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WorldView-3 Imagery and GEOBIA Method for the Urban Land Use Pattern Analysis: Case Study City of Split, Croatia

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Geographical Information Systems Theory, Applications and Management (GISTAM 2021, GISTAM 2022)

Abstract

In most urban environments, loss of natural vegetation, the reduction of open spaces, and the rapid invasive transformation of the natural environment into impervious have happened. These changes can lead to a decline in life quality and in an increase in various economic, social, ecological, and infrastructural problems and risks. The complexity of the urban environment at various scales requires the application of high spatial and temporal resolution data in the process of urban planning. The main goal of this paper was to derive specific landscape metrics characteristic for urban areas based on WV-3 very-high-resolution imagery and the GEOBIA method. A supervised machine learning technique support vector machine (SVM) was used as a classification algorithm. The derived land-cover model is evaluated using the confusion matrix and related accuracy metrics: user accuracy (UA), producer’s accuracy (PA), overall accuracy (OA), and Kappa coefficient. Land-cover classification accuracy assessment resulted in moderate overall accuracy while aggregation of classes depending on the physical characteristics of the material increased OA. For landscape diversity and area metric analysis, aggregated classes were used in combination with user-defined polygons. In the city of Split, there is no absolute homogeneity (SHDI = 0) within any of the hexagons. Inner parts of the city have a higher SHDI than the outskirts but impervious surfaces are the dominant material. Urban planning indicators (UPIs), have been derived for statistical circles (SC) of Split settlement in Croatia. Vegetation indicators (TCR - tree cover ratio, LCR - lawn cover ratio, GCR - green cover ratio) and indicators of urbanization (SCR - street cover ratio, BCR - building cover ratio, IMR - impervious surface ratio) were derived from the derived land cover model. The UPIs values at the studied level are the reflection of the historical spatial-functional development of the Split settlement. These types of UPIs can be used at the neighborhood level of urban planning and analysis of different issues in an urban environment.

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References

  1. Liu, Y.: Modelling Urban Development with Geographical Information Systems and Cellular Automata. CRC Press (2009)

    Google Scholar 

  2. Du, S., Shi, P., Van Rompaey, A., Wen, J.: Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Nat. Hazards 76(3), 1457 (2015)

    Article  Google Scholar 

  3. Wang, Z., et al.: Impact of rapid urbanization on the threshold effect in the relationship between impervious surfaces and water quality in Shanghai, China. Environ. Pollut., 115569 (2020)

    Google Scholar 

  4. Petralli, M., Massetti, L., Brandani, G., Orlandini, S.: Urban planning indicators: useful tools to measure the effect of urbanization and vegetation on summer air temperatures. Int. J. Climatol. 34(4), 1236–1244 (2014)

    Article  Google Scholar 

  5. Sleavin, W.J., Civco, D.L., Prisloe, S., Giannotti, L.: Measuring impervious surfaces for non-point source pollution modeling. In: Proceedings of the ASPRS 2000 Annual Conference, pp. 22–26, May 2000

    Google Scholar 

  6. Sinha, B.R.K. (ed.): Multidimensional Approach to Quality of Life Issues (2019)

    Google Scholar 

  7. Watson, V.: ‘The planned city sweeps the poor away…’: urban planning and 21st century urbanization. Prog. Plan. 72(3), 151–193 (2009)

    Article  Google Scholar 

  8. Miller, R.B., Small, C.: Cities from space: potential applications of remote sensing in urban environmental research and policy. Environ. Sci. Policy 6(2), 129–137 (2003)

    Article  Google Scholar 

  9. Hall, P.G.: Urban and Regional Planning. 4 edn. (2002)

    Google Scholar 

  10. Anguluri, R., Narayanan, P.: Role of green space in urban planning: outlook towards smart cities. Urban For. Urban Greening 25, 58–65 (2017)

    Article  Google Scholar 

  11. Levy, J.M.: Contemporary Urban Planning. Taylor & Francis (2016)

    Book  Google Scholar 

  12. Sénécal, G.: Urban environment: mapping a concept. Introductory note. Environ. Urbain Urban Environ. 1 (2007)

    Google Scholar 

  13. Blaschke, T., Hay, G.J., Weng, Q., Resch, B.: Collective sensing: integrating geospatial technologies to understand urban systems—an overview. Remote Sens. 3(8), 1743–1776 (2011)

    Article  Google Scholar 

  14. Brownson, R.C., Hoehner, C.M., Day, K., Forsyth, A., Sallis, J.F.: Measuring the built environment for physical activity: state of the science. Am. J. Prev. Med. 36(4), S99–S123 (2009)

    Article  Google Scholar 

  15. Gong, Y., Palmer, S., Gallacher, J., Marsden, T., Fone, D.: A systematic review of the relationship between objective measurements of the urban environment and psychological distress. Environ. Int. 96, 48–57 (2016)

