ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR)
DOI:
https://doi.org/10.21776/ub.jtsl.2024.011.2.11Keywords:
Built-Up Areas, Google Earth Engine, Machine Learning AlgorithmsAbstract
Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.
Downloads
References
Bofana, J., Zhang, M., Nabil, M., Wu, B., Tian, F., Liu, W., Zeng, H., Zhang, N., Nangombe, S. S., Cipriano, S. A., Phiri, E., Mushore, T. D., Kaluba, P., Mashonjowa, E., & Moyo, C. 2020. Comparison Of Different Cropland Classification Methods Under Diversified Agroecological Conditions In The Zambezi River Basin. Remote Sensing 12(13). https://doi.org/10.3390/rs12132096
BPS Kabupaten Cianjur. 2024. Kabupaten Cianjur Dalam Angka 2024. BPS Kabupaten Cianjur.
Denize, J., Hubert-Moy, L., Betbeder, J., Corgne, S., Baudry, J., & Pottier, E. 2019. Evaluation of Using Sentinel-1 and -2 Time-Series To Identifywinter Land Use In Agricultural Landscapes. Remote Sensing 11(1). https://doi.org/10.3390/rs11010037
Fang, P., Zhang, X., Wei, P., Wang, Y., Zhang, H., Liu, F., & Zhao, J. 2020. The Classification Performance and Mechanism of Machine Learning Algorithms In Winter Wheat Mapping Using Sentinel-2 10 M Resolution Imagery. Applied Sciences (Switzerland) 10(15). https://doi.org/10.3390/app10155075
Fikri, A. A., Darmawan, A., Hilmanto, R., Banuwa, I. S., Agustiono, A., & Agustiana, L. 2022. Pemanfaatan Platform Google Earth Engine dalam Pemantauan Perubahan Tutupan Lahan di Taman Hutan Raya Wan Abdul Rachman. Journal ff Forest Science Avicennia 5(1): 46-57. https://doi.org/10.22219/avicennia.v5i1.19938
Khan, T.U., Mannan, A., Hacker, C.E., Ahmad, S., Siddique, M.A., Khan, B.U., Din, E.U., Chen, M., Zhang, C., Nizami, M., & Luan, X. 2021. Use of GIS and Remote Sensing Data to Understand The Impacts Of Land Use/Land Cover Changes (LULCC) on Snow Leopard (Panthera uncia) Habitat in Pakistan. Sustainability (Switzerland) 13(7). https://doi.org/10.3390/su13073590
Macarringue, L.S., Bolfe, É.L., Duverger, S.G., Sano, E.E., Caldas, M.M., Ferreira, M.C., Zullo J.J. & Matias, L.F. 2023. Land Use and Land Cover Classification in The Northern Region of Mozambique Based on Landsat Time Series and Machine Learning. ISPRS International Journal of Geo-Information 12(8). https://doi.org/10.3390/ijgi12080342
Mardalena, A., Astuty, Y.I., & Wibowo, A. 2023. Analisis Spasial Temporal Lahan Terbangun Terhadap Rawan Bencana Gempa Bumi di Kabupaten Cianjur. Geodika: Jurnal Kajian Ilmu Dan Pendidikan Geografi 7(1): 86-95. https://doi.org/10.29408/geodika.v7i1.10558
Noraini, A. & Mabrur, Y.A. 2020. Perbandingan Visualisasi Hasil Deteksi Area Terbangun Berdasarkan Metode Maximum Likelihood Classification (MLC) dan Normalized Difference Built-Up Index (Ndbi). Buletin Loupe16(01). https://doi.org/10.51967/buletinloupe.v16i01.113
Novianti, T.C. 2021. Klasifikasi Landsat 8 Oli untuk Tutupan Lahan Di Kota Palembang Menggunakan Google Earth Engine. Jurnal Swarnabhumi 6(1).
Ouchra, H., Belangour, A., & Erraissi, A. 2023. Machine Learning Algorithms For Satellite Image Classification Using Google Earth Engine And Landsat Satellite Data: Morocco Case Study. IEEE Access 11: 71127-71142. https://doi.org/10.1109/ACCESS.2023.3293828
Palanisamy, P.A., Jain, K., & Bonafoni, S. 2023. Machine
Learning Classifier Evaluation for Different Input Combinations: A Case Study With Landsat 9 and Sentinel-2 Data. Remote Sensing 15(13). https://doi.org/10.3390/rs15133241
Phan, N.T., Kuch, V., & Lehnert, L.W. 2020. Land Cover Classification Using Google Earth Engine And Random Forest Classifier-The Role of Image Composition. Remote Sensing 12(15). https://doi.org/10.3390/rs12152411
Pratama, R. M. & Riana, D. 2022. Klasifikasi Penutupan Lahan Menggunakan Google Earth Engine dengan Metode Klasifikasi Terbimbing pada Wilayah Penajam Paser Utara. Jurnal Jupiter 1(1): 637-650.
Setiawan, H., Manessa, M.D.M., & Setiadi, H. 2023. Vegetation Indices From Landsat-8 Data in Palabuhanratu. International Journal of Remote Sensing and Earth Sciences (IJRESES) 20(1): 26. https://doi.org/10.30536/j.ijreses.2023.v20.a3829
Sianturi, R. 2022. Komparasi Metode Klasifikasi Tersupervisi untuk Pemetaan Lahan Terbangun dan Nonterbangun Menggunakan Landsat 8 Oli dan Google Earth Engine (Studi Kasus: Kota Malang). Jurnal Penataan Ruang 17(2). https://doi.org/10.12962/j2716179X.v17i2.13434
Sujud, L., Jaafar, H., Hassan, H.M.A., & Zurayk, R. 2021. Cannabis Detection From Optical And Radar Data Fusion: A Comparative Analysis Of The Smile Machine Learning Algorithms In Google Earth Engine. Remote Sensing Applications: Society And Environment 24. https://doi.org/10.1016/j.rsase.2021.100639
Suwanlee, S.R., Keawsomsee, S., Pengjunsang, M., Homtong, N., Prakobya, A., Borgogno-Mondino, E., Sarvia, F., & Som-Ard, J. 2023. Monitoring Agricultural Land and Land Cover Change from 2001-2021 of The Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine. Remote Sensing 15(17). https://doi.org/10.3390/rs15174339
Umar, I., Pramudya, B., & Barus, D.B. 2017. Evaluation For Suitability Land Of Settletment Area By Using Multi Criteria Evaluation Method In Padang. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan 7(2): 148-154. https://doi.org/10.29244/jpsl.7.2.148-154
Wang, M., Mao, D., Wang, Y., Song, K., Yan, H., Jia, M., & Wang, Z. 2022. Annual Wetland Mapping In Metropolis By Temporal Sample Migration And Random Forest Classification With Time Series Landsat Data And Google Earth Engine. Remote Sensing 14(13). https://doi.org/10.3390/rs14133191
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Jurnal Tanah dan Sumberdaya Lahan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The copyright of the received article shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
