DOI: https://doi.org/10.14710/geoplanning.12.1.%25p

Coastal Metropolitan Dynamics in Poland's Tri-City and Indonesia's Semarang: NTL, BLFEI, and OBIA in Google Earth Engine

*Abdurrahman Zaki scopus  -  Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Poland, Poland
Joanna Jaskuła orcid scopus  -  Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Poland, Poland

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Abstract
The increasing global urbanization, particularly in coastal regions, coupled with the risks of climate change and land subsidence, underscores the need to monitor coastal urban development for sustainability. This study focused on the coastal metropolitan regions of Poland's Tri-City and Indonesia's Semarang, employing GIS, remote sensing (RS), and cloud computing. By integrating nighttime light (NTL) and the Built-Up Land Features Extraction Index (BLFEI) through Google Earth Engine (GEE) and Object-Based Image Analysis (OBIA), the study aimed to gain insights into urban development trends. The methodology encompassed image collection, analysis, and classification over three decades (1992, 2007, 2022). Despite efforts to enhance accuracy through built-up masking in subsequent years, the methodology achieved an overall accuracy of 95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 to 0.90) in comparison. The analysis revealed a gradual expansion of built-up areas in both regions, with Gdynia and Gdańsk emerging as primary drivers in the Tri-City metropolitan region and Semarang as the primary driver in the Semarang metropolitan region. Notably, the Semarang metropolitan region exhibited an increase in waterbody areas, attributed to coastal flooding and land subsidence challenges.
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Keywords: urbanization; coastal metropolitan; data fusion; OBIA

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Section: Original Research
Language : EN
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  1. Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.1650447
  2. Adnani, A. El, Habib, A., Khalidi, K. El, & Zourarah, B. (2019). Spatio-Temporal Dynamics and Evolution of Land Use Land Cover Using Remote Sensing and GIS in Sebou Estuary, Morocco. Journal of Geographic Information System, 11(05), 551–566. https://doi.org/10.4236/jgis.2019.115034
  3. Ahmed, S., Huifang, W., Akhtar, S., Imran, S., Hassan, G., & Wang, C. (2021). An analysis of urban sprawl in Pakistan: consequences, challenges, and the way forward. International Journal of Agricultural Extension, 8(3), 257–278. https://doi.org/10.33687/008.03.3438
  4. Berliana S., S., Susanti, I., Siswanto, B., Nurlatifah, A., Latifah, H., Witono, A., Slamet, L., & Suhermat, M. (2021). Analysis of wet and dry season by using the Palmer Drought Severity Index (PDSI) over Java Island. The 2nd Science and Mathematics International Conference (SMIC 2020), 030010. https://doi.org/10.1063/5.0041843
  5. Berman, M., Baztan, J., Kofinas, G., Vanderlinden, J.-P., Chouinard, O., Huctin, J.-M., Kane, A., Mazé, C., Nikulkina, I., & Thomson, K. (2020). Adaptation to climate change in coastal communities: findings from seven sites on four continents. Climatic Change, 159(1), 1–16. https://doi.org/10.1007/s10584-019-02571-x
  6. Bouhennache, R., Bouden, T., Taleb-Ahmed, A., & Cheddad, A. (2019). A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto International, 34(14), 1531–1551. https://doi.org/10.1080/10106049.2018.1497094
  7. Buchori, I., Pramitasari, A., Pangi, P., Sugiri, A., Maryono, M., Basuki, Y., & Sejati, A. W. (2021). Factors distinguishing the decision to migrate from the flooded and inundated community of Sayung, Demak: A suburban area of Semarang City, Indonesia. International Journal of Disaster Risk Reduction, 52, 101946. https://doi.org/10.1016/j.ijdrr.2020.101946
  8. Buchori, I., Sugiri, A., Mussadun, M., Wadley, D., Liu, Y., Pramitasari, A., & Pamungkas, I. T. D. (2018). A predictive model to assess spatial planning in addressing hydro-meteorological hazards: A case study of Semarang City, Indonesia. International Journal of Disaster Risk Reduction, 27, 415–426. https://doi.org/10.1016/j.ijdrr.2017.11.003
