Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31533
Urban Growth Analysis Using Multi-Temporal Satellite Images, Non-stationary Decomposition Methods and Stochastic Modeling

Authors: Ali Ben Abbes, ImedRiadh Farah, Vincent Barra


Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I1, I2, … In at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

Keywords: Multi-temporal satellite image, urban growth, Non-stationarity, stochastic modeling.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1032


[1] C. Sun, Z.-f. Wu, Z.-q. Lv, N. Yao, and J.-b. Wei, “Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data,” International Journal of Applied Earth Observation and Geoinformation, vol. 21, pp. 409-417, 2013.
[2] J. E. Patino and J. C. Duque, “A review of regional science applications of satellite remote sensing in urban settings,” Computers, Environment and Urban Systems, vol. 37, pp. 1–17, 2013.
[3] H. S. Moghadam and M. Helbich, “Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model,” Applied Geography, vol. 40, pp. 140-149, 2013.
[4] E. A. Wentz, S. Anderson, M. Fragkias, M. Netzband, V. Mesev, S. W. Myint, D. Quattrochi, A. Rahman, and K. C. Seto, “Supporting global environmental change research: A review of trends and knowledge gaps in urban remote sensing,” Remote Sensing, vol. 6, no. 5, pp. 3879–3905, 2014.
[5] A. Belal and F. Moghanm, “Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt,” The Egyptian Journal of Remote Sensing and Space Science, vol. 14, no. 2, pp. 73–79, 2011.
[6] H. Zhang, X. Jin, L. Wang, Y. Zhou, and B. Shu, “Multi-agent based modeling of spatiotemporal dynamical urban growth in developing countries: simulating future scenarios of lianyungang city, china,” Stochastic environmental research and risk assessment, vol. 29, no. 1, pp. 63–78, 2015.
[7] A. Achmad, S. Hasyim, B. Dahlan, and D. N. Aulia, “Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Bandaaceh, Indonesia,” Applied Geography, vol. 62, pp. 237–246, 2015.
[8] A. Rienow and R. Goetzke, “Supporting sleuth–enhancing a cellular automaton with support vector machines for urban growth modeling,” Computers, Environment and Urban Systems, vol. 49, pp. 66–81, 2015.
[9] B. C. Pijanowski, A. Tayyebi, J. Doucette, B. K. Pekin, D. Braun, and J. Plourde, “A big data urban growth simulation at a national scale: Configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment,” Environmental Modelling & Software, vol. 51, pp. 250–268, 2014.
[10] H. Essid, I. R. Farah, A. Sellami, and V. Barra, “Monitoring intra-urban changes with hidden Markov models using the spatial relationships,” International Journal on Graphics, Vision and Image Processing, vol. 12, no. 1, pp. 49–55, 2012.
[11] M. K. Jat, P. K. Garg, and D. Khare, “Monitoring and modelling of urban sprawl using remote sensing and GIS techniques,” International journal of Applied earth Observation and Geoinformation, vol. 10, no. 1, pp. 26–43, 2008.
[12] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, “Review article digital change detection methods in ecosystem monitoring: a review,” International journal of remote sensing, vol. 25, no. 9, pp. 1565–1596, 2004.
[13] J. D. Hamilton, “A new approach to the economic analysis of nonstationary time series and the business cycle,” Econometrica: Journal of the Econometric Society, pp. 357–384, 1989.
[14] S. Azzali and M. Menenti, “Mapping vegetation-soil-climate complexes in Southern Africa using temporal Fourier analysis of Noaaavhrrndvi data,” International Journal of Remote Sensing, vol. 21, no. 5, pp. 973-996, 2000.
[15] B. Mart´ınez and M. A. Gilabert, “Vegetation dynamics from NDVI time series analysis using the wavelet transform,” Remote Sensing of Environment, vol. 113, no. 9, pp. 1823–1842, 2009.
[16] Dhodhi, Muhammad K., Saghri, John A., Ahmad, Imtiaz, et al. D-isodata: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations. Journal of Parallel and Distributed Computing, 1999, vol. 59, no 2, p. 280-301.
[17] J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, “Detecting trend and seasonal changes in satellite image time series,” Remote sensing of Environment, vol. 114, no. 1, pp. 106–115, 2010.
[18] J. Hutchinson, A. Jacquin, S. Hutchinson, and J. Verbesselt, “Monitoring vegetation change and dynamics on us army training lands using satellite image time series analysis,” Journal of environmental management, vol. 150, pp. 355–366, 2015.
[19] S. Jamali, P. Jonsson, L. Eklundh, J. Ardö, and J. Seaquist, “Detecting changes in vegetation trends using time series segmentation,” Remote Sensing of Environment, vol. 156, pp. 182–195, 2015.
[20] A. B. Abbes, H. Essid, I. R. Farah, and V. Barra, “An adaptive multiplicative decomposition of non-stationary multi-temporal satellite images: Application to urban changes detection,” in Image Processing, Applications and Systems Conference (IPAS), 2014 First International. IEEE, 2014, pp. 1–7.
[21] A. Ben Abbes, H. Essid, I. R. Farah, and V. Barra, “Rare events detection in NDVI time-series using Jarque-Bera test,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015, pp. 338–341.
[22] M. Brandt, A. Verger, A. A. Diouf, F. Baret, and C. Samimi, “Local vegetation trends in the Sahel of Mali and Senegal using long time series Fapar satellite products and field measurement (1982–2010),” Remote Sensing, vol. 6, no. 3, pp. 2408–2434, 2014.