Identifying and monitoring urban growth and expansion changes using spectral unmixing of remote sensing imagery and geographic information systems techniques at the micro level, (A case study of Amol city)

Document Type : Original Article


1 Prof. of Department of Geography and Urban Planning, University of Mazandaran, Mazandaran, Iran

2 Ph.D. Student, Department of Geography and Urban Planning, University of Mazandaran, Mazandaran, Iran


Introduction: Land is a vital natural resource for human survival and the basis of all ecosystem services. However, land degradation through land use and land cover changes has become a serious global issue. Rural-urban migration in developing countries, along with the conversion of nearby villages into urban areas, has largely driven the rapid increase in urban settlements. Evidence shows that northern Iran has been experiencing rapid land use changes, which have endangered the region's stability over the last three decades. This article aims to identify and compare the growth and expansion changes of Amol city, located in Mazandaran province, over three decades from 1986 to 2016. Urban growth is defined by the absolute increase in the population living in cities, while urban sprawl is defined by changes in land cover over time.
Materials and Methods: To produce images of land cover changes in different years, Landsat time series image data (TM, ETM+, and OLI/TIRS sensors) were used. A linear unmixing model based on the extraction of end members was employed, identifying two classes of land: urban and non-urban. To verify the classification accuracy, high-resolution images from Google Earth and field observations using GPS were employed. Moran's spatial autocorrelation analysis tool was used to analyze the spatial distribution of land cover, and the trend analysis tool in ArcGIS software was employed to analyze the trend of spatial changes in urban land.
Results and Discussion: The findings show that the city's population doubling over these three decades has not only been accompanied by a 60% horizontal expansion into surrounding green and open lands but also increased building density and the number of floors. Validation of land cover classification, using the ambiguity error matrix, with an overall accuracy of over 90% and a kappa coefficient of over 80% for the obtained images, indicates high accuracy. Based on Moran's spatial autocorrelation analysis, the development of Amol City follows a cluster structure. Calculating the difference in land cover data over the 20 years from 1996 to 2016 revealed that 28 percent of the total city area, equivalent to 890 hectares, has undergone significant changes, including fundamental alterations in land use and land cover. The expansion of Amol City has generally occurred from the center to the periphery, with the least changes in the central parts and the most changes in the northern and eastern parts of the city.
Conclusion: Population growth and urban expansion not only lead to the destruction of natural resources and the environment but also exacerbate urban management problems in providing infrastructure and services. Therefore, the goal of planning should be to achieve optimal development that prevents increased migration from villages to cities. This can be achieved both at the macro level, by distributing urban roles complementarily and strengthening the role of villages, and at the local level, by emphasizing the balanced distribution of the city's population and endogenous growth.


Abebe, G., Getachew, D., & Ewunetu, A., 2021, Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia, SN Applied Sciences, 4(1), 1-15.
Alberti, M., Weeks, R., & Coe, S., 2004, Urban land-cover change analysis in central Puget Sound, Photogrammetric Engineering & Remote Sensing, 70(9), 1043-1052.
Belal, A. A., & Moghanm, F. S., 2011, Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt, The Egyptian Journal of Remote Sensing and Space Science, 14(2), 73-79.
Chamling, M. & Bera, Biswajit., 2020, Spatio-temporal Patterns of Land Use/Land Cover Change in the Bhutan–Bengal Foothill Region Between 1987 and 2019: Study Towards Geospatial Applications and Policy Making, Earth Systems and Environment. 4. 10.1007/s41748-020-00150-0.
Chen, Y., 2013, New approaches for calculating Moran’s index of spatial autocorrelation, PloS one, 8(7), e68336.
Cochran, W.G., 1977, Sampling Techniques, 3rd Edition, John Wiley & Sons, New York. ISBN: 978-0-471-16240-7
Congalton, R.G., 1991, A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing of Environment, 37, 35-46.
Das, S., & Angadi, D. P., 2022, Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study, GeoJournal, 87(3), 2101-2123.
Ewing, R., 1994, Causes, characteristics, and effects of sprawl: a literature review, Environmental and Urban Issues 21(2): pp. 1-15.
Foody, G. M., 2002, Status of land cover classification accuracy assessment, Remote sensing of environment, 80(1), 185-201.
Hassan, Z., Shabbir, R., Ahmad, S. S., Malik, A. H., Aziz, N., Butt, A., & Erum, S., 2016, Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan, SpringerPlus, 5(1), 1-11.
Iran Statistics Center, population and housing census of 1365, 1375, 1385 and 1395.
Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M. E., Hmaidi, A. E., Ouali, A. E., ... & Teodoro, A. C., 2018, Land use/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco, Environments, 5(12), 131.
Mostafa, E., Li, X., Sadek, M., & Dossou, J. F., 2021, Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt, Remote Sensing, 13(22), 4498.
Quintano, C., Fernández-Manso, A., Shimabukuro, Y. E., & Pereira, G., 2012, Spectral unmixing, International Journal of Remote Sensing, 33(17), 5307-5340.
Rahnama, M. R., & bolori, Z., 2022, Analysis of the growth effects of Amol city on the surrounding environment using remote sensing and geographic information system, Political Organizing of Space, 4 (4) :288-302.
Rwanga, S. S., & Ndambuki, J. M., 2017, Accuracy assessment of land use/land cover classification using remote sensing and GIS, International Journal of Geosciences, 8(04), 611.
Seifolddini, F., Ziari, K., Pourahmad, A, & Nikpour, A., 2011, Explanation of Dispersion and Compactness of Urban Form in Amol City, Considering Sustainable Urban Form, Human Geography Research, 44(80), 155-176.
Shao, Y., Lan, J., Zhang, Y., & Zou, J., 2018, Spectral unmixing of hyperspectral remote sensing imagery via preserving the intrinsic structure invariant, Sensors, 18(10), 3528.
Tobler, W. R., 1970, A computer movie simulating urban growth in the Detroit region, Economic geography, 46(sup1), 234-240.
UNICEF Annual Report, 2012, World Population Prospects The 2012 Revision, Volume II: Demographic Profiles, New York.
Wei, J., & Wang, X., 2020, An overview on linear unmixing of hyperspectral data, Mathematical Problems in Engineering, 1-12.
Wellmann, T., Schug, F., Haase, D., Pflugmacher, D., & van der Linden, S., 2020, Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series, Landscape and Urban Planning, 202, 103857.
Zhu, Z., 2017, Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384.
Zhu, Z., Wulder, M. A., Roy, D. P., Woodcock, C. E., Hansen, C., Radeloff, V. C., et al., 2019, Benefits of the free and open Landsat data policy, Remote Sensing of Environment, 224, 382–385.
Zortea, M., & Plaza, A., 2009, Spatial preprocessing for endmember extraction, IEEE Transactions on Geoscience and Remote Sensing, 47(8), 2679-2693.