Advertisement

Land consumption monitoring: an innovative method integrating SAR and optical data

  • Sara Mastrorosa
  • Michele Crosetto
  • Luca Congedo
  • Michele Munafò
Article
First Online:
Received:
Accepted:

Abstract

In this paper, the use of synthetic aperture radar (SAR) for the monitoring of land consumption is analyzed. The paper presents an automatic procedure that integrates SAR and optical data, which can be effectively used to generate land consumption maps or update existing maps. The main input of the procedure is a series of SAR amplitude images acquired over a given geographical area and observation period. The main assumption of the procedure is that land consumption is associated with an increase of the SAR amplitude values. Such an increase is detected in the SAR amplitude time series using an automatic Bayesian algorithm. The results based on the SAR amplitude are then filtered using an NDVI map derived from optical imagery. The effectiveness of the proposed procedure is illustrated using SAR data from the Sentinel-1 and TerraSAR-X sensors, and optical data from the Sentinel-2 sensor.

Keywords

Multi-temporal series SAR images Step detection Time series Land use/land cover 
This is a preview of subscription content, log in to check access.

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Angelis, C. F., Freitas, C. C., Valeriano, D. M., & Dutra, L. V. (2002). Multitemporal analysis of land use/land cover JERS-1 backscatter in the Brazilian tropical rainforest. International Journal of Remote Sensing, 23(7), 1231–1240.CrossRefGoogle Scholar
  2. As-syakur, A. R., Wayan Sandi Adnyana, I., Wayan Arthana, I., & Wayan Nuarsa, I. (2012). Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing, 4, 2957–2970.CrossRefGoogle Scholar
  3. Brook R. M. and D ávila J. (2000). The peri-urban interface: a tale of two cities. Gwynedd, Wales: School of Agricultural and Forest Sciences, University of Wales and Development Planning.Google Scholar
  4. Campbell J.B., Wynne R.H. (2011). Introduction to remote sensing. Guilford Press, 667pp.Google Scholar
  5. Cardona, O.D., M.K. van Aalst, J. Birkmann, M. Fordham, G. McGregor, R. Perez, R.S. Pulwarty, E.L.F. Schipper, and B.T. Sinh (2012). Determinants of risk: exposure and vulnerability. In: Managing the risks of extreme events and disasters to advance climate change adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 65–108.Google Scholar
  6. Congedo L., Marinosci I., Riitano N., Strollo A., De Fioravante P., Munafò M. (2017). Monitoring of land consumption: an analysis of loss of natural and agricultural areas in Italy. Annali di Botanica, 7: 1–9.Google Scholar
  7. CRCS. (2012). Rapporto 2012. Centro di Ricerca sui Consumi di Suolo. Milano: INU Edizioni.Google Scholar
  8. EEA, European Environment Agency (2011). Urban soil sealing in Europe. https://www.eea.europa.eu/articles/urban-soil-sealing-in-europe.
  9. Estoquea, R. C., Murayamaa, Y., & Akiyamab, C. M. (2015). Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes. Geocarto International, 30(10), 1113–1129.CrossRefGoogle Scholar
  10. European Commission (2011). Our life insurance, our natural capital: an EU biodiversity strategy to 2020. COM (2011) 244 final. Brussels.Google Scholar
  11. European Commission (2011b). Roadmap to a resource efficient Europe. COM (2011)571 final. Brussels.Google Scholar
  12. European Commission (2012). Guidelines on best practice to limit, mitigate or compensate soil sealing. SWD (2012) 101. Brussels.Google Scholar
  13. Fan, F., Weng, Q., & Wang, Y. (2007). Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7, 1323–1342.CrossRefGoogle Scholar
  14. Gómez, C., White, J., & Wulder, M. (2016). Optical remotely sensed time series data for land cover classification: a review. ISPR Journal of Photogrammetry and Remote Sensing, 116(June 2016), 55–72.CrossRefGoogle Scholar
  15. Gomez-Chova, L., Fernández-Prieto, D., Calpe, J., Soria, E., Vila, J., & Camps-Valls, G. (2006). Urban monitoring using multi-temporal SAR and multi-spectral data. Pattern Recognition Letters, 27(4), 234–243.CrossRefGoogle Scholar
  16. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.CrossRefGoogle Scholar
