Spatial dynamics of built-up land development prediction based on cellular automata markov chain in tsunami prone areas of Ternate Island, Indonesia

Ternate Island's coastal geography makes it highly susceptible to tsunamis, and increasing built-up land intensifies this risk. This study analyzes the spatial dynamics of built-up land development in tsunami-prone areas using land cover data from 2005, 2015, and 2025, along with driving factors such as elevation, distance from roads, and economic centers. The Cellular Automata-Markov Chain (CA-Markov) method was used to project land cover for 2035 and 2045. Tsunami hazard zones were modeled using elevation, slope, 2025 land cover, and distances from the coastline and rivers. Overlay analysis of built-up land predictions with tsunami hazard zones reveals a continual expansion of built-up areas over time. Tsunami- prone zones were classified into low (31.77%), medium (24.83%), and high (43.40%) hazard levels. The findings show a direct correlation between the expansion of built-up land and increased exposure to tsunami hazards, emphasizing the need for spatial planning strategies to mitigate future risks.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-09-12
DOI
https://doi.org/10.1051/bioconf/202518804003/pdf
Primary Topic
Disaster Management and Resilience
Type
article
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article

Spatial dynamics of built-up land development prediction based on cellular automata markov chain in tsunami prone areas of Ternate Island, Indonesia

Philia Christi Latue, Heinrich Rakuasa, Intan Rabiyanti, Vadim V. Khromykh et al.
Springer Link (Chiba Institute of Technology)
Disaster Management and Resilience
article

Spatial dynamics of built-up land development prediction based on cellular automata markov chain in tsunami prone areas of Ternate Island, Indonesia

Philia Christi Latue, Heinrich Rakuasa, Intan Rabiyanti, Vadim V. Khromykh, Ahmad Rifai, Supriatna
article en

Abstract

Ternate Island's coastal geography makes it highly susceptible to tsunamis, and increasing built-up land intensifies this risk. This study analyzes the spatial dynamics of built-up land development in tsunami-prone areas using land cover data from 2005, 2015, and 2025, along with driving factors such as elevation, distance from roads, and economic centers. The Cellular Automata-Markov Chain (CA-Markov) method was used to project land cover for 2035 and 2045. Tsunami hazard zones were modeled using elevation, slope, 2025 land cover, and distances from the coastline and rivers. Overlay analysis of built-up land predictions with tsunami hazard zones reveals a continual expansion of built-up areas over time. Tsunami- prone zones were classified into low (31.77%), medium (24.83%), and high (43.40%) hazard levels. The findings show a direct correlation between the expansion of built-up land and increased exposure to tsunami hazards, emphasizing the need for spatial planning strategies to mitigate future risks.

Springer Link (Chiba Institute of Technology)
Sustainable cities and communities
Openalex Percentile: Top 16%
Disaster Management and Resilience
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Spatial dynamics of built-up land development prediction based on cellular automata markov chain in tsunami prone areas of Ternate Island, Indonesia — Philia Christi Latue, Heinrich Rakuasa, et al. · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS