian University, Tianjin, 300384, China

    Guangyao Duan & Zhenghui Yi

  2. Department of Resources Environment and Tourism, Capital Normal University, Beijing, 100048, China

    Huili Gong & Beibei Chen

  3. Beijing Institute of Geology, Beijing, 100120, China

    Huanhuan Liu

Authors
  1. Guangyao Duan
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  2. Huili Gong
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  3. Huanhuan Liu
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  4. Zhenghui Yi
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  5. Beibei Chen
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Correspondence to Guangyao Duan.

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Duan, G., Gong, H., Liu, H. et al. Establishment of an Improved Floor Area Ratio with High-Resolution Satellite Imagery. J Indian Soc Remote Sens 46, 275–286 (2018). https://doi.org/10.1007/s12524-017-0674-x

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  • DOI: https://doi.org/10.1007/s12524-017-0674-x

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