Satellite Imagery for Classification Analysis of Abrasion Areas on Panaitan, Banten
DOI:
https://doi.org/10.34123/jurnalasks.v17i2.850Keywords:
Abrasion, Classification, Machine learning, Panaitan, Satellite imageryAbstract
Introduction/Main Objectives: Abrasion causes severe environmental degradation and socio- economic losses, Waton and Karang Gundul Islands have already subsided due to erosion, posing risks to Panaitan Island, a national park that also faces deforestation, infrastructure development, and vegetation loss which may intensify abrasion. Background Problems: Limited spatial data on coastal abrasion in Panaitan Island hampers effective monitoring and management, highlighting the need for spatially explicit analysis. Novelty: This study identified and classified abrasion-prone areas on Panaitan Island (a rarely exposed island) with rarely variables which have impactful indices such as MVI, TCI, and LSWI. Research Methods: Landsat 8 and Sentinel-2 imagery from 2018 and 2023 were analyzed to assess changes in vegetation, mangroves, surface temperature, and soil moisture. Random Forest, Support Vector Machine, and Logistic Regression were employed to classify abrasion-prone areas. Finding/Results: The analysis revealed signs of abrasion covering 2.04 km², with Random Forest achieving the highest accuracy (82.23%) and NDVI as the most influential variable; abrasion was mainly associated with declining forest and mangrove cover, soil moisture showed weak correlation, while moderate surface temperature had a positive effect. Preventive measures such as reforestation and mangrove rehabilitation are recommended to mitigate risks and ensure long-term environmental sustainability.
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N. S. Gravitiani, E., Rahardjo, M., & Pratiwi, "Apakah turis bersedia membayar untuk mitigasi abrasi? Sebuah studi di daerah pesisir di Yogyakarta, Indonesia," Glob. J. Bus. Soc. Sci. Rev., pp. 5(2), 46-51, 2017, [Online]. Available: https://ssrn.com/abstract=3002423
D. Paprotny, P. Terefenko, A. Giza, P. Czaplinski, and M. I. Vousdoukas, "Future losses of ecosystem services due to coastal erosion in Europe," Sci. Total Environ., vol. 760, p. 144310, 2021, doi: 10.1016/j.scitotenv.2020.144310.
F. Cai, X. Su, J. Liu, B. Li, and G. Lei, "Coastal erosion in China under the condition of global climate change and measures for its prevention," Prog. Nat. Sci., vol. 19, no. 4, pp. 415-426, 2009, doi: 10.1016/j.pnsc.2008.05.034.
M. A. Marfai, Catatan kecil tentang informasi geospasial (Edisi ke-1). Badan Informasi Geospasial, 2023.
Y. Deslatama, "Tidak dirawat, 2 pulau di Banten hilang," Liputan6.com. Accessed: Feb. 20, 2025. [Online]. Available: https://www.liputan6.com/news/read/2253209/tidak-dirawat-2-pulau-di-banten-hilang
Balai Taman Nasional Ujung Kulon, "Taman Nasional Ujung Kulon," Kementerian Lingkungan Hidup dan Kehutanan Republik Indonesia. Accessed: Mar. 28, 2025. [Online]. Available: https://tnujungkulon.menlhk.go.id/
A. Hakam, B. Istijono, and T. Ophiyandri, "Vegetations as beach protection: Simulations of root protection mechanism against abrasion," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 6, pp. 2316-2321, 2017, doi: 10.18517/ijaseit.7.6.3445.
Dinas Lingkungan Hidup dan Kehutanan Prov. Banten, "Updating data luas kawasan mangrove di Provinsi Banten," 2023.
G. Kaplan and U. Avdan, "Mapping and monitoring wetlands using sentinel-2 satellite imagery," ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 4, no. 4W4, pp. 271-277, 2017, doi: 10.5194/isprs-annals-IV-4-W4-271-2017.
R. Bishop-Taylor, R. Nanson, S. Sagar, and L. Lymburner, "Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery," Remote Sens. Environ., vol. 267, no. March, p. 112734, 2021, doi: 10.1016/j.rse.2021.112734.
M. N. Nandaniko, Supriatna, and M. D. M. Manessa, "Modeling of coastline change by viewing trends from the abrasion and accretion rate in karawang regency," IEEE, pp. 0-4, 2019, doi: 10.1109/ICST47872.2019.9166274.
A. B. Baloloy, A. C. Blanco, R. R. C. Raymund Rhommel, and K. Nadaoka, "Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping," ISPRS J. Photogramm. Remote Sens., vol. 166, no. January, pp. 95-117, 2020, doi: 10.1016/j.isprsjprs.2020.06.001.
F. N. Kogan, "Global drought watch from space," Bull. Am. Meteorol. Soc., vol. 78, no. 4, pp. 621-636, 1997, doi: 10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2.
Y. Zhou et al., "Open surface water mapping algorithms: A comparison of water-related spectral indices and sensors," Water (Switzerland), vol. 9, no. 4, 2017, doi: 10.3390/w9040256.
A. Ghorbanian, S. Zaghian, R. M. Asiyabi, M. Amani, A. Mohammadzadeh, and S. Jamali, "Mangrove ecosystem mapping using sentinel-1 and sentinel-2 satellite images and random forest algorithm in google earth engine," Remote Sens., vol. 13, no. 13, pp. 1-18, 2021, doi: 10.3390/rs13132565.
E. D. Kusumaningrum, A. Saputra, and A. Nurwijayanti, "Analysis of vegetation density changes on coastline changes in the Glagah Coastal Area, Kulon Progo Regency 2018 and 2023," IOP Conf. Ser. Earth Environ. Sci., vol. 1357, no. 1, 2024, doi: 10.1088/1755-1315/1357/1/012009.
