Assessment of Urban Changes at the Residential Neighbourhood Level Based on Satellite Imageries

Nada Kadhim, Nabil M. Salih

Abstract


Ongoing urban expansion leads to the steady loss of green spaces. Residential units' gardens and green open spaces are a vital part of city life, contributing considerably to urban green infrastructure and ecological services. However, these areas are diverse, making it difficult to assess their changes over time to take advantage of their benefits and contribution to sustainable urban development. This study proposes a new methodology that combines survey data with high-resolution image analysis to construct maps and statistics of change in two residential neighbourhood areas in the Iraqi city of Baqubah. Three change detection techniques utilising very high-resolution multispectral Pléiades images were used to evaluate the changes: pixel value differencing, band index differencing, and categorical change detection. Then, a unique strategy employing geo-processing processes by the ModelBuilder tool was applied to the evaluation outcomes to assess the changes in a final manner. In addition to survey data that supported the final change detection outcomes, study validation was conducted through field verification, and the mean accuracy was 93%. The final results indicated that open or green spaces decreased over a period of seven years at rates of 24% and 14% of the total of both areas assessed. Policymakers and urban planners see such privately owned land as difficult to affect. However, reducing vegetative cover areas and turning them into impermeable surfaces may result in the areas becoming inefficient in the development of urban sustainability. Our developed method demonstrates the capability of utilising Very High Resolution (VHR) imagery with local survey data to accurately infer changes in urban vegetation within residential neighbourhood regions.

 

Doi: 10.28991/CEJ-2025-011-01-05

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Keywords


Urban Change Detection; Pléiades Satellite Images; Machine Learning; Residential Areas; Urban Sustainability; ArcGIS.

References


Xiao, P., Zhang, X., Wang, D., Yuan, M., Feng, X., & Kelly, M. (2016). Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 402–414. doi:10.1016/j.isprsjprs.2016.07.003.

Negeri, M. D., Guta, M. S., & Erena, S. H. (2023). Determinant factors hinder urban structure plan implementation: The case of Nekemte Town, Ethiopia. Heliyon, 9(3). doi:10.1016/j.heliyon.2023.e13448.

Kadhim, N., Ismael, N. T., & Kadhim, N. M. (2022). Urban Landscape Fragmentation as an Indicator of Urban Expansion Using Sentinel-2 Imageries. Civil Engineering Journal (Iran), 8(9), 1799–1814. doi:10.28991/CEJ-2022-08-09-04.

Kadhim, N., Mourshed, M., & Bray, M. (2016). Advances in remote sensing applications for urban sustainability. Euro-Mediterranean Journal for Environmental Integration, 1(1), 7. doi:10.1007/s41207-016-0007-4.

Kalfas, D., Kalogiannidis, S., Chatzitheodoridis, F., & Toska, E. (2023). Urbanization and Land Use Planning for Achieving the Sustainable Development Goals (SDGs): A Case Study of Greece. Urban Science, 7(2), 43. doi:10.3390/urbansci7020043.

Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106. doi:10.1016/j.isprsjprs.2013.03.006.

Kadhim, N., & Mourshed, M. (2018). A shadow-overlapping algorithm for estimating building heights from VHR satellite images. IEEE Geoscience and Remote Sensing Letters, 15(1), 8–12. doi:10.1109/LGRS.2017.2762424.

Du, P., Liu, S., Gamba, P., Tan, K., & Xia, J. (2012). Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1076–1086. doi:10.1109/JSTARS.2012.2200879.

Bruzzone, L., & Bovolo, F. (2013). A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 609–630. doi:10.1109/JPROC.2012.2197169.

Li, J., Yuan, Q., Shen, H., Meng, X., & Zhang, L. (2016). Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial-Spectral Group Sparsity. IEEE Geoscience and Remote Sensing Letters, 13(9), 1250–1254. doi:10.1109/LGRS.2016.2579661.

Singh, A., & Singh, K. K. (2018). Unsupervised change detection in remote sensing images using fusion of spectral and statistical indices. Egyptian Journal of Remote Sensing and Space Science, 21(3), 345–351. doi:10.1016/j.ejrs.2018.01.006.

Liu, T., Yang, L., & Lunga, D. (2021). Change detection using deep learning approach with object-based image analysis. Remote Sensing of Environment, 256, 112308. doi:10.1016/j.rse.2021.112308.

Yang, X., Chen, R., Zhang, F., Zhang, L., Fan, X., Ye, Q., & Fu, L. (2021). Pixel-level automatic annotation for forest fire image. Engineering Applications of Artificial Intelligence, 104, 104353. doi:10.1016/j.engappai.2021.104353.

Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). NDVI: Vegetation Change Detection Using Remote Sensing and GIS - A Case Study of Vellore District. Procedia Computer Science, 57, 1199–1210. doi:10.1016/j.procs.2015.07.415.

