LAND COVER CHANGE ANALYSIS IN TERONG TAWAH USING GOOGLE EARTH ENGINE
DOI:
https://doi.org/10.33019/fraction.v6i1.111Keywords:
Land Cover Change, Google Earth Engine, Random ForestAbstract
This study analyzes land cover changes in Terong Tawah Village, West Lombok using Google Earth Engine (GEE) as the main platform. Landsat 8 satellite imagery data for 2015, 2018, 2022, and 2023 was used to classify five types of land cover: forests, settlements, fields, vacant land, and roads. The methods applied include data preprocessing, classification using the Random Forest algorithm, and spatial change analysis. The results show significant trends, such as an increase in settlement area and a decrease in vegetation. Validation of classification accuracy results in an overall accuracy value of above 93% per year. This study demonstrates the effectiveness and accuracy of GEE in analyzing land cover changes and provides important insights into development dynamics in Terong Tawah Village.
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[1] Arisondang, V., Sudarsono, B., & Prasetyo, Y. (2015). Klasifikasi Tutupan Lahan Menggunakan Metode Segmentasi Berbasis Algoritma Multiresolusi. 4, 9– 19
[2] Bounoua, L., DeFries, R., Collatz, G. J., Sellers, P., & Khan, H. (2002). Effects of land cover conversion on surface climate. Climatic Change, 52(1–2), 29– 64. https://doi.org/10.1023/A:1013051420309
[3] BPS. (2016). Badan Pusat Statistik Kota Semarang Dalam Angka 2016. BPS Kota Semarang, 5.
[4] Febianti, V., Sasmito, B., & Bashit, N. (2022). Jurnal Geodesi Undip Oktober 2022 ( Studi Kasus : Kota Semarang ). Jurnal Geodesi Undip, 11(2), 1–10.
[5] Fikri, A. S., Setiawan, F., Violando, W. A., Muttaqin, A. D., & Rahmawan, F. (2021). Analisis Penutupan Lahan menggunakan Google Earth Engine (GEE) dengan Metode Klasifikasi Terbimbing (Studi kasus: WIilayah Pesisir Kabupaten Lamongan, Provinsi Jawa Timur). Prosiding FIT ISI, 1, 89–99. http://eprints.itn.ac.id/1418/
[6] Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., … Chen, J. (2013).
Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7),
[7] Hansen, M. C., Defries, R. S., Townshend, J. R. G., & Sohlberg, R. (2010). Global land cover classification at 1 km spatial resolution using a classification tree approach. In International Journal of Remote Sensing (Issue July 2012)
[8] Jia, K. (2014). Land cover classification using Landsat 8 Operational Land Imager data in Land cover classi fi cation using Landsat 8 Operational Land Imager. November. https://doi.org/10.1080/10106049.2014.894586
[9] Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., & Li, B. (2014). Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China. Geocarto International, 29(8), 941–951. https://doi.org/10.1080/10106049.2014.894586
[10] Jung, M., Henkel, K., Herold, M., & Churkina, G. (2006). Exploiting synergies of global land.
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Copyright (c) 2026 Muhammad Adriyansyah, Fitrah Ramadhan, Dimas Indrawardi, Nuzla Af'idatur Robbaniyyah, Kurnia Ulfa, Muhammad Rijal Alfian
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Fraction: Jurnal Teori dan Terapan Matematika