A. Rahman et al., "Remote sensing applications: Society and environment performance of different machine learning algorithms on satellite image classification in rural and urban setup," Remote Sens. Appl. Soc. Environ., vol. 20, no. July, p. 100410, 2020, doi: 10.1016/j.rsase.2020.100410.
M. Mahapatra, R. Ratheesh, and A. S. Rajawat, "Shoreline change analysis along the coast of South Gujarat, India, using digital shoreline analysis system," J. Indian Soc. Remote Sens., vol. 42, no. 4, pp. 869-876, 2014, doi: 10.1007/s12524-013-0334-8.
D. N. D. Pratama, N. Khakhim, A. Wicaksono, A. Musthofa, and W. Lazuardi, "Spatio-temporal analysis of abrasion susceptibility effect on land cover in the coastal area of Bantul regency, Yogyakarta, Indonesia," Int. J. Geoinformatics, vol. 17, no. 4, pp. 109-126, 2021, doi: 10.52939/ijg.v17i4.1961.
B. Deepika, K. Avinash, and K. S. Jayappa, "Shoreline change rate estimation and its forecast: Remote sensing, geographical information system and statistics-based approach," Int. J. Environ. Sci. Technol., vol. 11, no. 2, pp. 395-416, 2014, doi: 10.1007/s13762-013-0196-1.
T. M. Berhane et al., "Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory," Remote Sens., vol. 10, no. 4, 2018, doi: 10.3390/rs10040580.
I. N. Hidayati, R. Suharyadi, and P. Danoedoro, "Kombinasi indeks citra untuk analisis lahan terbangun dan vegetasi perkotaan," Majalah Geografi Indonesia, vol. 32, no. 1, p. 24, 2018. [Online]. Available: https://doi.org/10.22146/mgi.31899
R. H. J. Bagus Pamungkas, "Pemanfaatan citra pengindraan jarak jauh dan sistem informasi geografis untuk pemetaan erosi di DAS Serang Kabupaten Kulon Progo," Jurnal Bumi Indonesia, vol. 15, no. 1, pp. 165-175, 2016.
M. Conopio, A. B. Baloloy, J. Medina, and A. C. Blanco, "Spatio-Temporal mapping and analysis of mangrove extents around manila bay using landsat satellite imagery and mangrove vegetation index (MVI)," Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 46, no. 4/W6-2021, pp. 103-108, 2021, doi: 10.5194/isprs-Archives-XLVI-4-W6-2021- 103-2021.
M. P. Neri, A. B. Baloloy, and A. C. Blanco, "Limitation assessment and workflow refinement of the mangrove vegetation index (mvi)-based mapping methodology using sentinel-2 imagery," Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 46, no. 4/W6-2021, pp. 235-242, 2021, doi: 10.5194/isprs-Archives-XLVI-4-W6-2021-235-2021.
S. Huang, L. Tang, J. P. Hupy, Y. Wang, and G. Shao, "A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing," J. For. Res., vol. 32, no. 1, pp. 1-6, 2021, doi: 10.1007/s11676-020-01155-1.
X. Zhao, H. Xia, L. Pan, H. Song, W. Niu, R. Wang, R. Li, X. Bian, Y. Guo, and Y. Qin, "Drought monitoring over yellow river basin from 2003-2019 using reconstructed MODIS land surface temperature in google earth engine," Remote Sens., vol. 13, no. 18, 2021, doi: 10.3390/rs13183748.
J. Brema, T. S. Rahul, and J. J. Julius, Study on Drought Monitoring Based on Spectral Indices in Noyyal River Sub-watershed Using Landsat-8 Imageries. Springer International Publishing, 2019. doi: 10.1007/978-3-319-77276-9_42.
M. W. Browne, "Cross-validation methods. Journal of Mathematical Psychology," J. Math. Psychol., vol. 44, pp. 108-132, 2000.
N. Shrestha, "Detecting multicollinearity in regression analysis," Am. J. Appl. Math. Stat., vol. 8, no. 2, pp. 39-42, 2020, doi: 10.12691/ajams-8-2-1.
L. Breiman, "Support vector machines for classification and regression. Analytical Methods," Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 6, pp. 135(2), 230-240, 2001, doi: 10.1039/b918972f.
A. Fernandez, S. Garcia, F. Herrera, and N. V. Chawla, "SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary," J. Artif. Intell. Res., vol. 61, pp. 863-905, 2018, doi: 10.1613/jair.1.11192.
J. R. Wilson and K. A. Lorenz, "Short history of the logistic regression model," pp. 17-23, 2015, doi: 10.1007/978-3-319-23805-0_2.
W. P. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell. Res., pp. 16, 321-357, 2002, doi: 10.1613/jair.953.
K. Ichii, A. Kawabata, and Y. Yamaguchi, "Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990," International Journal of Remote Sensing, vol. 23, no.
Z. Feng, X. Wang, J. Yuan, Y. Zhang, and M. Yu, "Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations," Science of the Total Environment, vol. 892, art. no. 164496, 2023.
P. I. Frazier, "A tutorial on bayesian optimization," CoRR, vol. abs/1807.02811, Jul. 2018.18, pp. 3873-3878, 2002.
M. A. Wojtas and K. Chen, "Feature importance ranking for deep learning," Adv. Neural Inf. Process. Syst., vol. 2020-Decem, no. NeurIPS, 2020.
D. C. Donato, J. B. Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham, and M. Kanninen, "Mangroves among the most carbon-rich forests in the tropics," Nat. Geosci., vol. 4, no. 5, pp. 293-297, 2011, doi: 10.1038/ngeo1123.
