Student Publications [Scholarly]

Document Type

Article

Abstract

Introduction – Land use and land cover (LULC) change is rapidly redrawing the environmental landscape of coastal Bangladesh. Methods – This study integrates Landsat-based classification, machine learning-based approaches, and Markov Chain simulation to assess historical and future LULC dynamics in Chattogram District. Results – Random Forest classification achieved overall accuracies of 95.96%, 90.45%, and 94.25% for 2006, 2015, and 2025, respectively. Results reveal substantial cropland expansion (+32, 375 ha) and continued growth of built-up land, accompanied by declines in vegetation, waterbodies, and bare land. Random Forest demonstrated strong predictive performance, achieving an out-of-bag (OOB) accuracy of 92.63% and a skill score of 0.9171. By 2035, cropland and built-up land are projected to increase to 43.95% and 4.63%, respectively, while vegetation, waterbody, and transitional land-cover classes continue to decline. Discussion – These findings provide empirical evidence to support sustainable land-use planning and environmental management in coastal-urban systems. Copyright © 2026 Alam.

Publication Title

Frontiers in Environmental Science

Publication Date

2026

Volume

14

ISSN

2296-665X

DOI

10.3389/fenvs.2026.1923661

Keywords

Chattogram district, coastal-urban systems, land use and land cover change, Markov Chain Simulation, random forest, support vector machine

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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