DOI: https://doi.org/10.14710/tpwk.2023.33560

Pemodelan Spasio-Temporal Tren Perubahan Penutup Lahan Kota Semarang Tahun 2000 - 2020

*Ghiffari Barbarossa  -  Department of Urban and Regional Planning, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Anang Wahyu Sejati scopus publons  -  Department of Urban and Regional Planning, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

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Abstract
Fenomena urbanisasi merupakan proses transformasi perkotaan yang diyakini menyebabkan  terjadinya perubahan penutup lahan berupa peningkatan lahan terbangun serta menurunnya lahan terbuka hijau. Hal ini berpotensi menurunkan kualitas lingkungan dan kehidupan masyarakat perkotaan. Oleh sebab itu, simulasi pemodelan perubahan penutup lahan menjadi penting untuk dilakukan. Penelitian ini bertujuan untuk memodelkan tren spasio-temporal perubahan penutup lahan Kota Semarang Tahun 2000 – 2020. Kota Semarang dipilih sebagai objek studi sebab ekspansi lahan urban yang terjadi di sana. Penelitian ini dilakukan dengan memanfaatkan data penginderaan jauh dan GIS berbasis Random Forest Classification. Hasil penelitian menemukan bahwa terdapat dua tren pertumbuhan, mengikuti jaringan transportasi di tengah Kota Semarang dan menyebar secara acak pinggir Kota. Selain itu, ditemukan juga tumbuhnya lahan urban di pesisir yang ternyata berpengaruh pada meluasnya badan air yang disebabkan oleh penurunan muka tanah. Hasil penelitian ini diharapkan dapat menjadi rekomendasi dalam perencanaan penataan ruang perkotaan serta penyusunan kebijakan manajemen lahan.
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Keywords: Penutup lahan; tren perubahan; spasio-temporal; Kota Semarang

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  10. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011
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  16. Pradhan, R. P., Arvin, M. B., & Nair, M. (2020). Urbanization , transportation infrastructure , ICT , and economic growth : A temporal causal analysis. 115(September 2019)
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  23. Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L., & Dewitz, J. A. (2021). Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States. Remote Sensing of Environment, 257(January). https://doi.org/10.1016/j.rse.2021.112357

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