Model spatial analysis identifikasi risiko tsunami Pantai Selatan Jawa menggunakan Data Built-up Index

Wiwin Sulistyo; Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak

doi:10.24246/aiti.v23i3.465-481

Authors

Wiwin Sulistyo Universitas Kristen Satya Wacana
Sri Yulianto Joko Prasetyo Universitas Kristen Satya Wacana
Bistok Hasiholan Simanjuntak Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.24246/aiti.v23i3.465-481

Keywords:

spatial analysis, Getis-Ord statistic, satellite image, spectral vegetation indices

Abstract

The territory of Indonesia includes several areas located along the active fault line, particularly the southern coast of Java Island, and, based on the 2020 census by BNPB, has a history of tsunamis. The recurrence of such events over specific periods requires careful attention and appropriate mitigation measures. This study aims to identify and classify areas with a high risk of tsunami-related earthquakes using satellite imagery and spatial analysis. Satellite imagery was processed to derive Spectral Vegetation Index (SVI) values and several built-up and surface-characteristic indices using 3Drecons computer processing. The indices include the Index-based Built-up Index (IBI), Urban Index (UI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Built-up Index (NDBI), Normalized Built-up Area Index (NBAI), and New Built-up Index (NBI). A spatial analysis approach was subsequently applied to cluster and classify the study areas based on their spatial characteristics. The results produced clusters and classifications representing spatial patterns and corresponding risk levels across the study areas. The proposed approach provides spatial information that can support the identification of high-risk areas and contribute to data-driven tsunami risk assessment and mitigation planning.

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References

S. Y. Joko Prasetyo, B. H. Simanjuntak, Y. A. Susatyo, and W. Sulistyo, “A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 1138–1146, Apr. 2024, doi: 10.11591/eei.v13i2.5910.

A. M. Julius and Daryono, “Overview of 1990s deadly tsunamis in Indonesia,” in E3S Web of Conferences, EDP Sciences, Dec. 2021. doi: 10.1051/e3sconf/202133107001.

I. R. Pranantyo, M. Heidarzadeh, and P. R. Cummins, “Complex tsunami hazards in eastern Indonesia from seismic and non-seismic sources: Deterministic modelling based on historical and modern data,” Dec. 01, 2021, Springer Science and Business Media Deutschland GmbH. doi: 10.1186/s40562-021-00190-y.

W. Sulistyo, Subanar, and R. Pulungan, “Development of a spatial path-analysis method for spatial data analysis,” International Journal of Electrical and Computer Engineering, vol. 8, no. 4, pp. 2456–2467, Aug. 2018, doi: 10.11591/ijece.v8i4.pp2456-2467.

A. Getis and J. K. Ord, “The Analysis of Spatial Association by Use of Distance Statistics,” Geogr. Anal., vol. 24, no. 3, pp. 189–206, 1992, doi: 10.1111/j.1538-4632.1992.tb00261.x.

W. Liu, B. Jia, T. Li, Q. Zhang, and J. Ma, “Correlation Analysis between Urban Green Space and Land Surface Temperature from the Perspective of Spatial Heterogeneity: A Case Study within the Sixth Ring Road of Beijing,” Sustainability (Switzerland), vol. 14, no. 20, Oct. 2022, doi: 10.3390/su142013492.

M. Fischer and A. Getis, Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. doi: 10.1007/978-3-642-03647-7.

S. Mateen, N. Nuthammachot, K. Techato, and N. Ullah, “Billion Tree Tsunami Forests Classification Using Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2 and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan,” ISPRS Int. J. Geoinf., vol. 12, no. 1, Jan. 2023, doi: 10.3390/ijgi12010009.

W. L. Farahdita and H. S. R. Siagian, “Analysis of the area affected by the tsunami in Pandeglang, Banten: A case study of the Sunda Strait Tsunami,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Feb. 2020. doi: 10.1088/1755-1315/429/1/012052.

H. Hébert, P. E. Burg, R. Binet, F. Lavigne, S. Allgeyer, and F. Schindelé, “The 2006 July 17 Java (Indonesia) tsunami from satellite imagery and numerical modelling: A single or complex source?,” Geophys. J. Int., vol. 191, no. 3, pp. 1255–1271, Dec. 2012, doi: 10.1111/j.1365-246X.2012.05666.x.

