Urban structure type mapping method using spatial metrics and remote sensing imagery classification

Abstract

Urban Structure Types (USTs) stand for areas with homogeneous appearance over the urban matrix. The use of spatial metrics rises as a convenient alternative to quantify the homogeneity of areas on a specific scale. Remote sensing imagery is largely used to assess and study the urban environment, and its classification is a way to recreate the Earth’s surface digitally, both natural and urban spaces. This study proposes a method for city-scale UST mapping using remote sensing images as the unique source of information. Such a proposal comprehends the classification of images that express spatial metrics derived from previous land use and land cover (LULC) classification. We carried two case studies to assess the proposed method under different image resolutions and urban complexity conditions. For this purpose, Landsat-8 OLI and Sentinel-2 MSI images acquired from different cities in Brazil are submitted to the proposed method. An alternative object-based image classification method is included as a comparison baseline. The proposed method shows efficiency in the UST mapping process, which is highly influenced by the neighborhood size considered over the process. Also, it is verified that the proposed method is superior at a significance level of 5%.

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References

  1. Aljoufie M, Zuidgeest M, Brussel M, van Maarseveen M (2013) Spatial–temporal analysis of urban growth and transportation in jeddah city, saudi arabia. Cities 31:57–68. https://doi.org/10.1016/j.cities.2012.04.008

    Article  Google Scholar 

  2. Ananias PHM, Negri RG (2021) Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters. Int J Digit Earth 0 (0):1–22. https://doi.org/10.1080/17538947.2021.1907462

    Article  Google Scholar 

  3. Banzhaf E, Hofer R (2008) Monitoring urban structure types as spatial indicators with CIR aerial photographs for a more effective urban environmental management. IEEE J Select Topics Appl Earth Observ Remote Sens 1:129–138

    Article  Google Scholar 

  4. Banzhaf E, Höfer R, Romero H (2009) Analysing dynamic parameters for urban heat stress incorporating the spatial distribution of urban structure types. IEEE Urban Remote Sens Joint Event 1–4

  5. Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31

    Article  Google Scholar 

  6. Berger C, Voltersen M, Schmullius C, Hese S (2018) Robust mapping of urban structure types using high resolution geospatial data. gisScience 2:47–59

    Google Scholar 

  7. Böhm P (1998) Urban structural units as a key indicator for monitoring and optimizing the urban environment. Urban Ecology

  8. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  9. Bruzzone L, Persello C (2009) A novel context-sensitive semisupervised svm classifier robust to mislabeled training samples. IEEE Trans Geosci Remote Sens 47(7):2142–2154

    Article  Google Scholar 

  10. Chavez PS, Kwarteng AY (1989) Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis. Photogram Eng Remote Sensing 55(3):339–348

    Google Scholar 

  11. Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices, 2nd edn. CRC Press/Taylor & Francis, Boca Raton

    Google Scholar 

  12. Deng JS, Wand K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape Urban Plan 92:187–198

    Article  Google Scholar 

  13. Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40. https://doi.org/10.1109/TIT.1975.1055330

    Article  Google Scholar 

  14. Grizonnet M, Michel J, Poughon V, Inglada J, Savinaud M, Cresson R (2017) Orfeo toolbox: open source processing of remote sensing images. Open Geospatial Data Softw Stand 2(15)

  15. Hecht R, Herold H, Meinel G, Buchroithner M (2013) Automatic derivation of urban structure types from topographic maps by means of image analysis and machine learning. In: 26th international cartographic conference

  16. Herold M, Scepan J, Clarke KC (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plann A Econ Space 34(8):1443–1458. https://doi.org/10.1068/a3496

    Article  Google Scholar 

  17. Herold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302

    Article  Google Scholar 

  18. Herold M, Hemphill J, Dietzel C, Clarke KC (2005) Remote sensing derived mapping to support urban growth theory. Joint Symposia URBAN - URS 2005 Remote Sensing and Urban Growth Theory

  19. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8)

