Dinamika perubahan tutupan lahan pascabencana likuefaksi 2018 di Kelurahan Petobo, Kota Palu

Tika Nurhasanah, Girlly Marchlina Listyono, Tanuda Pedro Rusdiono, Eko Kusratmoko

Abstract

Land use and land cover change (LULCC) is a key indicator for understanding regional spatial dynamics, particularly in disaster-affected areas. Palu City, which experienced severe liquefaction in 2018, requires detailed land cover change analysis to support disaster-responsive regional development and spatial planning. This study aims to analyze land cover change dynamics in Petobo Sub-district before and after the 2018 liquefaction event using Pleiades imagery and aerial photographs from 2017, 2018, and 2022. A quantitative remote sensing approach was applied, employing automated classification on the Google Earth Engine (GEE) platform using the Random Forest (RF) method, followed by spatial analysis using ArcGIS Pro. The classification results demonstrate high accuracy, with Kappa coefficients of 0.82 in 2017 and 2018, and 0.86 in 2022. The analysis reveals significant land cover changes due to the 2018 liquefaction, particularly a reduction in built-up areas of more than 50%, from 25.03% in 2017 to 12.16% in 2018, before increasing to 15.60% in 2022. The dominance of low vegetation cover, reaching 58.46% in 2022, reflects post-disaster land-use adaptation and policy changes. These findings confirm the effectiveness of remote sensing technologies in supporting post-disaster regional development planning.

Keywords

Aerial Photo; Google Earth Engine; Land Cover Change; Liquefaction; Pleiades Imagery

Full Text:

PDF

References

[1]Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, et al. The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change 2001;11:261–9.

[2]Anissa AC, Rini EF, Soedwiwahjono S. Analisis perbandingan perubahan tutupan lahan menggunakan Citra Satelit Landsat 8 di Kecamatan Tawangmangu. Region: Jurnal Pembangunan Wilayah Dan Perencanaan Partisipatif 2024;19:184–99.

[3]Muslim S, Utomo RP, Permana CTH. Perubahan penggunaan lahan dan dan pola spasial tutupan lahan di sekitar Kawasan Industri Purwosuman, Sragen. Region: Jurnal Pembangunan Wilayah Dan Perencanaan Partisipatif 2023;18:38.

[4]Nath B, Niu Z, Singh RP. Land Use and Land Cover changes, and environment and risk evaluation of Dujiangyan city (SW China) using remote sensing and GIS techniques. Sustainability 2018;10:4631.

[5]Karadeniz E, Sunbul F. Land use and land cover change in Duzce region following the major earthquake: implications for ANN and Markov Chain Analysis. Environmental Earth Sciences 2023;82:243.

[6]Chen Y, Nakatsugawa M. Analysis of changes in land use/land cover and hydrological processes caused by earthquakes in the Atsuma River Basin in Japan. Sustainability 2021;13:13041.

[7]Syahbana AJ, Sugianti K. Pemodelan 2D perubahan tekanan air pori hubungannya dengan Likuifaksi: Studi kasus Kabupaten Bantul, Daerah Istimewa Yogyakarta. Jurnal Lingkungan Dan Bencana Geologi 2013;4:163–77.

[8]Juang CH, Ching J, Luo Z. Assessing SPT-based probabilistic models for liquefaction potential evaluation: a 10-year update. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 2013;7:137–50.

[9]Iqbal P, Tohari A, Sadisun IA, Nugroho D. Fasies Sedimen Kuarter berpotensi likuifaksi Pesisir Kota Padang, Provinsi Sumatra Barat berdasarkan Data Inti Bor dan CPTu. Jurnal Lingkungan Dan Bencana Geologi 2014;5:1–18.

[10]Thein PS, Pramumijoyo S, Brotopuspito KS, Kiyono J, Wilopo W, Furukawa A, et al. Estimation of seismic ground motion and shaking parameters based on microtremor measurements at Palu city, Central Sulawesi province, Indonesia. International Journal of Geological and Environmental Engineering 2014;8:308–19.

[11]Jalil A, Fathani TF, Satyarno I, Wilopo W. Liquefaction in Palu: the cause of massive mudflows. Geoenvironmental Disasters 2021;8:21.

[12]Hazarika H, Rohit D, Pasha SMK, Maeda T, Masyhur I, Arsyad A, et al. Large distance flow-slide at Jono-Oge due to the 2018 Sulawesi Earthquake, Indonesia. Soils and Foundations 2021;61:239–55.

[13]Muslim S, Utomo RP, Permana CTH. Perubahan penggunaan lahan dan dan pola spasial tutupan lahan di sekitar Kawasan Industri Purwosuman, Sragen. Region: Jurnal Pembangunan Wilayah Dan Perencanaan Partisipatif 2023;18:38.

[14]Nath B, Niu Z, Singh RP. Land Use and Land Cover changes, and environment and risk evaluation of Dujiangyan city (SW China) using remote sensing and GIS techniques. Sustainability 2018;10:4631.

[15]Sainuddin S. Analisis Dampak Kerusakan Infrastruktur Akibat Likuifaksi Di Palu. Jurnal Azimut 2022;4:78–84.

[16]Bashiir MF, Kurniadin N. Deteksi Kerusakan Perkotaan Akibat Gempa Bumi di Kota Palu Menggunakan Data Satelit Sentinel-1. Buletin Poltanesa 2021;22:66–9.

