Analisis Perubahan Penutup Lahan Berbasis Citra Sentinel-2a Di Kecamatan Ubud Tahun 2020-2025

Authors

  • Niatus Sholikhah Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Putra Ardi Hamzah Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Dian Zumrotin Khoiriyah Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Indriani Anggita Marsyilia Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Zahra Amelia Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Razita Dwi Khairina Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Alfiyatus Shufiyah Geography Education Department, Social and Political Science Faculty, Universitas Negeri Surabaya
  • Aldea Noor Alina, S.T., M.T. Geographic Information Science Department, Social and Political Science Faculty, Universitas Negeri Surabaya

DOI:

https://doi.org/10.36456/de4m7e66

Keywords:

klasifikasi, maximum likelihood, penutup lahan

Abstract

Perubahan penutup lahan merupakan fenomena yang dipengaruhi oelh perkembangan aktifitas manusia dan berdampak pada kondisi lingkungan. Kecamatan Ubud sebagai kawasan pariwisata di Kabupaten Gianyar mengalami perkembangan infrastruktur dan aktifitas ekonomi yang berpotensi mengubah penutup lahannya. Penelitian ini bertujuan untuk menganalisis perubahan penutup lahan tahun 2020-2025 menggunakan citra Sentinel-2A yang diperoleh melalui Google Earth Engine. Klasifikasi dilakukan dengan metode Maximum Likelihood Classification, sedangkan uji akurasi menggunakan Compute Confusion Matrix berdasarkan Parameter Overall Accuracy, User Accuracy, Producer Accuracy, dan Kappa Accuracy. Hasil klasifikasi menghasilkan empat kelas penutup lahan, yaitu sawah, permukiman, badan air, dan vegetasi lain, dengan nilai OA sebesar 92% dan Kappa sebesar 0,90 yang menunjukkan akurasi sangat kuat. Analisis ini menunjukkan luas sawah berkurang 190,200 ha, sedangkan badan air bertambah 448,314 ha selama periode penelitian. Hasil penelitian menunjukkan bahwa metode ML efektif untuk memetakan dan menganalisis dinamika perubahan penutup lahan di Kecamatan Ubud.

References

[1] D. Phiri, M. Simwanda, S. Salekin, V. R. Nyirendra, Y. Murayama, and M. Ranagalage, “remote sensing Sentinel-2 Data for Land Cover / Use Mapping : A Review,” Remote Sens., vol. 12, no. 2291, pp. 1–35, 2020.

[2] F. L. Hakim, T. Osawa, and I. W. S. Adnyana, “SHORELINE CHANGE ANALYSIS USING DIGITAL SHORELINE ANALYSIS SYSTEM METHOD IN SOUTHEAST BALI ISLAND,” ECOTROPHIC, vol. 15, no. 1, pp. 61–74, 2021.

[3] Badan Pusat Statistik Kabupaten Gianyar, “Kecamatan Ubud Dalam Angka 2020,” Gianyar, 2020. [Online]. Available: https://gianyarkab.bps.go.id

[4] Badan Pusat Statistik Kabupaten Gianyar, “Kecamatan ubud dalam angka 2025,” Gianyar, 2025. [Online]. Available: https://gianyarkab.bps.go.id

[5] I. L. Sari, C. J. Weston, G. J. Newnham, and L. Volkova, “Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia,” Remote Sens., vol. 13, no. 1446, pp. 1–18, 2021.

[6] A. Tarko, N. Tsendbazar, S. De Bruin, and K. B. Arnold, “Producing consistent visually interpreted land cover reference data : learning from feedback,” Int. J. Digit. Earth, vol. 14, no. 1, pp. 52–70, 2021, doi: 10.1080/17538947.2020.1729878.

[7] A. Arfa and M. Minaei, “ScienceDirect Utilizing multitemporal indices and spectral bands of Sentinel-2 to enhance land use and land cover classification with random forest and support vector machine,” Adv. Sp. Res., vol. 74, pp. 5580–5590, 2024, doi: 10.1016/j.asr.2024.08.062.

[8] I. N. P. Indrawan, Widiatmaka, and B. H. Trisasongko, “Land use land cover change in Badung Regency , Bali,” IOP Conf. Ser. Earth Environ. Sci., 2022, doi: 10.1088/1755-1315/950/1/012096.

[9] S. Talukdar, P. Singha, S. Mahato, S. Pal, Y.-A. Liou, and A. Rahman, “Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations — A Review,” Remote Sens., vol. 12, pp. 1–24, 2020.

[10] L. M. D. A. Sutantri and I. N. S. Wijaya, “Pengaruh perkembangan pariwisata terhadap perubahan penggunaan lahan pertanian di kecamatan ubud,” Plan. Urban Reg. Environ., vol. 10, no. 1, pp. 113–124, 2021.

[11] P. Potapov et al., “Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping,” Remote Sens., vol. 12, no. 426, pp. 1–24, 2020.

[12] A. N. Alina, L. M. Jaelani, N. Hayati, and M. A. Syariz, “Accuracy Results Comparison of Machine Learning Classification for Mangroves on Worldview-2 Satellite Imagery,” IEEE Asia-Pacific Conf. Geosci. Electron. Remote Sens. Technol., pp. 24–31, 2024, doi: 10.1109/AGERS65212.2024.10932968.

[13] V. Rashidian, L. G. Baise, and M. Koch, “Using High Resolution Optical Imagery to Detect Christchurch Earthquake,” Remote Sens., vol. 12, no. 377, pp. 1–20, 2020, doi: 10.3390/rs12030377.

[14] R. F. B. Marujo, J. G. Fronza, A. R. Soares, G. R. Queiroz, and K. R. Ferreira, “EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8 / OLI AND SENTINEL-2 / MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY,” SPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. V, no. 3, pp. 271–277, 2021.

[15] F. Pech-May, R. Aquino-Santos, G. Rios-Toledo, and J. P. F. Posadas-Durán, “Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine,” Sensors, vol. 22, no. 4729, pp. 1–19, 2022.

[16] S. Yousefi et al., “Image Classification and Land Cover Mapping Using Sentinel-2 Imagery : Optimization of SVM Parameters,” land, vol. 11, no. 993, pp. 1–14, 2022.

[17] M. Amani et al., “Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications : A Comprehensive Review,” IEEE Internet Things J., vol. 8, no. 12, pp. 1–26, 2020, doi: 10.1109/JSTARS.2020.3021052.

[18] R. Kwok, “Remote Sensing of Environment Declassi fi ed high-resolution visible imagery for Arctic sea ice investigations : An overview,” Remote Sens. Environ., vol. 142, pp. 44–56, 2014, doi: 10.1016/j.rse.2013.11.015.

[19] J. Kang, Z. Wang, L. Sui, X. Yang, and Y. Ma, “Consistency Analysis of Remote Sensing Land Cover Products in the Tropical Rainforest Climate Region : A Case Study of Indonesia,” Remote Sens., vol. 12, no. 1410, pp. 1–28, 2020.

[20] K. Cheng, J. Ling, T. Lin, Y. Liu, and Y. Shen, “Quantifying Uncertainty in Land-Use / Land-Cover Classi fi cation Accuracy : A Stochastic Simulation Approach,” Front. Environ. Sci., vol. 9, pp. 1–18, 2021, doi: 10.3389/fenvs.2021.628214.

Downloads

Published

2026-10-31