  20. Huang X, Liu H, Zhang L (2015) Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 53 (7):3639–3657

    Article  Google Scholar 

  21. Lehner A, Blaschke T (2019) A generic classification scheme for urban structure types. Remote Sensing 2:1–11. https://doi.org/10.3390/rs11020173

    Google Scholar 

  22. Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870. https://doi.org/10.1080/01431160600746456

    Article  Google Scholar 

  23. Mather PM (2004) Computer Processing of Remotely-Sensed Images: An Introduction. Wiley, Hoboken

    Google Scholar 

  24. Montanges AP, Moser G, Taubenböck H, Wurm M, Tuia D (2015) Classification of urban structural types with multisource data and structured models. In: 2015 joint urban remote sensing event (JURSE), pp 1–4. https://doi.org/10.1109/JURSE.2015.7120489

  25. Moon K, Downes N, Rujner H, Storch H (2009) Adaptation of the urban structure type approach for the assessment of climate change risks in ho chi minh city. 45 ISOCARP pp 1–7

  26. Mountrakis G, Im J, Ogole C (2011) Support Vector Machines in Remote Sensing: A review. ISPRS J Photogram Remote Sensing Soc 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

    Article  Google Scholar 

  27. Novack T, Stilla U (2017) Context-based classification of urban blocks according to their built-up structure. PFG J Photogram Remote Sens Geoinform Sci 85(6):365–376. https://doi.org/10.1007/s41064-017-0039-7

    Google Scholar 

  28. Pauleit S, Duhme F (2000) Assessing the environmental performance of land cover types for urban planning. Landsc Urban Plan 52:1–20. https://doi.org/10.1016/S0169-2046(00)00109-2

    Article  Google Scholar 

  29. Pham HM, Yamaguchi Y, Bui TQ (2011) A case study on the relation between city planning and urban growth using remote sensing and spatial metrics. Landsc Urban Plan 223–230

  30. Pushparaj J, Hegde AV (2017) Comparison of various pan-sharpening methods using quickbird-2 and landsat-8 imagery. Arab J Geosci 10(119). https://doi.org/10.1007/s12517-017-2878-3

  31. Simanjuntak RM, Reckien KMD (2019) Object-based image analysis to map local climate zones: The case of bandung, indonesia. Appl Geogr 106:108–121. https://doi.org/10.1016/j.apgeog.2019.04.001

    Article  Google Scholar 

  32. Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93(12):1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1

    Article  Google Scholar 

  33. Tam TH, Abd Rahman MZ, Harun S, Kaoje IU (2018) Mapping of highly heterogeneous urban structure type for flood vulnerability assessment. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 229–235. https://doi.org/10.5194/isprs-archives-XLII-4-W9-229-2018

  34. Theodoridis S, Koutroumbas K (2008) Pattern recognition fourth edition, 4th edn. Academic Press, Inc, Orlando

    Google Scholar 

  35. Tomás L, Fonseca L, Almeida C, Leonardi F, Pereira M (2016) Urban population estimation based on residential buildings volume using ikonos-2 images and lidar data. Int J Remote Sens 37(sup1):1–28. https://doi.org/10.1080/01431161.2015.1121301

    Article  Google Scholar 

  36. Webb AR (2002) Statistical pattern recognition, 2nd edn. Wiley, Chichester

    Book  Google Scholar 

  37. Webb AR, Copsey KD (2011) Statistical Pattern Recognition, 3rd edn. Wiley, Hoboken

    Book  Google Scholar 

  38. Wieland M, Torres Y, Pittore M, Benito B (2016) Object-based urban structure type pattern recognition from landsat tm with a support vector machine. Int J Remote Sens 37(17):4059–4083. https://doi.org/10.1080/01431161.2016.1207261

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the support from São Paulo Research Foundation - FAPESP (Grant 2018/01033-3).

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Correspondence to Luccas Z. Maselli.

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Maselli, L., Negri, R.G. Urban structure type mapping method using spatial metrics and remote sensing imagery classification. Earth Sci Inform (2021). https://doi.org/10.1007/s12145-021-00639-w

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Keywords

  • Urban structure types
  • Spatial metrics
  • Image classification
  • Remote sensing
  • Urban mapping