Amiri, F., Tabatabaie, T. (2021). Classification and Analysis of Land Use Changes in Urban Environments Using Multi-temporal Landsat Images: A Case Study of Bushehr. , 9(1), 167-186. doi: 10.22092/lmj.2021.123619
Fazel Amiri; Tayebeh Tabatabaie. "Classification and Analysis of Land Use Changes in Urban Environments Using Multi-temporal Landsat Images: A Case Study of Bushehr". , 9, 1, 2021, 167-186. doi: 10.22092/lmj.2021.123619
Amiri, F., Tabatabaie, T. (2021). 'Classification and Analysis of Land Use Changes in Urban Environments Using Multi-temporal Landsat Images: A Case Study of Bushehr', , 9(1), pp. 167-186. doi: 10.22092/lmj.2021.123619
Amiri, F., Tabatabaie, T. Classification and Analysis of Land Use Changes in Urban Environments Using Multi-temporal Landsat Images: A Case Study of Bushehr. , 2021; 9(1): 167-186. doi: 10.22092/lmj.2021.123619

Classification and Analysis of Land Use Changes in Urban Environments Using Multi-temporal Landsat Images: A Case Study of Bushehr

مدیریت اراضی
Volume 9, Issue 1, August 2021, Pages 167-186    PDF (1.7 M)
Document Type: Research Paper
DOI: 10.22092/lmj.2021.123619
Authors
Fazel Amiri1; Tayebeh Tabatabaie* 2
1Department of Natural Resources and Environment, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran
2Department of Environment, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Busheher, Iran
Abstract
The complicated relationship between land use (LU) and environmental factors influences human livelihood; hence, it is essential to monitor LU changes and to prepare pertinent maps due to their relevance to such vital fields as urban planning, climate change, and environmental monitoring. In this study, a supervised classification was applied to three Landsat images collected over time (1990, 2000, and 2018) in order to derive the data on land use changes in Bushehr region. The supervised classification results were further improved by employing image enhancement and classification accuracy of the Landsat images was enhanced by visual interpretation to enhance. Finally, eight LU categories were identified and mapped. It was found that land reclamation projects over the last three decades had had drastic effects on LU changes throughout the study area. Land use changes during the study period show increasing trends as evidenced by the 29.19 hectares of agricultural land with a rise of 30.19% and the 15.78 hectares of urban and rural residential land with a rise of 16.39%. In contrast, declines are observed in barren land covering 4.15 hectares with a change of -4.31%, salty land covering 11.66 hectares with a negative change of -12.11, and sandy land covering 5.21 hectares with a change of -5.41. Hence, agricultural land area as well as urban and rural residential areas show significant increases whereas barren, salty, and sandy land areas show decreases. These changes in LU reflect regional policies and the dynamics of human impacts (agricultural and construction activities) on land use changes in the study region. Identifying the causes underlying these changes, the present article tries to formulate policy recommendations toward improved land use management.
Keywords
Change trend; Multi-temporal assessment; Multispectral image; Remote sensing; GIS
References
  1. اسدی، ا؛ اکبری، ا؛ شفیعی، ن، 1399، پیش­بینی توسعۀ فیزیکی شهر قائن با استفاده از تصاویر ماهواره­ای، نشریۀ برنامه­ریزی فضایی (جغرافیا)، 10 (1)، 84-67.
  2. خنامانی، ع؛ فتحی زاد، ح؛ حکیم زاده، م. ع، 1397، ارزیابی روند تغییر کاربری و پوشش اراضی با استفاده از تکنیک سنجش از دور و الگوریتم طبقه‌بندی شیء‌گرا (مطالعه موردی: دشت برتش دهلران، استان ایلام)، نشریه تحقیقات مرتع و بیابان، 25 (4): 734-723.
  3. سودایی­زاده، ح؛ جبالی، ع، 1399، پایش تغییرات سطح اراضی کشاورزی تحت سیستم اگروفارستری در باغباغوئیه جیرفت، نشریه مدیریت اراضی، 8 (1): 27-13.
  4. صالحی،ن؛ اختصاصی، م. ر؛ طالبی، ع، 1398،  پیش بینی روند تغییرات کاربری اراضی با استفاده از مدل زنجیره مارکوف CA-Markov (مطالعه موردی: حوزه آبخیز صفارود رامسر)، نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 10 (1):120-106.
  5. محمودی، م. ع؛ امین خواه، س، 1397، تهیه نقشه کاربری و پوشش اراضی با استفاده از داده‌های سنجش از دور و شبکه عصبی مصنوعی، نشریۀ تحقیقات آب و خاک ایران (نشریۀ علوم کشاورزی ایران)، 49 (5): 1180-1171.
  6. Abdu HA. 2019. Classification accuracy and trend assessments of land cover-land use changes from principal components of land satellite images. International Journal of Remote Sensing, 40(4): 1275-1300.
  7. Adhikary PP, Barman D, Madhu M, Dash CJ, Jakhar P, Hombegowda H, Naik B, Sahoo D, Beer K. 2019. Land use and land cover dynamics with special emphasis on shifting cultivation in Eastern Ghats Highlands of India using remote sensing data and GIS. Environmental monitoring and assessment, 191(5): 315.
