CA-Markov Chain Model-based Predictions of Land Cover: A Case Study of Banjarmasin City

Supriatna, Mutia Kamalia Mukhtar*, Kartika Kusuma Wardani, Fathia Hashilah, Masita Dwi Mandini Manessa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Changes in land cover are widespread in Indonesia. This tendency frequently causes annual deforestation rates to increase, which might lead to numerous natural calamities. This study will examine land cover changes, develop land cover prediction models, and examine the link between land cover changes and the Banjarmasin City and surrounding area flood disaster. Annual variations in land cover are determined using images from the GlobeLand30 satellite and a remote sensing method. Using the Cellular Automata - Markov Chain approach, satellite imagery is analyzed to estimate land cover. The results indicate that built-up land and forests will have the most remarkable change in land cover from 2000 to 2020, whereas forests are expected to face deforestation of 356 km2 from 2020 to 2030. In 2021, deforestation produced catastrophic floods, with 111 flood locations in the plantation zone. The water has reached areas with low predicted flood risk.

Original languageEnglish
Pages (from-to)365-372
Number of pages8
JournalIndonesian Journal of Geography
Issue number3
Publication statusPublished - Oct 2022
Externally publishedYes


  • Cellular automata
  • deforestation
  • land cover change
  • land cover prediction
  • markov chain


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