TY - JOUR
T1 - CA-Markov Chain Model-based Predictions of Land Cover
T2 - A Case Study of Banjarmasin City
AU - Supriatna,
AU - Mukhtar, Mutia Kamalia
AU - Wardani, Kartika Kusuma
AU - Hashilah, Fathia
AU - Manessa, Masita Dwi Mandini
N1 - Funding Information:
This study was financially supported by Universitas Indonesia under a research grant of Kemenristek/BRIN 2021 with grant contact number NKB-249/UN2.RST/HKP.05.00/2021.
Publisher Copyright:
© 2022 Faculty of Geography UGM and The Indonesian Geographers Association © 2022 by the authors. Licensee Indonesian Journal of Geography, Indonesia.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - Cellular automata
KW - deforestation
KW - land cover change
KW - land cover prediction
KW - markov chain
UR - http://www.scopus.com/inward/record.url?scp=85148365298&partnerID=8YFLogxK
U2 - 10.22146/IJG.71721
DO - 10.22146/IJG.71721
M3 - Article
AN - SCOPUS:85148365298
SN - 0024-9521
VL - 54
SP - 365
EP - 372
JO - Indonesian Journal of Geography
JF - Indonesian Journal of Geography
IS - 3
ER -