  8. Allam M, Bakr N, Elbably W. 2019. Multi-temporal assessment of land use/land cover change in arid region based on landsat satellite imagery: Case study in Fayoum Region, Egypt. Remote Sensing Applications: Society and Environment, 14: 8-19. doi:https://doi.org/10.1016/j.rsase.2019.02.002.
  9. Amiri F, Shariff ARBM. 2012. Spatial Change Detector (SCDv®1). University Putra Malaysia. In: University Putra Malaysia. (Copyright No. B197), 1-12.
  10. Anderson JR, Survey G .1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. US Geological Survey Professional Paper 964, Washington, DC., 28 p,
  11. Arévalo P, Olofsson P, Woodcock CE. 2019. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sensing of Environment: Available online 29 January 2019. doi: 2010.1016/j.rse.2019.2001.2013. doi:https://doi.org/10.1016/j.rse.2019.01.013.
  12. Bekele D, Alamirew T, Kebede A, Zeleke G, Melesse AM. 2019. Land use and land cover dynamics in the Keleta watershed, Awash River basin, Ethiopia. Environmental Hazards, 18(3): 246-265.
  13. El-Kawy OA, Rød J, Ismail H, Suliman A. 2011. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2): 483-494.
  14. Evans MJ, Malcom JW. 2019. Automated Habitat Change Detection Methods using Satellite Data to Improve Conservation Law Implementation. bioRxiv: 611459.
  15. Fu Y, Li J, Weng Q, Zheng Q, Li L, Dai S, Guo B. 2019. Characterizing the spatial pattern of annual urban growth by using time series Landsat imagery. Science of The Total Environment, 666: 274-284.
  16. Gao J, Liu Y. 2010. Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. International Journal of Applied Earth Observation and Geoinformation, 12(1): 9-16.
  17. García-Álvarez D, Van Delden H, Olmedo MTC, Paegelow M. 2019. Uncertainty Challenge in Geospatial Analysis: An Approximation from the Land Use Cover Change Modelling Perspective. In: Geospatial Challenges in the 21st Century. Springer, pp 289-314.
  18. Grădinaru SR, Kienast F, Psomas A. 2019. Using multi-seasonal Landsat imagery for rapid identification of abandoned land in areas affected by urban sprawl. Ecological indicators, 96: 79-86.
  19. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.
  20. Kabisch N, Selsam P, Kirsten T, Lausch A, Bumberger J. 2019. A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Ecological Indicators, 99: 273-282.
  21. Liu Y, Peng J, Wang Y. 2017. Diversification of land surface temperature change under urban landscape renewal: a case study in the main city of Shenzhen, China. Remote Sensing, 9(9), 1-19.
  22. Rajani A, Varadarajan S. 2020. LU/LC Change Detection Using NDVI & MLC Through Remote Sensing and GIS for Kadapa Region. In: Mallick PK, Balas VE, Bhoi AK, Chae G-S (eds) Cognitive Informatics and Soft Computing, Singapore, Springer Singapore, pp 215-223.
  23. Pontius Jr RG. 2018. PontiusMatrix21.xlsx (Workbook). wwwclarkuedu/~rpontius.
  24. Robert S, Fox D, Boulay G, Grandclément A, Garrido M, Pasqualini V, Prévost A, Schleyer-Lindenmann A, Trémélo M-L. 2019. A framework to analyse urban sprawl in the French Mediterranean coastal zone. Regional Environmental Change, 19(2): 559-572.
  25. Rwanga SS, Ndambuki J. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04): 611.
  26. Shalaby A, Tateishi R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1): 28-41. doi:https://doi.org/10.1016/j.apgeog.2006.09.004.
  27. Shiferaw H, Bewket W, Alamirew T, Zeleke G, Teketay D, Bekele K, Schaffner U, Eckert S. 2019. Implications of land use/land cover dynamics and Prosopis invasion on ecosystem service values in Afar Region, Ethiopia. Science of The Total Environment, 675: 354-366.
  28. Syariz MA, Lin B-Y, Denaro LG, Jaelani LM, Van Nguyen M, Lin C-H. 2019. Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 56-64.
  29. Um J-S. 2019. Imaging Sensors. In: Um J-S (ed) Drones as Cyber-Physical Systems: Concepts and Applications for the Fourth Industrial Revolution. Springer Singapore, Singapore, pp 177-225.
  30. Wang Y, Ziv G, Adami M, Mitchard E, Batterman SA, Buermann W, Marimon BS, Junior BHM, Reis SM, Rodrigues D. 2019. Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery. Remote sensing of environment, 221: 474-488.
  31. Yang G, Chao S, Tsou JY, Zhang Y. 2019. Satellite Image-Based Methods of Spatiotemporal Analysis on Sustainable Urban Land Use Change and the Driving Factors: A Case Study in Caofeidian and the Suburbs, China. Sustainability, 11(10): 2927.
  32. Yin H, Prishchepov AV, Kuemmerle T, Bleyhl B, Buchner J, Radeloff VC. 2018. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote sensing of environment, 210: 12-24.
  33. Zhu Z, Woodcock CE. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of environment, 144:152-171.
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