D. P. Roy et al., “Landsat-8: Science and product vision for terrestrial global change research,” Remote Sens. Environ., vol. 145, pp. 154–172, Apr. 2014, doi: 10.1016/j.rse.2014.02.001.

R. D. Jackson, P. M. Slater, and P. J. Pinter, “Discrimination of growth and water stress in wheat by various vegetation indices through a clear a turbid atmosphere,” NTRS - NASA Technical Reports Server, 1982.

Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,” Int. J. Remote Sens., vol. 24, no. 3, pp. 583–594, Feb. 2003, doi: 10.1080/01431160304987.

F. Dwi Ramadhan, T. Hariyanto, and H. Hapsari Handayani, “Analysis of Urban Built-up Expansion Based on Combination of Spectral Indices in Surabaya City,” Geoid, vol. 17, no. 1, pp. 21–37, 2021.

H. Xu, “A new index for delineating built-up land features in satellite imagery,” Int. J. Remote Sens., vol. 29, no. 14, pp. 4269–4276, Jul. 2008, doi: 10.1080/01431160802039957.

Waqar, “Development of New Indices for Extraction of Built-Up Area & Bare Soil from Landsat Data,” 2012, doi: 10.4172/scientificreports.136.

H. Zhao and X. Chen, “Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2005, pp. 1666–1668. doi: 10.1109/igarss.2005.1526319.

C. T. Nguyen, A. Chidthaisong, P. K. Diem, and L. Z. Huo, “A modified bare soil index to identify bare land features during agricultural fallow-period in southeast asia using landsat 8,” Land (Basel)., vol. 10, no. 3, pp. 1–18, Mar. 2021, doi: 10.3390/land10030231.

L. Anselin, Spatial Econometrics: Methods and Models, vol. 4. in Studies in Operational Regional Science, vol. 4. Dordrecht: Springer Netherlands, 1988. doi: 10.1007/978-94-015-7799-1.

K. Kopczewska, Applied Spatial Statistics and Econometrics. Routledge, 2020. doi: 10.4324/9781003033219.

D. Griffith and Y. Chun, “Spatial autocorrelation and spatial filtering,” in Handbook of Regional Science, Springer Berlin Heidelberg, 2014, pp. 1477–1507. doi: 10.1007/978-3-642-23430-9_72.

Mahmudah, R. Azizah, N. Puspitasari, W. Kusumaningtyas, M. T Latif, C. Fitrahanjani, F. H. Ramadhani, “The Distribution Pattern of Covid-19 in Indonesia_ Study Using Spatial Autocorelation,” International Journal of Nursing Education, vol. 13, no. 2, pp. 48-53, 2021.

R. Anisya Faujia and M. Z. Subarkah, “Analisis Klaster K-Means Dan Visualisasi Data Spasial Berdasarkan Karakteristik Persebaran Covid-19 Dan Pelanggaran Protokol Kesehatan Di Jawa Tengah (K-Means Cluster Analysis And Spatial Data Visualization Based On The Characteristics Of The Spread Of Covid-19 And Violations Of Health Protocols In Central Java).” [Online]. Available: https://corona.jatengprov.goi.id,

D. Yu and C. Fang, “Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades,” Mar. 01, 2023, MDPI. doi: 10.3390/rs15051307.

J. P. Lesage, “The Theory and Practice of Spatial Econometrics,” Toledo, 1999. [Online]. Available: https://www.researchgate.net/publication/266218273

A. Páez and D. M. Scott, “Spatial statistics for urban analysis: A review of techniques with examples,” GeoJournal, vol. 61, no. 1, pp. 53–67, Sep. 2004, doi: 10.1007/s10708-005-0877-5.

A. Field, “Discovering Statistics Using IBM SPSS Statistics,” Sage Publications, 2013.

J. Macqueen, “Some Methods For Classification And Analysis Of Multivariate Observations,” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1: Statistics, 1967.

L. Anselin, I. Syabri, and Y. Kho, “GeoDa: An introduction to spatial data analysis,” Jan. 2006. doi: 10.1111/j.0016-7363.2005.00671.x.