A Framework for the Long-term Monitoring of Urban Green Volume Based on Multi-temporal and Multi-sensoral Remote Sensing Data

  • Annett FrickEmail author
  • Steffen Tervooren


Green urban infrastructure is of key importance for many aspects of urban life and urban planning. Valid and comprehensive databases with very high spatial and temporal resolution are needed to monitor changes and to detect negative trends. This paper presents an approach to assess urban indicators such as green volume and soil sealing with very high accuracy and based on a wide range of different sensors (aerial stereo images, QuickBird, WorldView 2 and 3, Sentinel 2, HRSC, LIDAR). A framework using regression tree methods was developed and successfully applied in a case study (the city of Potsdam, Germany) resulting in a long time series dating back 25 years. The methodology offers the opportunity to analyze urban development in detail and to understand the functional relationships of urban planning processes. Demands for effective climate change adaptation, especially in terms of reducing heat stress, can thus be better defined.


Urban green volume Remote sensing Monitoring Stereo matching 



The authors thank the city of Potsdam for financing the study.

Compliance with Ethical Standards

We comply with ethical standards.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

Our research did not involve human participants.

Informed Consent

Our research did not involve human participants.


  1. Audebert N, Boulch A, Randrianarivo H, Le Saux B, Ferecatu M, Lefèvre S, Marlet R (2017) Deep learning for urban remote sensing, 2017 Joint Urban Remote Sensing Event (JURSE), Dubai. pp 1–4.
  2. 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. CrossRefGoogle Scholar
  3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. CrossRefGoogle Scholar
  4. Casalegno S, Anderson K, Hancock S, Gaston KJ (2017) Improving models of urban greenspace: from vegetation surface cover to volumetric survey, using waveform laser scanning. Methods Ecol Evol 8:1443–1452. CrossRefGoogle Scholar
  5. EEA European Environment Agency (2015) Green infrastructure: better living through nature-based solutions. Accessed 18 Oct 2018
  6. Eichberger S, Sulzer W (2004) Urban development of Graz - a time-series analysis with historical aerial photographs. In: 1st Göttingen GIS & Remote Sensing Days - environmental studies - Göttingen, Göttinger Geographische Abhandlungen, vol 113. pp 63–70Google Scholar
  7. Fokaides PA, Kylili A, Nicolaou L, Ioannou B (2016) The effect of soil sealing on the urban heat island phenomenon. Indoor Built Environ 25(7):1136–1147. CrossRefGoogle Scholar
  8. Frick A, Coenradie B, Kenneweg H (2007) Environmental monitoring and urban development: application of modern remote sensing methods. In: Kenneweg H, Kröger T (ed) 2nd International Congress on Environmental Planning and Managment. Landschaftsentwicklung und Umweltforschung. Band S20. BerlinGoogle Scholar
  9. Gerstengarbe FW, Werner PC, Krellig H (2014) Climate development in Potsdam between 1761 and 2050. In: Historic gardens and climate change. Edition Leipzig, Leipzig, pp 54–59Google Scholar
  10. Griffiths P, Hostert P, Gruebner O, Van der Linden S (2010) Mapping megacity growth with multi-sensor data. Remote Sens Environ 114:426–439. CrossRefGoogle Scholar
  11. Haala N, Rothermel M (2012) Dense multi-stereo matching for high quality digital elevation models. PFG-J Photogramm Rem 2012(4):331–343. CrossRefGoogle Scholar
  12. Haas J, Ban Y (2017) Sentinel-1A SAR and Sentinel-2A MSI data fusion for urban ecosystem service mapping. Remote Sens Appl: Soc Environ 8:41–53. CrossRefGoogle Scholar
  13. Haas J, Ban Y (2018) Urban land cover and ecosystem service changes based on Sentinel-2A MSI and Landsat TM data. IEEE J Sel Top Appl Earth Obs Remote Sens 11(2):485–497. CrossRefGoogle Scholar
  14. Haralick R, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  15. Hecht R, Meinel G, Buchroithner MF (2008) Estimation of urban green volume based on single-pulse LiDAR data. IEEE Trans Geosci Remote Sens 46(11):3832–3840. CrossRefGoogle Scholar
