Land Use Land Cover Update of Cabanatuan City in 2026
Downloads
This study focuses on the update of land use and land cover (LULC) of Cabanatuan City in 2026 using satellite imagery and Geographic Information System (GIS) mapping. The research aims to generate an updated LULC map based on satellite imagery and GIS processing, classify major land cover categories, and determine the area and percentage distribution of each class. Sentinel-2 satellite images, supported by Landsat-8 data, were processed using QGIS through supervised classification techniques to identify categories such as agricultural land, built-up areas, and other minor land cover types.
The results show that agricultural land dominates the area, covering approximately 79.30% of the total mapped area, while built-up areas account for 18.69% and are mainly located near the city center and major road networks. The remaining land cover types occupy only a small percentage of the total area. These findings indicate that Cabanatuan City maintains a strong agricultural land base while also supporting urban functions in accessible locations.
The study provides essential baseline data that can support urban planning, land management, and decision-making. The study also demonstrates the applicability of GIS and remote sensing in analyzing spatial land use patterns and supporting planning-related applications.
Abbas, Z., & Jaber, H. S. (2020, March). Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP conference series: materials science and engineering (Vol. 745, No. 1, p. 012166). Iop Publishing.
Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global transitions proceedings, 2(1), 8-17.
Branch, J. (2017). Territorial conflict in the digital age: Mapping technologies and negotiation. International Studies Quarterly, 61(3), 557-569.
Aydinoglu, A. C., Yomralioglu, T., Inan, H. I., & Sesli, F. A. (2010). Managing land use/cover data harmonized to support land administration and environmental applications in Turkey. Scientific Research and Essays, 5(3), 275-284
Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14(19), 4978.
Xu, F., Heremans, S., & Somers, B. (2022). Urban land cover mapping with Sentinel-2: A spectro-spatio-temporal analysis. Urban Informatics, 1(1), 8.
Kalfas, D., Kalogiannidis, S., Chatzitheodoridis, F., & Toska, E. (2023). Urbanization and land use planning for achieving the sustainable development goals (SDGs): A case study of Greece. Urban Science, 7(2), 43.
Khan, M., Hanan, A., Kenzhebay, M., et al. (2024). Transformer-based land use and land cover classification with explainability using satellite imagery. Scientific Reports, 14, 16744.
M, A., Ahmed, S.A. & N, H. (2023). Land use and land cover classification using machine learning algorithms in Google Earth Engine. Earth Sci Inform 16, 3057–3073.
Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022). Developments in land use and land cover classification techniques in remote sensing: A review. Journal of Geographic Information System, 14(1), 1-28.
Nasiri, V., Deljouei, A., Moradi, F., Sadeghi, S. M. M., & Borz, S. A. (2022). Land use and land cover mapping using Sentinel-2, Landsat-8 satellite images, and Google Earth Engine: A comparison of two composition methods. Remote Sensing, 14(9), 1977.
Ouchra, H., Belangour, A., & Erraissi, A. (2022, October). Satellite data analysis and geographic information system for urban planning: A systematic review. In 2022 International Conference on Data Analytics for Business and Industry (ICDABI) (pp. 558-564)
