Comprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023

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dc.contributor.authorPandit, Santa
dc.contributor.authorDube, Timothy
dc.contributor.authorShimada, Sawahiko
dc.date.accessioned2025-01-21T09:32:20Z
dc.date.available2025-01-21T09:32:20Z
dc.date.issued2024
dc.description.abstractUnderstanding land use and land cover (LULC) dynamics in semi-arid regions is vital for unraveling complex environmental processes and resource management. This study delves into the intricate interplay of land patterns and resource dynamics, offering indispensable insights into the environmental repercussions of these changes. The study aims to quantify land use categories in Djibouti's semi-desert region using remote sensing. It analyzes temporal changes and evaluates Random forest (RF) algorithms for land use classification. Through meticulous quantification and comprehensive temporal analysis, the research contributes significantly to remote sensing and environmental science by enhancing understanding of land use dynamics and informing sustainable land management practices. Leveraging machine learning supervised classification on the google earth engine (GEE) platform using lands at data spanning four time periods (1990, 2002, 2012, and 2023), alongside spectral indices and digital elevation model (DEM) data, our study achieves unprecedented insights. Our findings reveal a significant landscape transformation, delineating seven major land cover classes: mangroves, bushes, farmland, built-up areas, water bodies, barren land, and salt plains. With overall accuracy ranging from 89 % to 95 %, our assessments demonstrate significant changes in land use types over the studied period. Notably, mangroves, bushes, farmland, and salt areas witnessed declines, while built-up areas, water bodies, and barren lands expanded.
dc.identifier.citationPandit, S., Shimada, S. and Dube, T., 2024. Comprehensive Analysis of Land Use and Cover Dynamics in Djibouti Using Machine Learning Technique: A Multi-Temporal Assessment from 1990 to 2023. Environmental Challenges, p.100920.
dc.identifier.urihttps://doi.org/10.1016/j.envc.2024.100920
dc.identifier.urihttps://hdl.handle.net/10566/19856
dc.language.isoen
dc.publisherElsevier B.V.
dc.subjectDjibouti
dc.subjectGoogle earth engine
dc.subjectLand use
dc.subjectMachine learning
dc.subjectSemi-desert landscapes
dc.titleComprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023
dc.typeArticle

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