  17. Huber S., Prokop G., Arrouays D., Banko G. et al. (2008). Environmental assessment of soil for monitoring. Volume I indicators and criteria. JRC, Office for the Official Publications of the European Communities.Google Scholar
  18. Irwin, E. G., & Bockstael, N. E. (2004). Land use externalities, open space preservation, and urban sprawl. Regional Science and Urban Economics, 34(6), 705–725.CrossRefGoogle Scholar
  19. ISPRA, Istituto Superiore per la Protezione e Ricerca Ambientale (2017). Consumo di suolo, dinamiche territoriali e servizi ecosistemici. Edizione 2017. (In Italian).Google Scholar
  20. Lam N. SN. (2008). Methodologies for mapping land cover/land use and its change. Advances in land remote sensing. Springer, pp 341–367.Google Scholar
  21. Lindblad T., Kinser J.M. (2005). Image processing using pulse-coupled neural networks, Second, Revised Version, 169pp.Google Scholar
  22. Maes, J., Egoha, B., Willemen, L., et al. (2012). Mapping ecosystem services for policy support and decision making in the European Union. Ecosystem Services, 1, 31–39.CrossRefGoogle Scholar
  23. Moran, M. S., Hymer, D. C., Qi, J., & Kerr, Y. (2002). Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions. Remote Sensing of Environment, 79(2), 243–252.CrossRefGoogle Scholar
  24. Munafò M., Riitano N. (2016). Cause ed effetti del consumo di suolo. Consumo di suolo, dinamiche territoriali e servizi ecosistemici. Ed. 2016, pp. 4–6 (in Italian).Google Scholar
  25. Munafò M., Marinosci I., Congedo L., La Mantia C., Luti T., Marchetti M., Raudner A., Riitano N., Sallustio L., Strollo A. (2016). Monitoraggio del territorio e del consumo di suolo in Italia. Consumo di suolo, dinamiche territoriali e servizi ecosistemici. Ed. 2016, pp. 23–26 (in Italian).Google Scholar
  26. O’Ruanaidh J.J.K., Fitzgerald W.J. (1996). Numerical Bayesian methods applied to signal processing. Springer, 244pp.Google Scholar
  27. Patel, N., Angiuli, E., Gamba, P., Gaughan, A., Stevens, F., Tatem, A., & Trianni, G. (2015). Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 35(Part B), 199–208.CrossRefGoogle Scholar
  28. Poursanidis, D., & Chrysoulakis, N. (2017). Remote sensing, natural hazards and the contribution of ESA Sentinels missions. Remote Sensing Applications: Society and Environment, Volume, 6(April 2017), 25–38.CrossRefGoogle Scholar
  29. Satalino G., Balenzano M.F., Belmonte A. and Impedovo D. (2011). Land cover classification by using multi-temporal COSMO-SkyMed data. 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).Google Scholar
  30. Scalenghe R., Ajmone Marsan F. (2009). The anthropogenic sealing of soil in urban areas. Landscape and urban planning, Volume 90, Issues 1-2, 15 March 2009, Pages 1–10.Google Scholar
  31. Sun, Z., Wang, C., Guo, H., & Shang, R. (2017). A modified normalized difference impervious surface index (MNDISI) for automatic urban mapping from Landsat imagery. Remote Sensing, 9, 942.CrossRefGoogle Scholar
  32. United Nations (2014). World urbanization prospects, the 2014 revision. Final report, ST/ESA/SER.A/366, New York.Google Scholar
  33. United Nations (2015). Transforming our world: the 2030 agenda for sustainable development. A/RES/70/1, UN General Assembly, 21 October, 2015.Google Scholar
  34. Yousif O., Ban Y. (2015). Object-based urban change detection using high resolution SAR images. 2015 Joint Urban Remote Sensing Event (JURSE).Google Scholar
  35. Zhang X., Tang X., Gao X., Zhao H. (2018). Multitemporal soil moisture retrieval over bare agricultural areas by means of alpha model with multisensor SAR data. Advances in Meteorology, Volume 2018, Article ID 7914581, 17 pages.Google Scholar
  36. Zhu, Z., Woodcock, C. E., Rogan, J., & Kellndorfer, J. (2012). Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sensing of Environment, 117, 72–82.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Civil and Environmental EngineerRomeItaly
  2. 2.Geomatics Division Head, Remote Sensing DepartmentCentre Tecnològic de Telecomunicacions de Catalunya (CTTC)BarcelonaSpain
  3. 3.ISPRA - Italian Institute for Environmental Protection and ResearchRomeItaly

Personalised recommendations

Cite article

Buy options