_____________________________________________________________________________________________________ *Corresponding author: E-mail: mbichiemmilly466@gmail.com; Cite as: Emmilly Mbichi Mwangi, Clifford Okembo, Godfrey Makokha, and Nashon Adero. 2025. “Modelling Land Use Land Cover Change Along With the Underlying Drivers in Taita Taveta County Between the Years (2000-2022)”. Journal of Geography, Environment and Earth Science International 29 (10):66–78. https://doi.org/10.9734/jgeesi/2025/v29i10953. Journal of Geography, Environment and Earth Science International Volume 29, Issue 10, Page 66-78, 2025; Article no.JGEESI.144600 ISSN: 2454-7352 Modelling Land Use Land Cover Change along with the Underlying Drivers in Taita Taveta County between the Years (2000-2022) Emmilly Mbichi Mwangi a* , Clifford Okembo b , Godfrey Makokha a and Nashon Adero a a Taita Taveta University, Kenya. b Technical University of Kenya, Kenya. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: https://doi.org/10.9734/jgeesi/2025/v29i10953 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://pr.sdiarticle5.com/review-history/144600 Received: 21/07/2025 Published: 24/09/2025 ABSTRACT Due to a variety of natural events and human activities, the earth's surface experiences rapid changes in land-use and land-cover (LULC). The primary aim of this study was to provide a quantitative assessment of the changes in land cover and land use in Taita Taveta County from 2000 to 2022, along with a forecast for 2030, and to examine the factors driving these changes. Remote sensing and GIS techniques were employed to conduct the land-use and land-cover change analysis, while SPSS was utilized to evaluate the driving factors. Landsat images for the years 2000, 2010, and 2022 were utilized to model LULCC. The images were digitized using the polygon-making feature in QGIS and correctly classified by the designated categories. ArcGIS Original Research Article