Modeling of land use and cover changes (LUCC) in Deli Serdang Regency, North Sumatra Province
Abstract
Land use/cover (LUC) is a substantial factor in land management and can influence policy in an area. LUC has the potential to change due to physical, economic, and social aspects. This study aims to analyze the spatial
land use and cover changes (LUCC) in Deli Serdang Regency for the 2010 to 2020 period and predict LUC in 2030. The analysis was run by applying the Cellular Automata-Markov Chain method. The driving factors used in this modeling are the distance to the road, the distance to the river, population density, the distance to the district capital, and the distance to Medan city. The results showed that Kappa for image classification was 0.86. The dominant type of LUC in Deli Serdang Regency is a plantation, with a total area of more than 45%, followed by paddy fields, dryland agriculture, forests, and settlements/built-up areas. LUCC model validation obtained a kappa value of 0.89 (very good category) and can be applicated for predicting land use change models in 2030. By 2030, the settlements/built-up area and dryland agriculture will increase significantly, which 21,060 ha and 4,587 ha, respectively, while forests, plantations, and paddy fields will decrease significantly by around 9,266 ha, respectively, respectively 8,306 ha and 7,806 ha.
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Anderson, J.R. 1971 Land Use Classification Schemes Used in Selected Recent Geographic Applications of Remote Sensing. Photogrammetric Engineering, 37, 379-387.
[BPS] Badan Pusat Statistik. 2020. Kabupaten Deli Serdang dalam angka 2020. Deli Serdang. Badan Pusat Statistik.
[BPS] Badan Pusat Statistik. 2020. Indikator Pertanian Provinsi Sumatera Utara 2019. Sumatera Utara. Badan Pusat Statistik.
Eastman, JR. 2012. IDRISI Selva Manual. IDRISI Tutorial. s.l. Clark University, Worcester. www.clarklabs.org
El-Hallaq, M.A. and Habboub, M.O. (2015) Using Cellular Automata-Markov Analysis and Multi Criteria Evaluation for Predicting the Shape of the Dead Sea. Advances in Remote Sensing, 4, 83-95. http://dx.doi.org/10.4236/ars.2015.41008
Fadilla L, Subiyanto S, Suprayogi A. 2017. Analisis Arah dan Prediksi Persebaran Fisik Wilayah Kota Semarang Tahun 2029 Menggunakan Sistem Informasi Geografi dan CA Markov Model. Jurnal Geodesi Undip. 6(4): 517-525.
Firmansyah I. 2016. Model Pengendalian Konversi Lahan Sawah Di Dalam DAS Citarum [disertasi]. Bogor (ID). Institut Pertanian Bogor.
Firmansyah I. Widiatmaka. Pramudya B. Budiharsono S. 2015. Dimanika Spasial Tekanan Lahan Pertanian di Kawasan Pertumbuhan Baru. Jurnal Ketranmigrasian. 32(2): 73-83.
Fitriyanto BR, Helmi M, Hadiyanto. 2019. Model Prediksi Perubahan Penggunaan Lahan dengan Pendekatan Sistem Informasi Geografis dan Cellular Automata Markov Chain: Studi Kasus Kabupaten Rokan Hulu, Provinsi Riau. Jurnal Teknologi Technoscientia. 11(2): 137-147.
Handayani E, Shaleh K, Panggabean EL. 2016. Identifikasi Potensi Komoditas Unggulan Sektor Peranian Tanaman Pangan. Jurnal Ilmiah Pertanian (JIPERTA). 1(2): 106-111.
Kosasih D, Saleh MB, Prasetyo LB. 2019. Interpretasi Visual dan Digital untuk Klasifikasi Tutupan Lahan di Kabupaten Kuningan, Jawa Barat. Jurnal Ilmu Pertanian Indonesia (JIPI). 24(2): 101-108. https://doi.org/10.18343/jipi.24.2.101
Kubangun SH. 2015. Model Spasial Bahaya Lahan Kritis di Kabupaten Bogor, Cianjur, dan Sukabumi [tesis]. Bogor (ID). Institut Pertanian Bogor.
Kusumaningrat MD, Subiyanto S, Yuwono BD. 2017. Analisis Perubahan Penggunaan dan Pemanfaatan Lahan Terhadap Rencana Tata Ruang Wilayah Tahun 2009 dan 2017 (Studi kasus : Kabupaten Boyolali). Jurnal Geodesi Undip. 6(4): 443-452.
Lillesand TM, Kiefer RW. 1994. Penginderaan Jauh dan Interpretasi Citra. Yogyakarta (ID): Gadjah Mada University Press.