  9. Choi, J. Y., Jeong, H., Choi, K.-Y., Hong, G. H., Yang, D. B., Kim, K., & Ra, K. (2020). Source identification and implications of heavy metals in urban roads for the coastal pollution in a beach town, Busan, Korea. Marine Pollution Bulletin, 161, 111724. https://doi.org/10.1016/j.marpolbul.2020.111724
  10. Cian, F., Blasco, J., & Carrera, L. (2019). Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS. Geosciences, 9(3), 124. https://doi.org/10.3390/geosciences9030124
  11. Clement, M. T., & Pino, N. W. (2023). Is urbanization sustainable? A longitudinal study of developing nations, 1990-2015. Environmental Sociology, 9(3), 327–347. https://doi.org/10.1080/23251042.2023.2211321
  12. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran. Sustainable Cities and Society, 47, 101502. https://doi.org/10.1016/j.scs.2019.101502
  13. Darren How, J. A., Mohd Hasmadi, I., & Farrah Melissa, M. (2020). Assessing Land-Use and Land-Cover Change (LULCC) Between 2009 and 2019 Using Object-Based Image Analysis (OBIA) in Cameron Highlands, Malaysia. IOP Conference Series: Earth and Environmental Science, 540(1), 012002. https://doi.org/10.1088/1755-1315/540/1/012002
  14. Das, R. C., Chatterjee, T., & Ivaldi, E. (2021). Sustainability of Urbanization, Non-Agricultural Output and Air Pollution in the World’s Top 20 Polluting Countries. Data, 6(6), 65. https://doi.org/10.3390/data6060065
  15. Day, J. W., Gunn, J. D., & Burger, J. R. (2021). Diminishing Opportunities for Sustainability of Coastal Cities in the Anthropocene: A Review. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.663275
  16. Esther R., A. (2022). Urbanization and Environmental Unsustainability: An Ecological Footprint Analysis for Nigeria. African Journal of Environment and Natural Science Research, 5(1), 12–24. https://doi.org/10.52589/AJENSR-WDVDCDUZ
  17. Ezadin mohammed, N., & Faraj Mustafa, A. (2022). Urbanizatition in Developing Countries. Journal of Kurdistani for Strategic Studies, 8. https://doi.org/10.54809/jkss.vi8.47
  18. Fahad, K. H., Hussein, S., & Dibs, H. (2020). Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques. IOP Conference Series: Materials Science and Engineering, 671(1), 012046. https://doi.org/10.1088/1757-899X/671/1/012046
  19. Giannakis, I., Bhardwaj, A., Sam, L., & Leontidis, G. (2023). Deep learning universal crater detection using Segment Anything Model (SAM). https://doi.org/https://doi.org/10.48550/arXiv.2304.07764
  20. Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A. K., Cheng, W.-H., & Balling, R. C. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026
  21. Gumel, I. A., Aplin, P., Marston, C. G., & Morley, J. (2020). Time-Series Satellite Imagery Demonstrates the Progressive Failure of a City Master Plan to Control Urbanization in Abuja, Nigeria. Remote Sensing, 12(7), 1112. https://doi.org/10.3390/rs12071112
  22. Hasnine, M., & Rukhsana. (2020). An Analysis of Urban Sprawl and Prediction of Future Urban Town in Urban Area of Developing Nation: Case Study in India. Journal of the Indian Society of Remote Sensing, 48(6), 909–920. https://doi.org/10.1007/s12524-020-01123-6
  23. Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225–255. https://doi.org/10.1080/10106049.2018.1516241
  24. Hu, B., Chen, J., & Zhang, X. (2019). Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS. Sensors, 19(14), 3181. https://doi.org/10.3390/s19143181
  25. Hu, L. (2021). A Global Assessment of Coastal Marine Heatwaves and Their Relation With Coastal Urban Thermal Changes. Geophysical Research Letters, 48(9). https://doi.org/10.1029/2021GL093260
  26. Hu, Y., Yang, C., Yang, J., Li, Y., Jing, W., & Shu, S. (2021). Review on unmanned aerial vehicle remote sensing and its application in coastal ecological environment monitoring. IOP Conference Series: Earth and Environmental Science, 821(1), 012018. https://doi.org/10.1088/1755-1315/821/1/012018
  27. Junaid, M., Sun, J., Iqbal, A., Sohail, M., Zafar, S., & Khan, A. (2023). Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification. Sustainability, 15(3), 1858. https://doi.org/10.3390/su15031858
