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

Yıl 2023, Cilt: 12 Sayı: 2, 626 - 636, 15.04.2023

Hasan Bilgehan Makineci

https://doi.org/10.28948/ngumuh.1203301
Cited By: 1

Öz

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.

Anahtar Kelimeler

Built-Up Areas Extraction Change Detection NDBI Sentinel-1A Random Forest Classification

Teşekkür

Many thanks to ESA for free SNAP software, Sentinel satellite data and all kinds of support, and the Republic of Turkey Ministry of Environment and Urbanization for their open-access Spatial Strategy Planning.

Kaynakça

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

Yıl 2023, Cilt: 12 Sayı: 2, 626 - 636, 15.04.2023

Hasan Bilgehan Makineci

https://doi.org/10.28948/ngumuh.1203301
Cited By: 1

Öz

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.

Anahtar Kelimeler

Yapılaşma Alanları Çıkarımı Değişim Analizi NDBI Sentinel-1A Rastgele Orman

Kaynakça

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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • A. Ghodieh, Urban built-up area estimation and change detection of the occupied West Bank, Palestine, using multi-temporal aerial photographs and satellite images. Journal of the Indian Society of Remote Sensing, 48(2): p. 235-247, 2020. https://doi.org/10.1007/s12524-019-01073-8
  • R. Guida, A. Iodice, D. Riccio, and U. Stilla, Model-based interpretation of high-resolution SAR images of buildings. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(2), 107-119, 2008. https://doi.org/10.1109/JSTARS.2008.2001155
  • M. Massano, E. Macii, A. Lanzini, E. Patti, and L. Bottaccioli, A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas. Engineering, 2022. https://doi.org/10.1016/j.eng.2022.06.020
  • A. Htitiou, A. Boudhar, Y. Lebrini, R. Hadria, H. Lionboui, and T. Benabdelouahab, A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: A machine learning approach. Geocarto International, 37(5), 1426-1449, 2022. https://doi.org/10.1080/10106049.2020.1768593
  • J. A. Gómez, J. E. Patiño, J. C. Duque, and S. Passos, Spatiotemporal modeling of urban growth using machine learning. Remote Sensing, 12(1), 109, 2019. https://doi.org/10.3390/rs12010109
  • X. Niu, and Y. Ban, Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1): p. 1-26, 2013. https://doi.org/10.1080/01431161.2012.700133
  • L. Bruzzone, M. Marconcini, U. Wegmuller, and A. Wiesmann, An advanced system for the automatic classification of multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1321-1334, 2004. https://doi.org/10.1109/TGRS.2004.826821
  • J. Geng, H. Wang, J. Fan, and X. Ma, Deep supervised and contractive neural network for SAR image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(4), 2442-2459, 2017. https://doi.org/10.1109/TGRS.2016.2645226
  • A. Jamali, and A.A. Rahman, SENTINEL-1 image classification for city extraction based on the support vector machine and random forest algorithms. Int Arch Photogramm Remote Sens Spat Inf Sci, 42(4): p. W16, 2019. https://doi.org/10.5194/isprs-archives-XLII-4-W16-297-2019
  • B. Wang, J Li, X. Jin, and H. Xiao, Mapping tea plantations from multi-seasonal Landsat-8 OLI imageries using a random forest classifier. Journal of the Indian Society of Remote Sensing, 47, 1315-1329, 2019. https://doi.org/10.1007/s12524-019-01014-5
  • C. Kar, and S. Banerjee, Tropical Cyclones Intensity Estimation by Feature Fusion and Random Forest Classifier Using Satellite Images. Journal of the Indian Society of Remote Sensing, p. 1-12, 2022. https://doi.org/10.1007/s12524-021-01477-5
  • H. Wu, J. Zhang, Z. Bao, G. Wang, W. Wang, Y. Yang, and J. Wang, Runoff modeling in ungauged catchments using machine learning algorithm-based model parameters regionalization methodology. Engineering, 2022. https://doi.org/10.1016/j.eng.2021.12.014
  • P.O. Gislason, J.A. Benediktsson, and J.R. Sveinsson, Random forests for land cover classification. Pattern recognition letters, 27(4): p. 294-300, 2006. https://doi.org/10.1016/j.patrec.2005.08.011
  • J. Sharma, J. Eppler, and J. Busler, Urban infrastructure monitoring with a spatially adaptive multi-looking InSAR technique. Proc. of Fringe, Frascati, Italy, 2015.
  • H. B. Makineci, and H. Karabörk, Evaluation digital elevation model generated by synthetic aperture radar data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1: p. 57-62, 2016. https://doi.org/10.5194/isprs-archives-XLI-B1-57-2016
  • Y. Ban, A. Jacob, and P. Gamba, Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS Journal of Photogrammetry and Remote Sensing, 103: p. 28-37, 2015. https://doi.org/10.1016/j.isprsjprs.2014.08.004
  • Y. Yao, D. Chen, L. Chen, H. Wang, and Q. Guan, A time series of urban extent in China using DSMP/OLS nighttime light data. PLoS One, 13(5), e0198189, 2018. https://doi.org/10.1371/journal.pone.0198189
  • F. Li, Q. Yan, Z. Bian, B. Liu, and Z. Wu, A POI and LST adjusted NTL urban index for urban built-up area extraction. Sensors, 20(10), 2918, 2020. https://doi.org/10.3390/s20102918
  • Z. Jun, Y. Xiao-Die, and L. Han, The extraction of urban built-up areas by integrating night-time light and POI data—A case study of Kunming, China. IEEE Access, 9: p. 22417-22429, 2021. https://doi.org/10.1109/ACCESS.2021.3054169