  16. Hirschmüller H (2008) Stereo processing by semi-global matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341. CrossRefGoogle Scholar
  17. Hodgson ME, Jensen JR, Tullis JA, Riordan KD, Archer CM (2003) Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness. Photogramm Eng Remote Sens 69(9):973–980CrossRefGoogle Scholar
  18. Hofmann P, Strobl J, Nazarkulova A (2011) Mapping green spaces in Bishkek—how reliable can spatial analysis be? Remote Sens 3(6):1088–1103. CrossRefGoogle Scholar
  19. Huang Y, Yu B, Zhou J et al (2013) Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution remote sensing images. Front Earth Sci 7(1):43–54. CrossRefGoogle Scholar
  20. Kopecká M, Szatmári D, Rosina K (2017) Analysis of urban green spaces based on Sentinel-2A: case studies from Slovakia. Land 6(2):25. CrossRefGoogle Scholar
  21. Krüger T, Hecht R, Herbrich J, Behnisch M, Oczipka M (2018) Investigating the suitability of Sentinel-2 data to derive the urban vegetation structure. Proc SPIE 10793, Remote Sensing Technologies and Applications in Urban Environments III.
  22. Labib SM, Harris A (2018) The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. Eur J Remote Sens 51(1):231–240. CrossRefGoogle Scholar
  23. Lehner A, Naeimi V, Steinnocher K (2017) Sentinel-1 for urban areas - comparison between automatically derived settlement layers from Sentinel-1 data and Copernicus high resolution information layers. In: Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1. GISTAM, pp 43–49. ISBN 978-989-758-252-3.
  24. Maher BA, Ahmed IA, Davison B, Karloukovski V, Clarke R (2013) Impact of roadside tree lines on indoor concentrations of traffic-derived particulate matter. Environ Sci Technol 47(23):13737–13744CrossRefGoogle Scholar
  25. Matikainen L, Karila K (2011) Segment-based land cover mapping of a suburban area—comparison of high-resolution remotely sensed datasets using classification trees and test field points. Remote Sens 3(8):1777–1804CrossRefGoogle Scholar
  26. Meinel G, Netzband M (1997) Bestimmung der Oberflächenversiegelung von Stadtgebieten auf Grundlage von ATM Scannerdaten. PFG-J Photogramm Rem 2:93–102Google Scholar
  27. Pesaresi M, Julea AM, Syrris V (2016) A new method for earth observation data analytics based on symbolic machine learning. Remote Sens 8(5):399. CrossRefGoogle Scholar
  28. Pesaresi M, Corbane C, Julea A, Florczyk AJ, Syrris V, Soille P (2018) Assessment of the added-value of Sentinel-2 for detecting built-up areas. Remote Sens 8(4):299CrossRefGoogle Scholar
  29. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San MateoGoogle Scholar
  30. Rouse J, Haas R, Schell J, Deering D (1973) Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium. NASA, pp 309–317Google Scholar
  31. Roy S, Byrne J, Pickering C (2012) A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For Urban Green 11(4):351–363CrossRefGoogle Scholar
  32. Schulze H-D, Pohl W, Großmann M (1984) Gutachten: Werte für die Landschafts- und Bauleitplanung: Bodenfunktionszahl, Grünvolumenzahl. – Schriftenreihe der Behörde für Bezirksangelegenheiten. Naturschutz und Umweltgestaltung Freie Hansestadt Hamburg. 9. 1. Aufl. Christians. HamburgGoogle Scholar
  33. Susca T, Gaffin SR, Dell’Osso GR (2011) Positive effects of vegetation: urban heat island and green roofs. Environ Pollut 159(8–9):2119–2126. CrossRefGoogle Scholar
  34. Tervooren S (2015) Potenziale von Grünvolumen und Entsiegelung zur Klimaanpassung am Beispiel der Landeshauptstadt Potsdam. AGIT-Journal für angewandte Geoinformatik. Wichmann, Berlin, pp 258–267Google Scholar
  35. Tervooren S, Frick A (2010) Bodenversiegelung, Grünvolumen, Biotopwertigkeit – Praktische Erfahrungen des Umweltmonitorings in Potsdam. In: Meinel G, Schumacher U (eds) Flächennutzungsmonitoring II. Konzepte – Indikatoren – Statistik. IÖR Schriften, vol 52. Rhombos, Berlin, pp 155–167Google Scholar
  36. UNDP. 2019. United Nations development programme, sustainable development goals website. Accessed 24 Jan 2019

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LUP GmbHPotsdamGermany
  2. 2.State Capital Potsdam, Environment and NaturePotsdamGermany

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