|
Land Cover Change Detection Using Classification Algorithms, Machine Learning, and Fusion of Sentinel-1 and Sentinel-2 Imagery: A Case Study of District 22, Tehran
|
Ebrahim Karami *   |
|
|
|
Abstract: (83 Views) |
District 22 of Tehran, as one of the rapidly developing urban areas, has undergone significant land use changes, making it a critical subject for environmental change studies. This research aimed to analyze and identify environmental and structural changes over the period from 2016 to 2021. Various remote sensing methods were employed, including Change Vector Analysis (CVA), regression techniques, and spectral indices.Satellite data from Sentinel-1 and Sentinel-2 were used in an integrated manner, and textural features were extracted from both optical and radar imagery. The results indicated that data fusion significantly improved classification accuracy: overall accuracy in 2016 reached 91% with a Kappa coefficient of 89%, while in 2021, overall accuracy was 86% with a Kappa coefficient of 85%. In addition to percentage changes in land use classes—construction (13%), vegetation (15%), water resources (4%), and soil (11%)—pixel-level analysis was also conducted. This enabled the identification of both the percentage and number of pixels that changed between different land use classes. For example, in the water class, the largest change was toward soil (18,300 pixels). In the road class, 21,000 pixels remained unchanged, while some transitioned to vegetation, construction, and water. Vegetation showed the highest stability with 158,000 unchanged pixels, although portions were converted to soil and construction. In the soil class, besides 42,000 stable pixels, over 156,000 pixels changed to roads and 12,000 to construction.This detailed analysis allows for more precise identification of change patterns and supports the development of targeted management strategies.
|
Article number: 4 |
| Keywords: Land use, satellite data, regression analysis, multilayer perceptron neural network (MLP), fusion of optical and radar data |
|
|
Full-Text [PDF 2449 kb]
(45 Downloads)
|
Type of Study: Research |
Subject:
Photo&RS
Received: 2025/02/7 | Accepted: 2025/10/18
|
|
|
|
|
|
|
| References |
1. Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666-20671. [ DOI:10.1073/pnas.0704119104] 2. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28(1), 205-241. [ DOI:10.1146/annurev.energy.28.050302.105459] 3. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. [ DOI:10.1080/01431160600746456] 4. Drusch, M., et al. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25-36. [ DOI:10.1016/j.rse.2011.11.026] 5. Torres, R., et al. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9-24. [ DOI:10.1016/j.rse.2011.05.028] 6. Claverie, M., et al. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. [ DOI:10.1016/j.rse.2018.09.002] 7. Ban, Y., Jacob, A., & Gamba, P. (2015). Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 28-37.
https://doi.org/10.1016/j.isprsjprs.2014.08.004 [ DOI:10.1016/j.isprsjprs.2015.01.011] 8. Hafner, M., et al. (2020). Semi-supervised change detection using Sentinel-1 and Sentinel-2 time series. Remote Sensing, 12(3), 456. [ DOI:10.3390/rs12030456] 9. Zhang, X., et al. (2021). GLC_FCS10: A 10 m resolution global land cover dataset based on Sentinel imagery. Remote Sensing, 13(5), 922. [ DOI:10.3390/rs13050922] 10. Coppin, P., & Bauer, M. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3-4), 207-234. [ DOI:10.1080/02757259609532305] 11. Zhu, X. X., et al. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36. [ DOI:10.1109/MGRS.2017.2762307] 12. Chen, Y., et al. (2021). OFNet: Optical flow guided network with dual attention for change detection. Remote Sensing, 13(2), 254. [ DOI:10.3390/rs13020254] 13. Chen, J., et al. (2003). A simple method for detecting change in remotely sensed images by using the change vector analysis. International Journal of Remote Sensing, 24(8), 1657-1666. [ DOI:10.1080/01431160210163095] 14. Li, X., et al. (2019). Urban change detection using Sentinel-1 data and support vector machines. Remote Sensing Letters, 10(5), 451-460. [ DOI:10.1080/2150704X.2019.1585183] 15. Johnson, B. A., et al. (2020). Land cover classification in Africa using Sentinel-2 and artificial neural networks. Remote