Modelling Land Use Land Cover Change along with the Underlying Drivers in Taita Taveta County between the Years (2000-2022)

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 desktop software was used for mapping to evaluate the LULC changes and Support Vector Machine (SVM) and machine learning Spectral Angle Mapper (SAM) algorithms were used in classifying the images. Accuracy assessments were conducted for each reference year with overall accuracies of more than 87.5% and Kappa coefficients greater than 0.89 calculated.SPSS was used for the analysis of livelihood of the residents. Analysis results reveal that the built-up area rose from 17.93% to 39.59%, while crop land increased from 5.17% to 9.03%. Additionally, bush land expanded from 30.96% to 43.19%, and bare land grew from 14.75% to 18.95%. A minor decrease in water bodies was observed, dropping from 34.02% to 33.93%. Furthermore, forest cover declined significantly from 4.12% to 2.77% between the years 2000 and 2022. It was also noted that there was a sharp decline in grassland area from 59.405% to 35.69% between 2000 and 2010, although it saw an increase by 2022, rising to 44.21%. The primary activities linked to these changes encompassed population increase, urban expansion, deforestation, overgrazing, and mining operations. The focus of this study is a critical issue amidst the rapid urbanization and accompanying changes in land use and land cover. Understanding the drivers behind LULC changes is essential as it affects the growth of an area; this knowledge is vital for suggesting effective strategies to manage urban sprawl. This topic is also fundamental to the development of carbon policies. The findings from this study will aid both the county and national government in their decision-making processes concerning the region. Furthermore, it will provide researchers with vital insights for understanding the relationships between humans and their surroundings, along with the alterations in land use during the specified period.

Authors

Publication Details

Journal
Journal of Geography Environment and Earth Science International
Published
2025-09-24
DOI
https://doi.org/10.9734/jgeesi/2025/v29i10953
Primary Topic
Indigenous Studies and Ecology
Type
article
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article

Modelling Land Use Land Cover Change along with the Underlying Drivers in Taita Taveta County between the Years (2000-2022)

Nashon Juma Adero, Clifford Okembo, Godfrey Ouma Makokha, Emmah Mwangi
Journal of Geography Environment and Earth Science International
Indigenous Studies and Ecology
article

Modelling Land Use Land Cover Change along with the Underlying Drivers in Taita Taveta County between the Years (2000-2022)

Nashon Juma Adero, Clifford Okembo, Godfrey Ouma Makokha, Emmah Mwangi
article en

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 desktop software was used for mapping to evaluate the LULC changes and Support Vector Machine (SVM) and machine learning Spectral Angle Mapper (SAM) algorithms were used in classifying the images. Accuracy assessments were conducted for each reference year with overall accuracies of more than 87.5% and Kappa coefficients greater than 0.89 calculated.SPSS was used for the analysis of livelihood of the residents. Analysis results reveal that the built-up area rose from 17.93% to 39.59%, while crop land increased from 5.17% to 9.03%. Additionally, bush land expanded from 30.96% to 43.19%, and bare land grew from 14.75% to 18.95%. A minor decrease in water bodies was observed, dropping from 34.02% to 33.93%. Furthermore, forest cover declined significantly from 4.12% to 2.77% between the years 2000 and 2022. It was also noted that there was a sharp decline in grassland area from 59.405% to 35.69% between 2000 and 2010, although it saw an increase by 2022, rising to 44.21%. The primary activities linked to these changes encompassed population increase, urban expansion, deforestation, overgrazing, and mining operations. The focus of this study is a critical issue amidst the rapid urbanization and accompanying changes in land use and land cover. Understanding the drivers behind LULC changes is essential as it affects the growth of an area; this knowledge is vital for suggesting effective strategies to manage urban sprawl. This topic is also fundamental to the development of carbon policies. The findings from this study will aid both the county and national government in their decision-making processes concerning the region. Furthermore, it will provide researchers with vital insights for understanding the relationships between humans and their surroundings, along with the alterations in land use during the specified period.

Journal of Geography Environment and Earth Science InternationalVol. 29(10)
Openalex Percentile: Top 59%
Indigenous Studies and Ecology
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