MODELING OF URBAN GREEN SPACES BASED ON MOBILE LASER SCANNING DATA

DOI: 10.24057/2414-9179-2018-2-24-5-17

View or download the article (Rus)

About the Authors

Anatoly V. Pogorelov

Kuban State University,
Stavropolskaya str., 149, 350040, Krasnodar, Russia,
E-mail: pogorelov_av@bk.ru

Vladimir A. Brusilo

Company “Aerogeomatica”,
Frunze str., 350063, Krasnodar, Russia,
E-mail: brusilo.va@aerogeomatica.ru

Nikolai V. Granik

Kuban State University,
Stavropolskaya str., 149, 350040, Krasnodar, Russia,

Abstract

Modern technologies of inventory of green plantations in the city on the basis of remote sensing and mobile laser scanning are considered. The city green fund is the complex economy demanding new approaches, methods of management and technologies of inventory in modern conditions. In the city of Krasnodar (an area of more than 850 km²), amid an unprecedentedly high rate of urbanization, there is a marked change in the structure and composition of green spaces. A necessary condition for the content, protection and reproduction of urban green spaces is to provide information about the green fund. In practice, data are required about the objects of gardening with an accuracy of an individual tree in the park, square, etc.

We solve the problem of creation of information system of green spaces of the municipality on the basis of modern high technology. The technology of mobile laser scanning of urban landscaping objects — parks, squares, gardens, lawns, etc. has been developed. The accuracy of the survey is 3–5 cm, the scanning range is up to 190 m. For the first time in Krasnodar the mobile scanning of green plantations has been done. Mobile laser scanning data are represented by clouds of laser reflection points for each landscaping object. The main result of the work is a geodatabase, including high-precision vector models of municipal landscaping objects (with permission to an individual tree), their attributive description necessary for urban landscaping services. Vector data consist of the following layers: boundaries of the planting site, lawn, trees, old tree wells, young tree wells, shrubs, shrubbery wells, flower beds, mobile objects, other objects, decorative stuff. The received attributive information allows to estimate the costs for the maintenance of each object and the routine works. An information basis for quantitative assessments of the influence of green plantations on the urban climate has been created.

Keywords

urban green spaces, inventory, mobile laser scanning, geodatabase

References

  1. Alyoshin R., Spivak I., Davydenko E., Kulikov V. Development and introduction of GIS of registered green plantations. SAPR i grafika. 2014. No 3. P. 18–20 (in Russian).
  2. Baymaganbetova G.A., Golubeva Ye.I. Satellite images for mapping and monitoring the state of the green framework in Astana city. InterCarto. InterGIS 22. Proceedings of the International conference “InterCarto. InterGIS”. T. 1. M.: Izdatel’skiy dom “Nauchnaya biblioteka”, 2016. P. 370–379. DOI: 10.24057/2414-9179-2016-1-22-370-380 (in Russian).
  3. Gruzdev G.S., Dorozhkina L.A., Petrichenko S.A. Protection of green spaces in cities: a guide. M., 1990. 544 p. (in Russian).
  4. Jwan Al-doski, Shattri. B Mansor, Helmi Zulhaidi Mohd Shafri. NDVI differencing and post-classification to detect vegetation changes in Halabja city, Iraq. Journal of Applied Geology and Geophysics (IOSR-JAGG). 2013. V. 1, No 2. P. 1–10.
  5. Kavajyas F., Ramos Yu., Boje A. Inventory of urban green spaces and their monitoring using WorldView-2 data. Geomatica. 2011. No 3. P. 67–73 (in Russian).
  6. Kryuchkov A.N. Monitoring of the state of urban green spaces as part of effective management of green economy of Tolyatti sity. Izvestiya Samarskogo nauchnogo centra RAN. 2015. T. 11 (27), No 1(6). P. 1170–1173 (in Russian).
  7. Kulakova S.A. Assessment of the state of green spaces of the city. Vestnik PGU. 2012. No 4(23). P. 59–66 (in Russian).
  8. Lillesand T.M., Kiefer R.W., Chipman J.W. Remote Sensing and Image Interpretation. Fifth Edition. New Jersey: John Wiley and Sons, 2004. 804 p.
  9. Martin Mwirigi M’Ikiugu, Isami Kinoshita, Yoritaka Tashiro. Urban Green Space Analysis and Identification of its Potential Expansion Areas. Procedia—Social and Behavioral Sciences. 2012. V. 35. P. 449–458.
  10. Meera Gandhi G., Parthiban S., Nagaraj Thummalu, Christy A. NDVI: vegetation change detection using remote sensing and GIS—A case study of vellore district. Procedia Computer Science. 2015. V. 57. P. 1199–1210.
  11. Mohd Hisyam Rasidia, Nurzuliza Jamirsahb, Ismail Saidc. Urban Green Space Design Affects Urban Residents’ Social Interaction. Procedia—Social and Behavioral Sciences. 2012. V. 68. P. 464–480.
  12. Morozova G.Yu. Monitoring of urban environment: the structure of plant populations. Izvestiya Samarskogo nauchnogo centra RAN. 2009. T. 11 (27), No 1(6). P. 1170–1173 (in Russian).
  13. Morozova G.Yu., Gluhov V.A., Baburin A.A. Geoinformation system «Green plantings of the city of Khabarovsk». Izvestiya Samarskogo nauchnogo centra RAN. 2011. V. 13, No 1(6). P. 1367–1370 (in Russian).
  14. Mougiakou Eleni, Photis Yorgos N. Urban green space network evaluation and planning: optimizing accessibility based on connectivity and raster GIS analysis. European Journal of Geography. 2014. V. 5. November. P. 19–46.
  15. Pogorelov A.V., Lipilin D.A. Green spaces of the city of Krasnodar. Assessment and longterm changes. Vestnik PNIPU. Applied ecology. Urbanistics. 2017. No 3 (27). P. 192–205. DOI: 10.15593/2409-5125/2017.03.15 (in Russian).
  16. Pogorelov A.V., Lipilin D.A., Lubencova A.A. Assessment of long-term changes in the greenery of the city of Krasnodar according to satellite images. Regional Geographical Research. Sbornik nauchnyh trudov. Krasnodar. 2017. V. 1(11). P. 119–137 (in Russian).

For citation: Pogorelov A.V., Brusilo V.A., Granik N.V. MODELING OF URBAN GREEN SPACES BASED ON MOBILE LASER SCANNING DATA. Proceedings of the International conference “InterCarto. InterGIS”. 2018;24(2):5–17 DOI: 10.24057/2414-9179-2018-2-24-5-17 (in Russian)