  • A. Braun, Retrieval of digital elevation models from Sentinel-1 radar data–open applications, techniques, and limitations. Open Geosciences, 13(1): p. 532-569, 2021. https://doi.org/10.1515/geo-2020-0246
  • A. Mercier, J. Betbeder, F. Rumiano, J. Baudry, V. Gond, L. Blanc, C. Bourgoin, G. Cornu, C. Ciudad, M. Marchamalo, R. Poccard-Chapuis, and L. Hubert-Moy, Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sensing, 11, 979, 2019. https://doi.org/10.3390/rs11080979
  • L. Carrasco, A. W. O’Neil, R.D. Morton, and C. S. Rowland, Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sensing, 11(3), 288, 2019. https://doi.org/10.3390/rs11030288
  • A. Semenzato, S. E. Pappalardo, D. Codato, U. Trivelloni, S. De Zorzi, S. Ferrari, M. De Marchi, and M. Massironi, Mapping and Monitoring Urban Environment through Sentinel-1 SAR Data: A Case Study in the Veneto Region (Italy). ISPRS Int. J. Geo-Inf., 9, 375, 2020. https://doi.org/10.3390/ijgi9060375
  • D. Colson, G.P. Petropoulos, and K.P. Ferentinos, Exploring the potential of Sentinels-1 & 2 of the Copernicus Mission in support of rapid and cost-effective wildfire assessment. International journal of applied earth observation and geoinformation, 73: p. 262-276, 2018. https://doi.org/10.1016/j.jag.2018.06.011
  • H. Karabörk, H. B. Makineci, O. Orhan, and P. Karakus, Accuracy assessment of DEMs derived from multiple SAR data using the InSAR technique. Arabian Journal for Science and Engineering, 46, 5755-5765, 2021. https://doi.org/10.1007/s13369-020-05128-8
  • S. Niculescu, J. Xia, D. Roberts, and A. Billey, Rotation Forests and Random Forest classifiers for monitoring of vegetation in Pays de Brest (France). The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 727-732, 2020. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-727-2020
  • T. T. H. Nguyen, T. N. Q. Chau, T. A. Pham, T. X. P. Tran, T. H. Phan, and T. M. T. Pham, Mapping Land use/land cover using a combination of Radar Sentinel-1A and Sentinel-2A optical images. In IOP Conference Series: Earth and Environmental Science (Vol. 652, No. 1, p. 012021). IOP Publishing, 2021. https://doi:10.1088/1755-1315/652/1/012021
  • A. R. As-Syakur, I. W. S. Adnyana, I. W. Arthana, and I. W. Nuarsa, Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote sensing, 4(10), 2957-2970, 2012. https://doi.org/10.3390/rs4102957
  • P. Ettehadi Osgouei, S. Kaya, E. Sertel, and U. Alganci, Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery. Remote Sensing, 11(3), 345, 2019. https://doi.org/10.3390/rs11030345
  • C. Yang, Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example. Engineering, 6(5): p. 528-532, 2020. https://doi.org/10.1016/j.eng.2019.10.015
  • K. Getu, and H.G. Bhat, Analysis of spatio-temporal dynamics of urban sprawl and growth pattern using geospatial technologies and landscape metrics in Bahir Dar, Northwest Ethiopia. Land Use Policy, 109: p. 105676, 2021. https://doi.org/10.1016/j.landusepol.2021.105676
  • E. Ustaoglu, and A.C. Aydınoglu, Suitability evaluation of urban construction land in Pendik district of Istanbul, Turkey. Land Use Policy, 99: p. 104783, 2020. https://doi.org/10.1016/j.landusepol.2020.104783
  • M.C. Peel, B.L. Finlayson, and T.A. McMahon, Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., 11(5): p. 1633-1644, 2007. https://doi.org/10.5194/hess-11-1633-2007
  • T. Kindap, A severe sea-effect snow episode over the city of Istanbul. Natural Hazards, 54(3): p. 707-723, 2010. https://doi.org/10.1007/s11069-009-9496-7
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  • D. Amitrano, G. Di Martino, R. Guida P. Iervolino, A. Iodice, M. N. Papa, D. Riccio, and G. Ruello, Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications. Remote Sens., 13, 604, 2021. https://doi.org/10.3390/rs13040604
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  • M. Stasolla, and P. Gamba, Spatial indexes for the extraction of formal and informal human settlements from high-resolution SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(2): p. 98-106, 2008. https://doi.org/10.1109/JSTARS.2008.921099
  • W. K. Baek, and H. S. Jung, Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry: Application to the 2016 Kumamoto Earthquake. Engineering, 6(8): p. 927-935, 2020. https://doi.org/10.1016/j.eng.2020.06.012
  • A.W. Jacob, F. Vicente-Guijalba, C. Lopez-Martinez, J.M. Lopez-Sanchez, M. Litzinger, H. Kristen, A. Mestre-Quereda, D. Ziółkowski, M. Lavalle, C. Notarnicola, G. Suresh, O. Antropov, S. Ge, J. Praks, Y. Ban, E. Pottier, J.J. Mallorqu Franquet, J. Duro, M.E. Engdahl, Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers, IEEE J. Select. Topics Appl. Earth Observ. Remote Sens., 13, pp. 535-552, 2020. https://doi.org/10.1109/JSTARS.2019.2958847
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  • F. Calò, S. Abdikan, T. Görüm, A. Pepe, H. Kiliç, and F. Balik Şanli, The space-borne SBAS-DInSAR technique as a supporting tool for sustainable urban policies: The case of Istanbul Megacity, Turkey. Remote Sensing, 7(12), 16519-16536, 2015. https://doi.org/10.3390/rs71215842
Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Ortak Disiplinler
Yazarlar