Lillesand, TM, Kiefer RW, Chipman JW. 2004. Remote Sensing and Image Interpretation. 5th Edition, John Wiley, New York.
Losiri C, Nagai M, Ninsawat S, Shrestha RP. 2016. Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models. Sustainability. 8(7), 686. https://doi.org/10.3390/su8070686.
Mondal Md S, Sharma N, Garg PK, Kappas M. 2016. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. The Egyptian Journal of Remote Sensing and Space Sciences. 19: 259-272. http://dx.doi.org/10.1016/j.ejrs.2016.08.001
Mulyani A, Kuncoro D, Nursyamsi D, Agus F. 2016. Analisis Konversi Lahan Sawah: Penggunaan Data Spasial Resolusi Tinggi Memperlihatkan Laju Konversi yang Mengkhawatirkan. Jurnal Tanah dan Iklim. 40(2): 121–133.
Hedge NP, Muralikrishna IV, Chalapatirao KV. 2008. Settlement growth prediction using neural network and cellular automata. Journal of Theoretical and Applied Information Technology. 419-428.
Oktaviati I, Ermatita, Rini DP. 2019. Analisis Pola Prediksi Data Time Seriesmenggunakan Support Vector Regression, Multilayer Perceptron, danRegresi Linear Sederhana. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). 3(2): 282–287.
Purwaningsi N. 2016. Penerapan multilayer perceptron untuk klasifikasi jenis kulit sapi tersamak, Jurnal TEKNOIF. 4(1):1-7.
Roseana B, Subiyanto A, Sudarsono B. 2019. Analisis Spasial Perkembangan Fisik Wilayah Kabupaten Klaten Menggunakan Sistem Informasi Geografis dan Prediksinya Tahun 2025 dengan CA Markov Model. Jurnal Geodesi Undip. 8(4): 59-68.
Rwanga SS and Ndambuki JM. 2017. Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 8, 611-622. https://doi.org/10.4236/ijg.2017.84033.
Salakory M, Rakuasa H. 2019. Modeling of Cellular Automata Markov Chain for predicting the carrying capacity of Ambon City. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 12(2): 372-387. http://dx.doi.org/10.29244/jpsl.12.2.372-387.
Sandi D. 2020. Strategi Keberlanjutan Lahan Sawah Baru di Kecamatan Lubuk Pinang Kabupaten Mukomuko [tesis]. Bogor (ID). Institut Pertanian Bogor.
Shen L, Li JB, Wheate R, Yin J, Paul SS. 2020. Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change. Journal of Environmental Informatics Letters 3(1) 29-39. https://doi.org/10.3808/jeil.202000023.
Sugiyanto TA. 2018. Model Perubahan Tutupan/Penggunaan Lahan Dan Arahan Pengendaliannya Di Kabupaten Sikka, Provinsi Nusa Tenggara Timur [tesis]. Bogor (ID). Institut Pertanian Bogor.
Widiatmaka. 2015. Integrasi Informasi Geografis dan Informasi Sumberdaya Lahan Pertanian Mendukung Kedaulatan Pangan Nasional. Prosiding Seminar Nasional Peranan Geografi Dalam Mendukung Kedaulatan Pangan. Cibinong, 7 April 2015.
Widiatmaka, Ambarwulan W, Munibah K, Firmansyah I, Santoso PBK. 2013. Analisis Perubahan Penggunaan Lahan dan Kesesuaian Lahan Untuk Sawah di Sepanjang Jalur Jalan Tol Jakarta-Cikampek dan Jalan Nasional Pantura. Kab. Karawang. Prosiding Seminar Nasional dan Forum Ilmiah Tahunan Ikatan Surveyor Indonesia. Yogyakarta, 30 Oktober 2013.
Wijaya CI. 2011. Land use change modelling in Siak district, Riau province, Indonesia using multinomial logistic regression. [tesis]. Bogor (ID): Institut Pertanian Bogor.
Zhu L, Song R, Sun S, Li Y, Hu K. 2022. Land use/land cover change and its impact on ecosystem carbon storage in coastal areas of China from 1980 to 2050. Ecological Indicators. 142 (2022) 109178. https://doi.org/10.1016/j.ecolind.2022.109178
Authors
VerawatyI., Widiatmaka and FirmansyahI. (2023) “Modeling of land use and cover changes (LUCC) in Deli Serdang Regency, North Sumatra Province”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 13(2), pp. 237-251. doi: 10.29244/jpsl.13.2.237-251.
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