Prediction of Land Use Land Cover Change: Molusce Plug -Based Technologies anAnalysis of Kiambu County Kenya
DOI:
https://doi.org/10.54536/ajgt.v5i1.7441Keywords:
Artificial Neural Network (ANN), Land Use Land Cover Changes (LULCC), Modules for Land Use Change Evaluation (MOLUSCE), Quantum Geographic Information System (QGIS), United States Geological Survey (USGS)Abstract
Land use land cover change is a very important factor that impacts on regional planning, sustainability in environmental and resource management. This study focused on the prediction of Land Use Land Cover changes in Kiambu County in Kenya, deploying MOLUSCE plug-based technologies. The results showed a decrease in cropland from 85,918.90 Ha in 2024 to 62,268.00 Ha in 2044. Vegetation cover is also decline from 45,871.5 Ha to 40,537.00 ha within the same period. Similarly, water bodies reduce from 12,040.50 Ha in 2024 to 10,040.50 Ha in 2044, while bare lands decline from 4,627.0 Ha to 4,050 Ha. Contrarily, built-up areas increased from 105,581.1 Ha in 2024 to 137,454 Ha in 2044. These findings highlight a trend of urban growth at the expense of agricultural and natural land covers. The study demonstrated that predictive models provide valuable insights that guides planners, policymakers, and environmentalists in making informed decisions to promote sustainable land use and environmental conservation in Kiambu County.
References
Abdulai, A., & Owusu, V. (2022). Impacts of land-use and land-cover change on rural livelihoods: Evidence from eastern Sierra Leone. Applied Geography, 147, Article 102784. https://doi.org/10.1016/j.apgeog.2022.102784
Afroz, T., Miah, M. G., Abdullah, H. M., Islam, M. R., & Rahman, M. M. (2022). Monitoring of LULC changes and forest loss using geospatial techniques: A case study from the northern region of Bangladesh. American Journal of Geospatial Technology, 1(2), 1–9. https://doi.org/10.54536/ajgt.v1i2.907
Celdric, Y. A., Ioron, B., Niambe, O. K., & Adamgbe, T. B. (2025). Detection of human-induced land use and land cover change in Nagi Forest Reserve in Gwer-West Local Government Area of Benue State, Nigeria. American Journal of Geospatial Technology, 4(1), 49–59. https://doi.org/10.54536/ajgt.v4i1.3688
Chandra, P. G., Long, J., & Pengra, S. (2021). Remote sensing and GIS technologies for monitoring land use and land cover change: Advances and applications. Remote Sensing, 13(9), Article 1734. https://doi.org/10.3390/rs13091734
Githui, F., Mutua, F., & Muthoni, F. (2022). Land use and land cover change assessment and future predictions in the Matenchose Watershed, Rift Valley Basin, Kenya, using CA-Markov simulation. Land, 11(10), Article 1632. https://doi.org/10.3390/land11101632
Intergovernmental Panel on Climate Change. (2019). Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. https://www.ipcc.ch/srccl/
Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press. https://doi.org/10.1017/9781009325844
Liang, S., Wang, J., & Zhang, X. (2023). Geospatial technologies and land use change detection: Progress and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 130–145. https://doi.org/10.1016/j.isprsjprs.2023.01.009
Mohammed, A., et al. (2025). Analyzing historical land use and land cover changes using machine learning approaches integrated with geospatial techniques in the Omo Gibe River Basin, Ethiopia. Environmental Challenges, 20, Article 101296. https://doi.org/10.1016/j.envc.2025.101296
Mugari, E., & Masundire, H. (2022). Consistent changes in land use/land cover in semi-arid areas: Implications for ecosystem service delivery and adaptation in the Limpopo Basin, Botswana. Land, 11(11), Article 2057. https://doi.org/10.3390/land11112057
Muhammad, M., et al. (2025). Geospatial technology for monitoring and predicting land cover change using CA-Markov and spatial modelling approaches. Geoid Journal, 20(2), 35–44.
Mutunga, E., Mwangi, J., & Mutua, F. (2024). Spatio-temporal patterns of land use and land cover change in Kibwezi West, Eastern Kenya. Discover Soil, 1(1), 1–18. https://doi.org/10.1007/s44378-024-0002
Mwangi, J., Kimani, P., & Otieno, R. (2025). Monitoring and prediction of land use and land cover using remote sensing and CA-ANN: A case study of Nairobi National Park and its environs. Remote Sensing Applications: Society and Environment. Advance online publication. https://doi.org/10.1016/j.rsase.2025.06.015
Yildirim, A., et al. (2025). Determination of land use and land cover change using multi-temporal PlanetScope images and a deep learning CNN model. Paddy and Water Environment, 23, 405–423. https://doi.org/10.1007/s10333-025-01024-9
Zhang, A., Tariq, A., Quddoos, A., Naz, I., Aslam, R. W., Barboza, E., & Abdullah-Al-Wadud, M. (2025). Spatio-temporal analysis of urban expansion and land use dynamics using Google Earth Engine and predictive models. Scientific Reports, 15, Article 6993. https://doi.org/10.1038/s41598-025-92034-4
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Copyright (c) 2026 Pauline Angerlyline Wamalwa, Samson Mabwoga, Charity Konana
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