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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2023 / Cilt: 12 - Sayı: 2

Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul

Sentinel-1 verilerine rastgele orman sınıflandırma yaparak İstanbul Arnavutköy için yapılaşma alanlarının konumsal ve zamansal değişiminin tespiti

Hasan Bilgehan MAKİNECİ
Pages
626–636
Publication Date
2023-04-15
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of the study, SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

📌 3 Key Takeaways

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Objective & Hypothesis:As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the crowd of people masses.
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Key Findings & Data:This research aims to determine urban areas expansion zones, also defined as the BAs footprint, from Sentinel-1 radar data.
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Conclusion & Impact:As a result of the study, SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.
🔬Design:Modeling & Simulation
📍Scope:2018-2021 Verileri
⚙️Variables:Yapılaşma Alanları Çıkarımı · Değişim Analizi · NDBI

Abstract

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the crowd of people masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BAs of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BAs footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. Based on the changes in spatial-temporal data, the direction of urban development has been determined. In addition, to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. As a result of the study, SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

Özet

Türkiye'nin ve dünyanın en kalabalık şehirlerinden biri olan İstanbul, her zaman kitleler halinde insanları kendine çekmiştir. Arnavutköy, artan Yapılaşma Alanları (YA’lar) ile İstanbul şehrinin kritik ilçelerinden biri haline gelmiştir. Bu ilçenin YA’larının genişlemesinin konumsal ve zamansal değişiminin tespiti, İstanbul adına önemli bir ihtiyaçtır. Bu çalışma, Sentinel-1 radar verilerinden YA ayak izi olarak da tanımlanan kentsel alanların genişleme bölgelerini belirlemeyi amaçlamaktadır. Bu çalışmada, 2018-2021 yılları arasında Arnavutköy Kasabasında karşılaşılan kentsel alan değişim tespitinin Sentinel-1A verilerinden makine öğrenmesi algoritması olan Rastgele Orman (RO) sınıflandırıcısı ile belirlenmesi incelenmiştir. Konumsal-zamansal verilerde yaşanan değişimlerden yola çıkarak, kentsel gelişimin yönü belirlenmiştir. Ayrıca, Normalize Fark Yapı İndeksi (NDBI) ve Sentinel-2A yalancı renkli RGB kompoziti görsel olarak kullanılarak karşılaştırmalı olarak yapısal değişim kanıtlanmıştır. Çalışma sonucunda uzaktan algılanan verilerle YA'ların çıkarılması konusunda, SAR uydu verilerinin atmosferik koşullardan etkilenmediği için optik uydu verilerine göre daha uygun olduğu belirlenmiştir.

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Cite This Article

MAKİNECİ, H. B. (2023). Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 12 - Sayı: 2, 626–636. https://ajindex.com/articles/892477

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References (8)

  1. M. Simwanda, Y. Murayama, and M. Ranagalage, Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach. Land Use Policy, 92: p. 104441, 2020. https://doi.org/10.1016/j.landusepol.2019.104441
  2. X. Li, Y. Zhou, M. Hejazi, M. Wise, C. Vernon, G. Iyer, and W. Chen, Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling. Communications Earth & Environment, 2(1), 201, 2021. https://doi.org/10.1038/s43247-021-00273-w E. Tercan, and U. H. Atasever, Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study. Environmental Science and Pollution Research, p. 1-13, 2021. https://doi.org/10.1007/s11356-021-12893-y
  3. S. Demir, M. Basaraner, and A.T. Gumus, Selection of suitable parking lot sites in megacities: A case study for four districts of Istanbul. Land Use Policy, p. 105731, 2021. https://doi.org/10.1016/j.landusepol.2021.105731
  4. Y. Lu, L. Yang, K. Yang, Z. Gao, H. Zhou, F. Meng, and J. Qi, A distributionally robust optimization method for passenger flow control strategy and train scheduling on an urban rail transit line. Engineering, 12, 202-220, 2022. https://doi.org/10.1016/j.eng.2021.09.016
  5. Y. Lv, W. Chen, and J. Cheng, Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models. Energy Policy, 147: p. 111858, 2020. https://doi.org/10.1016/j.enpol.2020.111858 A.Varshney, and E. Rajesh, A comparative study of built-up index approaches for automated extraction of built-up regions from remote sensing data. Journal of the Indian Society of Remote Sensing, 42(3): p. 659-663, 2014. https://doi.org/10.1007/s12524-013-0333-9 A. Singh, and S. K. P. Kushwaha, Forest Degradation Assessment Using UAV Optical Photogrammetry and SAR Data. Journal of the Indian Society of Remote Sensing, 49(3): p. 559-567, 2021. https://doi.org/10.1007/s12524-020-01232-2
  6. S. K. Saha, Remote Sensing and Geographic Information System Applications in Hydrocarbon Exploration: A Review. Journal of the Indian Society of Remote Sensing, p. 1-19, 2022. https://doi.org/10.1007/s12524-022-01540-9
  7. H. Cao, H. Zhang, C. Wang, and B. Zhang, Operational built-up areas extraction for cities in China using Sentinel-1 SAR data. Remote Sensing, 10(6), 874, 2018. https://doi.org/10.3390/rs10060874
  8. A. Ghodieh, Urban built-up area estimation and change detection of the occupied West