Document Type : Research Paper
Authors
1 Employee
2 employee
3 Member of the academic staff of Azad University, Yazd branch
Abstract
Illegal construction is one of the major challenges in urban development.Most of the illegal construction in large cities of Iran is related to height and construction violations, building permits, and commercial uses. Common methods for controlling construction are time-consuming and costly.Therefore, the main goal of this study is to provide a framework for rapid and low-cost estimation in monitoring and detecting unauthorized buildings and height violations in the ImamShahr area of Yazd due to population density,construction, and high potential for migration,using a combination of Sentinel 1 and Sentinel 2 radar satellite images in the period 2015 to 2020.For this purpose, after geometric and radiometric corrections, the images were first grouped together using the SNAP and ENVI software to separate and differentiate buildings based on height, using texture features including mean,variance, and dissimilarity inVV and VH polarizations, and then extracted using the artificial neural network classification method,urban impervious surfaces, and building heights, and using optical images to identify building shadows.The results of the processing,the average kappa coefficient and overall accuracy of Sentinel-1 images withVV polarization were 77.16and 79.5percent, and inVH polarization were79.16and 80.66percent, and for the neural network classification map of Sentinel-2 images, 66.73 and 66.75 percent;The number and area of unauthorized constructions also showed that in 2015, the study area had the highest amount of unauthorized construction compared to other study years due to the weakness of the building police field inspection, the legal vacuum in dealing with violators, and the manner of handling or factors of the violation(municipality, citizen).
Keywords
- Sentinel 1 radar images
- Sentinel 2 optical images
- illegal buildings
- radar interferometric method
- Imamshahr region of Yazd
Main Subjects
- Adamowski, J, and Prokoph, A. (2013). Assessing the Impacts of the Urban Heat Island Effecton Streamflow Patterns in Ottawa, Canada. Journal of Hydrology, 496, 225-237.
- Aliabad, F. A., Malamiri, H. R. G., Shojaei, S., Sarsangi, A., Ferreira, C. S. S., & Kalantari, Z. (2022). Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2. Remote Sensing, 14(13), 3227.
- Azmedroub, B., Ouarzeddine, M., & Souissi, B. (2016). Extraction of Urban Areas from Polarimetric SAR Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2583-2591
- Dabbiru, L.‚ Aanstoos, J.V.‚ Ball, J.E.‚ Younan, N.H., (2017).Screening Mississippi River LeveesUsing Texture-Based and Polarimetric-Based Features from Synthetic Aperture RadarData‚ Vol 6, 29 p, https://doi.org/10.3390/electronics6020029
- Douglas, I. (2006). Peri-urban ecosystems and societies: transitional zones and contrasting values. In: McGregor, D, Simon, D, Thompson, D. (Eds.), The Peri-Urban, Interface: Approaches to Sustainable Natural and Human Resource Use. Earth scan, London
- Fekade, W. (2000). Deficits of formal urban land management and informal responses under rapid urban growth: an. international perspective. Habitat Int, https://doi.org/10.1016/S0197-3975(99)00034-X
- Frantz, David & Schug, Franz & Okujeni, Akpona & Navacchi, Claudio & Wagner, Wolfgang & van der Linden, Sebastian & Hostert, Patrick. (2020). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment. 252.
https://doi.org/10.1016/j.rse.2020.112128
- FU, Huyan‚ Shao, Zhenfeng‚ (2016). Impacts of feature selection for urban impervious surface extraction using optical image and SAR data‚ IEEE‚ School of Electronics Information and Communications Huazhong University of Science and Technology Wuhan, China‚ 978-1-5090-14 79-8/16, DOI: 1109/EORSA.2016.7552842
- Hoornweg, D., Sugar, L., & Trejos Gómez, C. L. (2011). Cities and Greenhouse Gas Emissions:Moving Forward. Environment and Urbanization, 23(1), 207-227.
- Jimboh, B.A, Al-Hasan, A.Z, Imimole, W.O, and Ahmed, M.B(2017). Contravention of Development Control Measures in Auchi, Edo State, Nigeria, Applied Science Reports, 20 (1), pp:30-34. https://ssrn.com/abstract=3201451
- Kaya, S., Basar, U. G., Karaca, M., & Seker, D. Z. (2012a). Assessment of Urban Heat Islands Using Remotely Sensed Data. Ekoloji, 21(84), 107-113.
- Kaya, S., Seker, D. Z., & Tanik, A. (2012b). Analysis of Urbanized Areas Using VIS Components Model. Fresenius Environmental Bulletin, 21(11), 3243-3248.
- Kević, V., Racetin, I., & Krtalić, A. (2025). Temporal Analysis of Multispectral Satellite Data for the Purpose of Urbanization Monitoring. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 165-170.
- Kim C, Jeong J, Kim R, Son Y, Lee KH, et al. (2011). Allometric equations and biomass expansion factors of Japanese red pine on the local level. Landscape Ecology Engineering 7: 283-289, https://doi.org/10.1007/s11355-010-0131-2
- Lagarias, A. (2012). Urban Sprawl Simulation Linking Macro-Scale Processes to Micro-Dynamics through Cellular Automata, an Application in Thessaloniki, Greece. Applied Geography,34, 146-160.
- Lu, Dengsheng, Moran, Emilio, Hetrick, Scott, (2011). Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier‚ ISPRS Journal of Photogrammetry and Remote Sensing, Pp 298-299, https://doi.org/10.1016/j.isprsjprs.2010.10.010
- Mehta, Y., Baz, A., & Patel, S. K. (2024). Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning. Results in Engineering, 24, 103383.
- Sun, Z., Xu, R., Du, W., Wang, L., & Lu, D. (2019).High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine.Remote Sensing, 117, 752.
- Sekertekin, A., Kutoglu, S. H., & Kaya, S. (2016). Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey. Environmental monitoring and assessment, 188(1), 30.
- Sekertekin, A., Abdikan, S., & Marangoz, A. M. (2018). The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environmental monitoring and assessment, 190(7), 381.
- Vigneshwaran, S., & Kumar, S. V. (2018). Extractionof Built-up area using Highresolution Sentinel-2A and Google Satellite Imagery. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42
- Wu, Y., Li, X., Gong, Y., & Mwaniki, D. (2025). A flexible framework for identifying urban villages using Sentinel-2 observations and deep learning. International Journal of Applied Earth Observation and Geoinformation, 141, 104631
- Zhang, Hongsheng‚ Lina, Hui‚ Li, Yu‚ Zhang, Yuanzhi‚ Fang, ChaoYang, (2016). Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban-rural landscape‚ Landscape and Urban Planning, Pp 4812-4815.
- Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y.,Xie, S., & Zhang, B. (2020). Development of a global30 m impervious surface map using multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data,123, 1625-1648.
- Żuk, L., & Królewicz, S. (2022). Uses of sentinel-1 and-2 images in heritage management: a case study from Lednica Landscape Park (Poland). Geosciences, 12(4), 159.
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