    Article  Google Scholar 

  16. Abbate, G., Fiumi, L., De Lorenzo, C., Vintila, R.: Evaluation of remote sensing data for urban planning. Applicative examples by means of multispectral and hyperspectral data. In: 2nd GRSS/ISPRS Joint Workshop on 2003 Remote Sensing and Data Fusion over Urban Areas (2003)

    Google Scholar 

  17. Tenedório, J.A., Rebelo, C., Estanqueiro, R., Henriques, C.D., Marques, L., Gonçalves, J.A.: New developments in geographical information technology for urban and spatial planning. In: Geospatial Research: Concepts, Methodologies, Tools, and Applications, pp. 1965–1997. IGI Global (2016)

    Google Scholar 

  18. Bodzin, A.M., Cirucci, L.: Integrating geospatial technologies to examine urban land-use change: a design partnership. J. Geogr. 108(4–5), 186–197 (2009)

    Google Scholar 

  19. Marić, I., Šiljeg, A., Domazetović, F.: Geoprostorne tehnologije u 3D dokumentaciji i promociji kulturne baštine–primjer utvrde Fortica na otoku Pagu. Geodetski glasnik 50, 19–44 (2019)

    Google Scholar 

  20. Dibiase, D., et al.: Geographic Information Science & technology: body of knowledge. USGIS, Association of American Geographers, Washington, DC (2006)

    Google Scholar 

  21. Dimoudi, A., Nikolopoulou, M.: Vegetation in the urban environment: microclimatic analysis and benefits. Energy Build. 35(1), 69–76 (2003)

    Article  Google Scholar 

  22. LeGates, R., Tate, N.J., Kingston, R.: Spatial thinking and scientific urban planning. Environ. Plann. B. Plann. Des. 36(5), 763–768 (2009)

    Article  Google Scholar 

  23. McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E.: FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps, project homepage, University of Massachusetts, Amherst (2002)

    Google Scholar 

  24. Vivoni, E.R., Teles, V., Ivanov, V.Y., Bras, R.L., Entekhabi, D.: Embedding landscape processes into triangulated terrain models. Int. J. Geogr. Inf. Sci. 19(4), 429–457 (2005)

    Article  Google Scholar 

  25. Schindler, S., Poirazidis, K., Wrbka, T.: Towards a core set of landscape metrics for biodiversity assessments: a case study from Dadia National Park, Greece. Ecol. Ind. 8(5), 502–514 (2008). https://doi.org/10.1016/j.ecolind.2007.06.001

    Article  Google Scholar 

  26. Uuemaa, E., Antrop, M., Roosaare, J., Marja, R., Mander, Ü.: Landscape metrics and indices: an overview of their use in landscape research. Living Rev. Landscape Res. 3(1), 1–28 (2009)

    Google Scholar 

  27. Aghsaei, H., et al.: Effects of dynamic land use/land 1039 cover change on water resources and sediment yield in the Anzali wetland catchment, Gilan, Iran. Sci. Total Environ. 712, 136449 (2020). https://doi.org/10.1016/j.scitotenv.2019.136449

  28. Horning, N.: Reference module in earth systems and environmental sciences. Remote Sens. (2018). https://doi.org/10.1016/B978-0-12-409548-9.10607-4

  29. Wang, Z., Gang, C., Li, X., Chen, Y., Li, J.: Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images. Int. J. Remote Sens. 36(4), 1055–1069 (2015)

    Article  Google Scholar 

  30. Zhao, C., Fu, G., Liu, X., Fu, F.: Urban planning indicators, morphology and climate indicators: a case study for a north–south transect of Beijing, China. Build. Environ. 46, 1174–1183 (2011)

    Article  Google Scholar 

  31. Lin, P., Lau, S.S.Y., Qin, H., Gou, Z.: Effects of urban planning indicators on urban heat island: a case study of pocket parks in high-rise high-density environment. Landsc. Urban Plan. 168, 48–60 (2017)

    Article  Google Scholar 

  32. Shen, L.Y., Ochoa, J.J., Shah, M.N., Zhang, X.: The application of urban sustainability indicators–a comparison between various practices. Habitat Int. 35(1), 17–29 (2011)

    Article  Google Scholar 

  33. La Rosa, D.: Accessibility to greenspaces: GIS based indicators for sustainable planning in a dense urban context. Ecol. Ind. 42, 122–134 (2014)

    Article  Google Scholar 

  34. Chrysoulakis, N., et al.: A conceptual list of indicators for urban planning and management based on earth observation. ISPRS Int. J. Geo Inf. 3(3), 980–1002 (2014)

    Article  Google Scholar 

  35. Bryant, M.M.: Urban landscape conservation and the role of ecological greenways at local and metropolitan scales. Landsc. Urban Plan. 76(1–4), 23–44 (2006)

