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

Supriatna Supriatna(1), Mutia Kamalia Mukhtar(2*), Kartika Kusuma Wardani(3), Fathia Hashilah(4), Masita Dwi Mandini Manessa(5)

(1) Department of Geography, Universitas Indonesia, Indonesia
(2) Department of Social Sciences Education Faculty of Education and Teacher Training Universitas Terbuka
(3) Department of Geography, Universitas Indonesia, Indonesia
(4) Department of Geography, Universitas Indonesia, Indonesia
(5) Department of Geography, Universitas Indonesia, Indonesia
(*) Corresponding Author


Land cover change is a prevalent thing in Indonesia. This phenomenon often causes deforestation rates to continue to increase every year, which can cause various natural disasters. This study will look at changes in land cover, make land cover prediction models, and see the relationship between land cover changes and the flood disaster that occurred in Banjarmasin City and its surroundings. Remote sensing is used to see changes in land cover from year to year with GlobeLand30 satellite imagery. Satellite imagery processing is carried out using the Cellular Automata – Markov Chain method to see the land cover prediction. The results show that the most significant land cover change from 2000 to 2020 is experienced by built-up land and forests, while in 2030, forests are predicted to experience deforestation of 356 km2 from 2020. The deforestation will cause catastrophic flooding in 2021, where flooding extends to areas that are not estimated to be high flood hazards, with 111 flood points located in the plantation area.


Deforestation; land cover change; land cover prediction; cellular automata; markov chain; flood

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