Experience in the determining the building area using satellite images for the purposes of meteorological modeling (case of Moscow city)

DOI: 10.35595/2414-9179-2020-2-26-298-312

View or download the article (Rus)

About the Authors

Mikhail Yu. Grischenko

Moscow State University named after M.V. Lomonosov, Faculty of Geography,
Leninskiye Gory, 1, 119991, Moscow, Russia,
E-mail: m.gri@geogr.msu.ru

Evgeny Yu. Sarychev

Moscow State University named after M.V. Lomonosov, Faculty of Geography,
Leninskiye Gory, 1, 119991, Moscow, Russia,
E-mail: evgen.sarychev.1@gmail.com

Mikhail I. Varentsov

Moscow State University named after M.V. Lomonosov, Research Computing Center, Faculty of Geography,
Leninskiye Gory, 1, 119991, Moscow, Russia,
E-mail: mvar91@gmail.com

Timofey E. Samsonov

Moscow State University named after M.V. Lomonosov, Faculty of Geography,
Leninskiye Gory, 1, 119991, Moscow, Russia,
E-mail: tsamsonov@geogr.msu.ru

Abstract

Detailed weather and climate modeling for urban areas is in demand in various scientific and applied tasks, starting from a numerical weather forecast and ending with an assessment of the bioclimatic conditions of the city and long-term urban planning. The application of modern meteorological models for urban areas requires the determination of a set of parameters characterizing the urban environment and urban canopy features. One of these parameters is the area fraction, occupied by buildings.

In this paper, we propose a universal method for determining building area fraction based on the interpretation of high-resolution satellite images from the Sentinel-2 satellites. The methodology was tested on the example of the territory of the city of Moscow, characterized by a variety of forms of urban development. The calculation of the building area fraction was performed for the cells of the computational grid of the COSMO mesoscale numerical meteorological model with 1 km spacing. To verify the developed method, we used an alternative estimate of the building area fraction based on the crowdsourcing cartographic data OpenStreetMap. The data on the building area fraction derived from the satellite images and from OpenStreetMap data have shown a good mutual agreement, which confirms the promise of using the proposed methodology. On the example of territories where the two methods show significant differences, their typical causes are identified, namely the lack of information about buildings in the OpenStreetMap database, or the masking of buildings by trees, which does not allow them to be revealed from satellite images.