  28. Kejna, M., & Pospieszyńska, A. (2023). Variability in the occurrence of thermal seasons in Poland in 1961–2020. Meteorological Applications, 30(4). https://doi.org/10.1002/met.2132
  29. Liu, Chang, Yang, K., Bennett, M. M., Guo, Z., Cheng, L., & Li, M. (2019). Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data. Remote Sensing, 11(13), 1571. https://doi.org/10.3390/rs11131571
  30. Liu, Chenli, Li, W., Zhu, G., Zhou, H., Yan, H., & Xue, P. (2020). Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture. Remote Sensing, 12(19), 3139. https://doi.org/10.3390/rs12193139
  31. Liu, T., & Yang, X. (2015). Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography, 56, 42–54. https://doi.org/10.1016/j.apgeog.2014.10.002
  32. Luo, J., Ma, X., Chu, Q., Xie, M., & Cao, Y. (2021). Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong’an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine. ISPRS International Journal of Geo-Information, 10(7), 464. https://doi.org/10.3390/ijgi10070464
  33. Mathanraj, S., Rusli, N., & Ling, G. H. T. (2021). Applicability of the CA-Markov Model in Land-use/Land cover Change Prediction for Urban Sprawling in Batticaloa Municipal Council, Sri Lanka. IOP Conference Series: Earth and Environmental Science, 620, 012015. https://doi.org/10.1088/1755-1315/620/1/012015
  34. McGee, T. (2022). Desakota (1991). In The Horizontal Metropolis (pp. 393–413). Springer International Publishing. https://doi.org/10.1007/978-3-030-56398-1_25
  35. Muhammad, R., Zhang, W., Abbas, Z., Guo, F., & Gwiazdzinski, L. (2022). Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China. Land, 11(3), 419. https://doi.org/10.3390/land11030419
  36. Pangastuti, E. I., & Wijayanto, Y. (2021). Land cover analysis using object based image analysis based on Landsat 8 OLI images in the city of Jember. IOP Conference Series: Earth and Environmental Science, 747(1), 012047. https://doi.org/10.1088/1755-1315/747/1/012047
  37. Qi, Y., Li, H., Pang, Z., Gao, W., & Liu, C. (2022). A Case Study of the Relationship Between Vegetation Coverage and Urban Heat Island in a Coastal City by Applying Digital Twins. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.861768
  38. Qu, Y., Jevrejeva, S., Jackson, L. P., & Moore, J. C. (2019). Coastal Sea level rise around the China Seas. Global and Planetary Change, 172, 454–463. https://doi.org/10.1016/j.gloplacha.2018.11.005
  39. Ragia, L., & Krassakis, P. (2019). Monitoring the changes of the coastal areas using remote sensing data and geographic information systems. In G. Papadavid, K. Themistocleous, S. Michaelides, V. Ambrosia, & D. G. Hadjimitsis (Eds.), Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019) (p. 48). SPIE. https://doi.org/10.1117/12.2533659
  40. Rajaoalison, H., & Knez, D. (2021). Current trends in land subsidence of the North-Central part of Poland using DInSAR technique. E3S Web of Conferences, 266, 03006. https://doi.org/10.1051/e3sconf/202126603006
  41. Raju, V. N. G., Lakshmi, K. P., Jain, V. M., Kalidindi, A., & Padma, V. (2020). Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 729–735. https://doi.org/10.1109/ICSSIT48917.2020.9214160
  42. Ren, S., Luzi, F., Lahrichi, S., Kassaw, K., Collins, L. M., Bradbury, K., & Malof, J. M. (2023). Segment anything, from space? https://doi.org/arXiv:2304.13000
  43. Sanders, F. C., & Oliveira, A. C. de. (2020). Resilience of coastal cities with accumulating climate-change coupled threats; depends on the cooperation of government, experts and the citizens. IOP Conference Series: Earth and Environmental Science, 588(3), 032037. https://doi.org/10.1088/1755-1315/588/3/032037
  44. Siegel, F. R. (2020). An Example of Coastal Cities Hazard Exposure and Economics (pp. 63–69). https://doi.org/10.1007/978-3-030-22669-5_7
  45. Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/10.1016/j.asoc.2019.105524
  46. Su, L., Sharp, S. M., Pettigrove, V. J., Craig, N. J., Nan, B., Du, F., & Shi, H. (2020). Superimposed microplastic pollution in a coastal metropolis. Water Research, 168, 115140. https://doi.org/10.1016/j.watres.2019.115140