Hasan Bilgehan Makineci 0000-0003-3627-5826

Yayımlanma Tarihi 15 Nisan 2023
Gönderilme Tarihi 12 Kasım 2022
Kabul Tarihi 17 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 2

Kaynak Göster

APA Makineci, 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, 12(2), 626-636. https://doi.org/10.28948/ngumuh.1203301
AMA Makineci HB. Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. NÖHÜ Müh. Bilim. Derg. Nisan 2023;12(2):626-636. doi:10.28948/ngumuh.1203301
Chicago Makineci, Hasan Bilgehan. “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 12, sy. 2 (Nisan 2023): 626-36. https://doi.org/10.28948/ngumuh.1203301.
EndNote Makineci HB (01 Nisan 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 12 2 626–636.
IEEE H. B. Makineci, “Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul”, NÖHÜ Müh. Bilim. Derg., c. 12, sy. 2, ss. 626–636, 2023, doi: 10.28948/ngumuh.1203301.
ISNAD Makineci, Hasan Bilgehan. “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 12/2 (Nisan 2023), 626-636. https://doi.org/10.28948/ngumuh.1203301.
JAMA Makineci HB. Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. NÖHÜ Müh. Bilim. Derg. 2023;12:626–636.
MLA Makineci, Hasan Bilgehan. “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, c. 12, sy. 2, 2023, ss. 626-3, doi:10.28948/ngumuh.1203301.
Vancouver Makineci HB. Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. NÖHÜ Müh. Bilim. Derg. 2023;12(2):626-3.

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