Sensing, 12(3), 456. [ DOI:10.3390/rs12030456] 16. Davis, C., et al. (2019). Evaluating CNN architectures for satellite image classification. ISPRS International Journal of Geo-Information, 8(5), 219. [ DOI:10.3390/ijgi8050219] 17. Clark, M., et al. (2021). Improving urban land cover classification using optimized U-Net architecture. Remote Sensing, 13(10), 1987. [ DOI:10.3390/rs13101987] 18. Fawzy, M., & Barsi, Á. (2022). Urban land cover classification using high-resolution imagery and U-Net. Remote Sensing, 14(3), 678. [ DOI:10.3390/rs14030678] 19. Alkhediri, M., et al. (2018). Object-based change detection using PCA and high-resolution imagery. Remote Sensing Letters, 9(2), 123-132. [ DOI:10.1080/2150704X.2017.1410292] 20. Peterson, J., et al. (2020). Deep learning for urban change detection: A comparative study. Remote Sensing, 12(6), 1012. [ DOI:10.3390/rs12061012] 21. Zhang, Y., et al. (2021). Multi-temporal urban expansion analysis using machine learning. Remote Sensing, 13(7), 1356. [ DOI:10.3390/rs13071356] 22. Ghorbanzadeh, O., et al. (2020). Fusion of Sentinel-1 and Sentinel-2 data for urban change detection. Remote Sensing, 12(4), 678. [ DOI:10.3390/rs12040678] 23. Li, Y., et al. (2022). Siamese networks for fine-grained urban change detection. Remote Sensing, 14(1), 123. [ DOI:10.3390/rs14010123] 24. Hafner, M. (2021). Multi-modal learning for urban change detection using Sentinel data (Doctoral dissertation). [University Repository] 25. Mastro, L., et al. (2020). Urban and disaster change detection using random forest and Sentinel-1. Remote Sensing, 12(9), 1456. [ DOI:10.3390/rs12091456] 26. Zhang, X., et al. (2022). Urban change detection using CNN and Sentinel-2 imagery. Remote Sensing, 14(2), 456. [ DOI:10.3390/rs14020456] 27. نویسندگان مقاله ایرانی]. (سال). طبقهبندی پوشش زمین مبتنی بر روشهای یادگیری ماشین و یادگیری عمیق با استفاده از تصاویر ماهوارهای سنتینل-۲؛ مطالعه موردی: منطقه شهری غرب تهران. مجله علم و فنون. 28. ESA. (2020). Sentinel-2 User Handbook. European Space Agency. 29. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309. 30. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. [ DOI:10.1016/0034-4257(88)90106-X] 31. ESA. (2021). Sentinel-1 Toolbox - SNAP Documentation. European Space Agency. 32. Lopes, A., Touzi, R., & Nezry, E. (1990). Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6), 992-1000. [ DOI:10.1109/36.62623] 33. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. [ DOI:10.1109/TSMC.1973.4309314] 34. Zhang, Y., & Mishra, R. (2012). A review and comparison of commercially available SAR image fusion techniques. International Journal of Remote Sensing, 33(14), 4434-4460. [35] Pohl, C., & Van Genderen, J. L. (1998). Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823-854. [ DOI:10.1080/014311698215748] 35. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. [ DOI:10.1080/01431160600746456] 36. Malila, W. A. (1980). Change vector analysis: An approach for detecting forest changes with Landsat. Proceedings of the 6th Annual Symposium on Machine Processing of Remotely Sensed Data, Purdue University, 326-335. 37. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. [ DOI:10.1080/01431160304987] 38. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. [ DOI:10.1016/0034-4257(79)90013-0] 39. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107. [ DOI:10.1016/0034-4257(95)00186-7] 40. Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033. [ DOI:10.1080/01431160600589179] 41. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., & Kuemmerle, T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70. [ DOI:10.3390/rs8010070] 42. Mahyouba, S., Fadil, A., Mansour, E. M., Rhinane, H., & Al-Nahmi, F. (2019). Fusing of optical and synthetic aperture radar (SAR) remote sensing data: A systematic literature review. ISPRS Archives, XLII-4/W12, 127-134. [ DOI:10.5194/isprs-archives-XLII-4-W12-127-2019] 43. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, [ DOI:10.1016/0034-4257(95)00186-7] |
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
|
karami E. Land Cover Change Detection Using Classification Algorithms, Machine Learning, and Fusion of Sentinel-1 and Sentinel-2 Imagery: A Case Study of District 22, Tehran. JGST 2025; 15 (2) : 4 URL: http://jgst.issgeac.ir/article-1-1213-en.html
|