[17]Martinez SL, See L, Yordanov M, Verhegghen A, Elvekjaer N, Muraro D, et al. Automatic classification of land cover from LUCAS in-situ landscape photos using semantic segmentation and a Random Forest model. Environmental Modelling & Software 2024.

[18]Faheem Z, Kazmi JH, Shaikh S, Arshad S, Mohammed S. Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan. Ecological Indicators 2024;159:111670.

[19]Kasahun M, Legesse A. Machine learning for urban land use/cover mapping: Comparison of artificial neural network, random forest and support vector machine, a case study of Dilla town. Heliyon 2024;10.

[20]Aziz F, Kusratmoko E, Manessa MDM. Google earth engine application for estimating changes in water surface area of Lake Toba. IOP Conference Series: Earth and Environmental Science, vol. 500, IOP Publishing; 2020, p. 012028.

[21]Guo F, Fan L, Zhang C, Xue S. A novel space–spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China. Ecological Informatics 2024;81:102607. https://doi.org/https://doi.org/10.1016/j.ecoinf.2024.102607.

[22]Patel A, Vyas D, Chaudhari N, Patel R, Patel K, Mehta D. Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city. Results in Engineering 2024;21:101788. https://doi.org/https://doi.org/10.1016/j.rineng.2024.101788.

[23]Farhan M, Wu T, Amin M, Tariq A, Guluzade R, Alzahrani H. Monitoring and prediction of the LULC change dynamics using time series remote sensing data with Google Earth Engine. Physics and Chemistry of the Earth, Parts A/B/C 2024;136:103689. https://doi.org/https://doi.org/10.1016/j.pce.2024.103689.

[24]Sumarsono M, Sukojo B. Analisa Ketelitian Orthorektifikasi Citra Pleiades untuk Pembuatan Peta Rencana Detail Tata Ruang Terbuka Hijau (Studi Kasus: Kota Surabaya). Jurnal Teknik ITS 2016;5. https://doi.org/10.12962/j23373539.v5i2.17167.

[25]Phan T-N, Kuch V, Lehnert L. Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition. Remote Sensing 2020. https://doi.org/10.3390/rs12152411.

[26]Putri K, Handayani H. Perbandingan Hasil Klasifikasi Tutupan Lahan Menggunakan Metode Artificial Neural Network (ANN), Support Vector Machine (SVM), dan Random Forest (RF) dengan Bahasa Pemrograman R. GEOID 2024;19:349–60. https://doi.org/10.12962/geoid.v19i2.1145.

[27]Islami FA, Tarigan SD, Wahjunie ED, Dasanto BD. Accuracy Assessment of Land Use Change Analysis Using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conference Series: Earth and Environmental Science 2022;950:012091. https://doi.org/10.1088/1755-1315/950/1/012091.

[28]Sadono R, Hartono H, Machfoedz M, Setiaji S. Monitoring Land Cover Changes in the Disaster-Prone Area: A Case Study of Cangkringan Sub-District, the Flanks of Mount Merapi, Indonesia. Forum Geografi 2017;31. https://doi.org/10.23917/forgeo.v31i2.5324.

[29]Chrysilla F, Komariah S, Wulandari P. Kelas Sosial dan Budaya Konsumtif dalam Ruang Lingkup Masyarakat Metropolis dan Tradisional: Teori Perubahan Sosial. SOSIETAS 2023;13:117–23. https://doi.org/10.17509/sosietas.v13i2.57708.

[30]Marwati A, Prasetyo Y, Suprayogi A. Analisis Perbandingan Klasifikasi Tutupan Lahan Kombinasi Data Point Cloud Lidar Dan Foto Udara Berbasis Metode Segmentasi Dan Supervised. Jurnal Geodesi Undip 2018;7:36–45.

[31]Rezki D. Pemetaan Risiko Likuifaksi Menggunakan Metode Analytical Hierarchy Process (AHP) Dan Weighted Overlay Di Kota Palu, Sulawesi Tengah. Jurnal Teknik Geologi : Jurnal Ilmu Pengetahuan Dan Teknologi 2023;6:1. https://doi.org/10.30872/jtgeo.v6i1.12204.

[32]Chen Y, Nakatsugawa M. Analysis of changes in land use/land cover and hydrological processes caused by earthquakes in the Atsuma River Basin in Japan. Sustainability 2021;13:13041.

[33]Rafsenja U, Muh L, Jaya G, Rahim S. Analisis Perbandingan Citra Landsat 8 dan Citra Sentinel 2-A untuk Mengidentifikasi Sebaran Mangrove. Jurnal Geografi Aplikasi Dan Teknologi 2020;4:63–70.

[34]Madutujuh N. “Seminar on Palu Earthquake : Rehabilitasi dan Mitigasi Pasca Bencana Gempa Palu 28 Sept 2018” Sistem Struktur dan Pondasi Bangunan Tahan Gempa, Retakan tanah, Liquifaksi, Aliran Tanah dan Gelombang Tsunami untuk daerah Palu. 2019.

[35]Anidhea NO. Identifikasi Karakteristik Struktur Tanah Dan Mitigasi Bencana Likuifaksi di Sulawesi Tengah. Proceedings of the Universitas Negeri Surabaya Physics Seminar, vol. 5, 2021, p. 144–50.

DOI: https://doi.org/10.20961/region.v21i1.97724

Refbacks

  • There are currently no refbacks.