skip to main content

Prediction of Future Land Use and Land Cover (LULC) in Makassar City

*Sudirman Nganro  -  Departemen Arsitektur Fakultas Teknik Universitas Hasanuddin, Indonesia
Slamet Trisutomo  -  Hasanuddin University, Indonesia
Roland Barkey  -  Hasanuddin University, Indonesia
Mukti Ali  -  Hasanuddin University, Indonesia
Hidefumi Imura  -  Global Cooperation Institute for Sustainable Cities, Yokohama City University, Japan
Akio Onishi  -  Global Cooperation Institute for Sustainable Cities, Yokohama City University, Japan
Pei-I Tsai  -  Global Cooperation Institute for Sustainable Cities, Yokohama City University, Japan
Mohd Amirul Mahamud  -  Universiti Sains Malaysia, Penang, Malaysia, Malaysia

Citation Format:
Abstract

Migration from rural area to urban area increases urban population. It increases and needs for settlements, leading to conversion of agricultural lands into settlement areas. Inconsistent land use compared with spatial planning causes change in land use. Spatial land use expansion can be monitored and predicted by modeling. NetLogo application is a software integrated with Agent-Based Modeling (ABM), which can be used to predict change of land use with various complex parameters. The present study used population growth as a parameter to predict change of land use of Makassar in 2050 based on 2017 land use classification map as the start of the prediction. The analysis result showed that the biggest change of land use happens to Settlement class which is 594.74 hectares and the smallest is Water Body class which is 8.76 hectares.

Fulltext View|Download
Keywords: prediction; land use land cover; agent-based models; Makassar City
Funding: Hasanuddin University

Article Metrics:

Article Info
Section: Articles
Language : EN
  1. Abdul Hafid. (2016). Kota Makassar dalam Angka 2016. Makassar: BPS Kota Makassar
  2. Anwar Haris. (2002). Makassar Dalam Angka 2002. Makassar: BPS Kota Makassar
  3. Bata. (2012). Simulasi Berbasis Agen-Based Modeling (ABM) Menggunakan Netlogo. In SENTIKA 2012. Yogyakarta
  4. Dhartaredjasa, I. (2013). Analisis citra satelit multitemporal untuk kajian perubahan penggunaan lahan di kota surabaya, kabupaten gresik dan sidoarjo tahun 1994-2012. Jurnal Bumi Indonesia, 2, 164–173
  5. Fatichah, Rahman, A. (2017). Pemodelan Epidemik Menggunakan Cellular Automata. Retrieved from https://www.researchgate.net/publication/265751686_PEMODELAN_EPIDEMIK_MENGGUNAKAN_CELLULAR_AUTOMATA
  6. Kurnianti, Rustiadi, B. (2016). Land Use Projection for Spatial Plan Consistency in Jabodetabek. Indonesian Journal of Geography, 47, 124–131
  7. MacAl, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. https://doi.org/10.1057/jos.2010.3
  8. Setiady, D. (2016). Prediksi Perubahan Lahan Pertanian Sawah Sebagian Kabupaten Klaten dan Sekitarnya Menggunakan Cellular Automata dan Data Penginderaan Jauh. Jurnal Bumi Indonesia, 5
  9. Tisue, S., & Wilensky, U. (2004). Netlogo: A Simple Environment for Modeling Complexity. Conference on Complex Systems, 1–10. https://doi.org/10.1109/ICVD.2004.1261037
  10. Wijaya, S. (2013). Integrasi Model Spasial Cellular Automata dan Regresi Logistik Biner untuk Pemodelan Dinamika Perkembangan Lahan Terbangun. Jurnal Bumi Indonesia, 2, 126–133
  11. Wilensky. (1999). NetLogo. Retrieved November 1, 2017, from http://ccl.northwestern.edu/netlogo/index.shtml
  12. Zope, Eldho, J. (2016). Impacts of land use-land cover change and urbanization on flooding: A case study of Oshiwara River Basin in Mumbai, India. Catena, 145, 142–154. https://doi.org/10.1016/j.catena.2016.06.009

Last update:

No citation recorded.

Last update:

No citation recorded.