Leichtle, T., Geiß, C., Wurm, M., Lakes, T., & Taubenböck, H. (2017). Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, 15–27. doi:10.1016/j.jag.2016.08.010.

Chen, D., Loboda, T. V., Silva, J. A., & Tonellato, M. R. (2021). Characterizing small-town development using very high-resolution imagery within remote rural settings of Mozambique. Remote Sensing, 13(17), 3385. doi:10.3390/rs13173385.

Liu, H., Yang, M., Chen, J., Hou, J., & Deng, M. (2018). Line-constrained shape feature for building change detection in VHR remote sensing imagery. ISPRS International Journal of Geo-Information, 7(10), 410. doi:10.3390/ijgi7100410.

Zhang, C., Wei, S., Ji, S., & Lu, M. (2019). Detecting large-scale urban land cover changes from very high-resolution remote sensing images using CNN-based classification. ISPRS International Journal of Geo-Information, 8(4), 189. doi:10.3390/ijgi8040189.

Warth, G., Braun, A., Assmann, O., Fleckenstein, K., & Hochschild, V. (2020). Prediction of socio-economic indicators for urban planning using VHR satellite imagery and spatial analysis. Remote Sensing, 12(11). doi:10.3390/rs12111730.

Chen, H., Zhang, K., Xiao, W., Sheng, Y., Cheng, L., Zhou, W., Wang, P., Su, D., Ye, L., & Zhang, S. (2021). Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification. International Journal of Remote Sensing, 42(7), 2686–2705. doi:10.1080/01431161.2020.1862437.

Afaq, Y., & Manocha, A. (2021). Analysis on change detection techniques for remote sensing applications: A review. Ecological Informatics, 63, 101310. doi:10.1016/j.ecoinf.2021.101310.

Baker, F., Smith, C. L., & Cavan, G. (2018). A combined approach to classifying land surface cover of urban domestic gardens using citizen science data and high-resolution image analysis. Remote Sensing, 10(4), 537. doi:10.3390/rs10040537.

Setiowati, R., & Koestoer, R. H. (2024). Valuation of Urban Green Open Spaces Using the Life Satisfaction Approach. Civil Engineering Journal (Iran), 10(4), 1159–1181. doi:10.28991/CEJ-2024-010-04-010.

Kim, Y., & Yoon, H. (2024). Accurate and efficient feature classification of urban public open spaces: A deep learning-based multivariate time-series approach. International Journal of Applied Earth Observation and Geoinformation, 133, 104113. doi:10.1016/j.jag.2024.104113.

Villaverde, A., Álvarez, I., Rojí, E., & Garmendia, L. (2024). Categorisation of urban open spaces for heat adaptation: A cluster based approach. Building and Environment, 263, 111861. doi:10.1016/j.buildenv.2024.111861.

Odhengo, P., Lutta, A. I., Osano, P., & Opiyo, R. (2024). Urban green spaces in rapidly urbanizing cities: A socio-economic valuation of Nairobi City, Kenya. Cities, 155, 105430. doi:10.1016/j.cities.2024.105430.

Hou, Y., Liu, Y., Wu, Y., & Zhang, B. (2024). Assessment of spatial and socioeconomic disparities in urban green space accessibility based on a Physical Activity Diversity Index (PADI). Ecological Indicators, 166, 112478. doi:10.1016/j.ecolind.2024.112478.

Shepherd, J. D., Bunting, P., & Dymond, J. R. (2019). Operational large-scale segmentation of imagery based on iterative elimination. Remote Sensing, 11(6), 658. doi:10.3390/RS11060658.

Wessel, M., Brandmeier, M., & Tiede, D. (2018). Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sensing, 10(9), 1419. doi:10.3390/rs10091419.

Antonelli, L., De Simone, V., & di Serafino, D. (2020). Spatially adaptive regularization in image segmentation. Algorithms, 13(9), 226. doi:10.3390/A13090226.

Saha, S., Bovolo, F., & Bruzzone, L. (2019). Unsupervised deep change vector analysis for multiple-change detection in VHR Images. IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3677–3693. doi:10.1109/TGRS.2018.2886643.

Touati, R., Mignotte, M., & Dahmane, M. (2020). Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 588–600. doi:10.1109/JSTARS.2020.2964409.

Zhou, L., Gong, Y., López-Carr, D., & Huang, C. (2024). A critical role of the capital green belt in constraining urban sprawl and its fragmentation measurement. Land Use Policy, 141, 107148. doi:10.1016/j.landusepol.2024.107148.

Grover, A., Vadakkuveettil, A., Chen, R., & Wu, J. (2024). Warming reality of Kozhikode Urban Area: Uncovering the heat of built-up expansion and vegetation loss. Environmental Challenges, 17, 101016. doi:10.1016/j.envc.2024.101016.


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DOI: 10.28991/CEJ-2025-011-01-05

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