Keywords

building area, urban environment parameters, urban climate, Sentinel-2, COSMO

References

  1. Baklanov A., Grimmond C.S.B., Carlson D., Terblanche D., Tang X., Bouchet V., Lee B., Langendijk G., Kolli R.K., Hovsepyan A. From urban meteorology, climate and environment research to integrated city services. Urban Climate, 2018. V. 23. P. 330–341.
  2. Bechtel B., Alexander P.J., Böhner J., Ching J., Conrad O. Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS International Journal of Geo-Information, 2015. V. 4. P. 199–219.
  3. Brousse O., Martilli A., Foley M., Mills G., Bechtel B. WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid. Urban Climate, 2016. V. 17. P. 116–134.
  4. Chen L., Ng E. Quantitative urban climate mapping based on a geographical database: A simulation approach using Hong Kong as a case study. International Journal of Applied Earth Observation and Geoinformation, 2011. V. 13. No 4. P. 586–594.
  5. Ching J., Aliaga D., Mills G., Masson V., See L., Neophytou M., Middel A., Baklanov A., Ren C., Ng E., Fung J., Wong M., Huang Y., Martilli A., Brousse O., Stewart I., Zhang X., Shehata A., Miao S., Wang X., Wang W., Yamagata Y., Duarte D., Li Y., Feddema J., Bechtel B., Hidalgo J., Roustan Y., Kim Y., Simon H., Kropp T., Bruse M., Lindberg F., Grimmond S., Demuzure M., Chen F., Li C., Gonzales-Cruz J., Bornstein B., He Q., Tzu-Ping, Hanna A., Erell E., Tapper N., Mall R.K., Niyogi D. Pathway using WUDAPT’s Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling. Urban Climate, 2019. V. 28. P. 100459.
  6. Ching J., Mills G., Bechtel B., See L., Feddema J., Wang X., Ren C., Brousse O., Martilli A., Neophytou M., Mouzourides P., Stewart I., Hanna A., Ng E., Foley M., Alexander P., Aliaga D., Niyogi D., Shreevastava A., Bhalachandran P., Masson V., Hidalgo J., Fung J., Andrade M., Baklanov A., Dai W., Milcinski G., Demuzere M., Brunsell N., Pesaresi M., Miao S., Mu Q., Chen F., Theeuwes N. WUDAPT: An urban weather, climate, and environmental modeling infrastructure for the anthropocene. Bulletin of the American Meteorological Society, 2018. V. 99. No 9. P. 1907–1924.
  7. Climate of Moscow (Climate features of the big city). Leningrad: Gidrometeoizdat, 1969. 323 p. (in Russian).
  8. Climate of Moscow under global warming. Moscow: Moscow University Press, 2017. 288 p. (in Russian).
  9. Gál T., Unger J. A new software tool for SVF calculations using building and tree-crown databases. Urban Climate, 2014. V. 10. No 3. P. 594–606.
  10. Garuma G.F. Review of urban surface parameterizations for numerical climate models. Urban Climate, 2018. V. 24. P. 830–851.
  11. Grishchenko M.Y., Ermilova Y.V. Mapping the development of the largest cities of the Russian Arctic by satellite images of different spectral ranges. Geodesy and Cartography, 2018. V. 79. No 3. P. 23–34 (in Russian).
  12. Hammerberg K., Brousse O., Martilli A., Mahdavi A. Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria. International Journal of Climatology, 2018. V. 38. P. e1241–e1257.
  13. Kasimov N.S., Nikiforova E.M., Kosheleva N.E., Khaibrakhmanov T.S. Geoinformation landscape-geochemical mapping of urban areas (by the example of EAO of Moscow). Geoinformatika, 2013. No 1. P. 28–32 (in Russian).
  14. Kislov A.V., Konstantinov P.I. Detailed spatial modeling of temperature in Moscow. Russian Meteorology and Hydrology, 2011. V. 36. No 5. P. 25–32 (in Russian).
  15. Kuznetsova I.N., Brusova N.E., Nakhaev M.I. Moscow urban heat island: Detection, boundaries, and variability. Russian Meteorology and Hydrology, 2017. V. 42. No 5. P. 305–313 (in Russian).
  16. Landsberg H.E. The urban climate. International Geophysics Series. V. 28. New York, London: Academic Press, 1981. 275 p.
  17. Lindberg F. Modelling the urban climate using a local governmental geo-database. Meteorological Applications, 2007. V. 273. P. 263–273.
  18. Lokoshchenko M.A. Urban ‘heat island’ in Moscow. Urban Climate, 2014. V. 10. P. 550–562.
  19. Lokoshchenko M.A. Urban heat island and urban dry island in Moscow and their centennial changes. Journal of Applied Meteorology and Climatology, 2017. V. 56. No 10. P. 2729–2745.
  20. Lu D., Hetrick S., Moran E. Land cover cassification in a complex urban-rural landscape with QuickBird imagery. Photogrammetric Engineering & Remote Sensing, 2010. V. 76. No 10. P. 1159–1168.
  21. Masson V., Heldens W., Bocher E., Bonhomme M., Bucher B., Burmeister C., Munck C. de, Esch T., Hidalgo J., Kanani-Sühring F., Kwok Y.T., Lemonsu A., Lévy J.P., Maronga B., Pavlik D., Petit G., See L., Schoetter R., Tornay N., Votsis A., Zeidler J. City-descriptive input data for urban climate models: Model requirements, data sources and challenges. Urban Climate, 2020. V. 31. P. 100536.
  22. Myagkov M.S. Ecological consequences of mesoclimatic anomalies of the Moscow megacity. Ecology of Urban Areas, 2006. No 1. P. 49–61 (in Russian).
  23. Oke T.R., Mills G., Christen A., Voogt J.A. Urban climates. Cambridge: Cambridge University Press, 2017. 509 p.
  24. Orlanski L. A rational subdivision of scale for atmospheric processes. Bulletin of the American Meteorological Society, 1975. V. 56. P. 527–530.
  25. Peeters A., Etzion Y. Automated recognition of urban objects for morphological urban analysis. Computers, Environment and Urban Systems, 2012. V. 36. No 6. P. 573–582.
  26. Samsonov T.E., Konstantinov P.I., Varentsov M.I. Object-oriented approach to urban canyon analysis and its applications in meteorological modeling. Urban Climate, 2015. V. 13. P. 122–139.
  27. Samsonov T.E., Trigub K.S. Mapping of local climate zones of Moscow city. Geodesy and Cartography, 2018. V. 79. No 6. P. 20–31 (in Russian).
  28. Varentsov M.I., Samsonov T.E., Kislov A.V., Konstantinov P.I. Simulations of Moscow agglomeration heat island within framework of regional climate model COSMO-CLM. Herald of Moscow University. Series 5. Geography, 2017. No 6. P. 25–37 (in Russian).
  29. Varentsov M., Wouters H., Platonov V., Konstantinov P. Megacity-induced mesoclimatic effects in the lower atmosphere: A modeling study for multiple summers over Moscow, Russia. Atmosphere, 2018. V. 9. No 2. P. 50.
  30. Varentsov M.I., Grishchenko M.Y., Wouters H. Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling. Geography, Environment, Sustainability, 2019. V. 12. No 4. P. 74–95.

For citation: Grischenko M.Yu., Sarychev E.Yu., Varentsov M.I., Samsonov T.E. Experience in the determining the building area using satellite images for the purposes of meteorological modeling (case of Moscow city). InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: Moscow University Press, 2020. V. 26. Part 2. P. 298–312. DOI: 10.35595/2414-9179-2020-2-26-298-312 (in Russian)