  47. Sumari, N. S., Xu, G., Ujoh, F., Korah, P. I., Ebohon, O. J., & Lyimo, N. N. (2019). A Geospatial Approach to Sustainable Urban Planning: Lessons for Morogoro Municipal Council, Tanzania. Sustainability, 11(22), 6508. https://doi.org/10.3390/su11226508
  48. Sun, L., Chen, J., Li, Q., & Huang, D. (2020). Dramatic uneven urbanization of large cities throughout the world in recent decades. Nature Communications, 11(1), 5366. https://doi.org/10.1038/s41467-020-19158-1
  49. Taherkhani, M., Vitousek, S., Barnard, P. L., Frazer, N., Anderson, T. R., & Fletcher, C. H. (2020). Sea-level rise exponentially increases coastal flood frequency. Scientific Reports, 10(1), 6466. https://doi.org/10.1038/s41598-020-62188-4
  50. United Nations. (2019a). The world population prospects 2019: highlights. https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf
  51. United Nations. (2019b). World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). United Nations. https://www.un.org/development/desa/publications/2018-revision-of-world-urbanization-prospects.html
  52. Valente, S., & Veloso-Gomes, F. (2020). Coastal climate adaptation in port-cities: adaptation deficits, barriers, and challenges ahead. Journal of Environmental Planning and Management, 63(3), 389–414. https://doi.org/10.1080/09640568.2018.1557609
  53. Viana, C. M., Girão, I., & Rocha, J. (2019). Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region. Remote Sensing, 11(9), 1104. https://doi.org/10.3390/rs11091104
  54. Vitousek, S., Buscombe, D., Vos, K., Barnard, P. L., Ritchie, A. C., & Warrick, J. A. (2023). The future of coastal monitoring through satellite remote sensing. Cambridge Prisms: Coastal Futures, 1, e10. https://doi.org/10.1017/cft.2022.4
  55. Wang, D., Zhang, J., Du, B., Tao, D., & Zhang, L. (2023). Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model. http://arxiv.org/abs/2305.02034
  56. Wang, S., Liu, Y., Feng, Y., & Lei, Z. (2021). To move or stay? A cellular automata model to predict urban growth in coastal regions amidst rising sea levels. International Journal of Digital Earth, 14(9), 1213–1235. https://doi.org/10.1080/17538947.2021.1946178
  57. Wdowinski, S., Oliver-Cabrera, T., & Fiaschi, S. (2020). Land subsidence contribution to coastal flooding hazard in southeast Florida. Proceedings of the International Association of Hydrological Sciences, 382, 207–211. https://doi.org/10.5194/piahs-382-207-2020
  58. Wojtowicz-Jankowska, D., & Bou Kalfouni, B. (2022). A Vision of Sustainable Design Concepts for Upgrading Vulnerable Coastal Areas in Light of Climate Change Impacts: A Case Study from Beirut, Lebanon. Sustainability, 14(7), 3986. https://doi.org/10.3390/su14073986
  59. Woodcock, C. E., Loveland, T. R., Herold, M., & Bauer, M. E. (2020). Transitioning from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing of Environment, 238, 111558. https://doi.org/10.1016/j.rse.2019.111558
  60. 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. https://doi.org/10.1080/01431160600589179
  61. Yadav, S., Sahu, R. K., & Prasad, S. (2022). Land Cover Cloud Analytics: from Global Services to Regional Insights. International Journal of Geoinformatics, 1285–1298. https://doi.org/10.52939/ijg.v18i6.2451
  62. Zaki, A., Buchori, I., Sejati, A. W., & Liu, Y. (2022). An object-based image analysis in QGIS for image classification and assessment of coastal spatial planning. The Egyptian Journal of Remote Sensing and Space Science, 25(2), 349–359. https://doi.org/10.1016/j.ejrs.2022.03.002
  63. Zhang, C., & Li, X. (2022). Land Use and Land Cover Mapping in the Era of Big Data. Land, 11(10), 1692. https://doi.org/10.3390/land11101692
  64. Zhang, J., Zhou, Z., Mai, G., Mu, L., Hu, M., & Li, S. (2023). Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models. http://arxiv.org/abs/2304.10597
  65. Zhang, S. (2020). Application of Remote Sensing Information Technology and Geographic Information System in Land Dynamic Monitoring. International Journal of Geology, 5(1). https://doi.org/10.26789/IJG.2020.01.004
  66. Zhang, X., & Pan, J. (2021). Spatiotemporal Pattern and Driving Factors of Urban Sprawl in China. Land, 10(11), 1275. https://doi.org/10.3390/land10111275

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