    Article  Google Scholar 

  36. Elshater, A.: Widen the scale of urban design to the level of city planning: argument beyond a case of two cities. UPLanD-J. Urban Plann. Landscape Environ. Des. 2(2), 207–221 (2017)

    Google Scholar 

  37. Hay, G.J., Castilla, G.: Geographic object-based image analysis (GEOBIA): a new name for a new discipline. In: Blaschke, T., Lang, S., Hay, G.J. (eds.) Object-Based Image Analysis, pp. 75–89. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77058-9_4

  38. Šiljeg, S., Marić, I., Nikolić, G., Šiljeg, A.: Accessibility analysis of urban green spaces in the settlement of Zadar in Croatia. Šumarski list 142(9–10), 487–496 (2018)

    Google Scholar 

  39. Ye, B., Tian, S., Ge, J., Sun, Y.: Assessment of WorldView-3 data for lithological mapping. Remote Sens. 9(11), 1132 (2017)

    Article  Google Scholar 

  40. Maxar Technologies: Stereo Imagery datasheet (2019). https://www.digitalglobe.com/resources. Accessed 03 Dec 2020

  41. Bhakti, T., et al.: Combining land cover, animal behavior, and master plan regulations to assess landscape permeability for birds. Landsc. Urban Plan. 214, 104171 (2021)

    Article  Google Scholar 

  42. Zhan, Q., Shi, W., Xiao, Y.: Quantitative analysis of shadow effects in high-resolution images of urban areas. In: International Archives of Photogrammetry and Remote Sensing, vol. 36, no. 8/W27 (2005)

    Google Scholar 

  43. Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., Zhang, S.: Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors 18(11), 3717 (2018)

    Article  Google Scholar 

  44. Foody, G.M.: Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ. 239, 111630 (2020). https://doi.org/10.1016/j.rse.2019.111630

  45. Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. Wiley (2013)

    Google Scholar 

  46. Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q.: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery 115(5), 1145–1161 (2011). https://doi.org/10.1016/j.rse.2010.12.017

  47. Klempić, S.: Razvoj stambenih naselja Splita nakon Drugog svjetskog rata. Hrvatski geografski glasnik 66(2), 95–119 (2004)

    Google Scholar 

  48. Kranjčić, N., Medak, D., Župan, R., Rezo, M.: Machine learning methods for classification of the green infrastructure in city areas. ISPRS Int. J. Geo Inf. 8(10), 463 (2019)

    Article  Google Scholar 

  49. Chen, W., Li, X., Wang, L.: Fine land cover classification in an open pit mining area using optimized support vector machine and WorldView-3 imagery. Remote Sens. 12(1), 82 (2019)

    Article  Google Scholar 

  50. Hiscock, O.H., Back, Y., Kleidorfer, M., Urich, C.: A GIS-based land cover classification approach suitable for fine-scale urban water management. Water Resour. Manage 35(4), 1339–1352 (2021)

    Article  Google Scholar 

  51. Benarchid, O., Raissouni, N.: Mean-shift segmentation parameters estimator (MSPE): a new tool for very high spatial resolution satellite images. In: 2014 International Conference on Multimedia Computing and Systems (ICMCS), pp. 357–361. IEEE, April 2014

    Google Scholar 

  52. Choi, J., Park, H., Seo, D.: Pansharpening using guided filtering to improve the spatial clarity of VHR satellite imagery. Remote Sens. 11(6), 633 (2019)

    Article  Google Scholar 

  53. Rwanga, S.S., Ndambuki, J.M.: Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int. J. Geosci. 8(04), 611 (2017)

    Article  Google Scholar 

  54. Nagendra, H.: Opposite trends in response for the Shannon and Simpson indices of landscape diversity 22(2), 0–186 (2002). https://doi.org/10.1016/s0143-6228(02)00002-4

  55. Milošević, R., Šiljeg, S., Marić, I.: Derivation of urban planning indicators (UPIs) using Worldview-3 imagery and GEOBIA method for split settlement, Croatia. In: Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management, pp. 267–273 (2021). ISBN 978-989-758-503-6, ISSN 2184-500X. https://doi.org/10.5220/0010465102670273

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Acknowledgments

This work has been supported by INTERREG Italy- Croatia PEPSEA (Protecting the Enclosed Parts of the Sea in Adriatic from pollution) and the Croatian Science Foundation under the project UIP-2017-05-2694.

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Correspondence to Silvija Šiljeg or Ivan Marić .

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Milošević, R., Šiljeg, S., Marić, I. (2023). WorldView-3 Imagery and GEOBIA Method for the Urban Land Use Pattern Analysis: Case Study City of Split, Croatia. In: Grueau, C., Laurini, R., Ragia, L. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM GISTAM 2021 2022. Communications in Computer and Information Science, vol 1908. Springer, Cham. https://doi.org/10.1007/978-3-031-44112-7_4

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