Comparative Assessment of Machine Learning Classifiers for Land Use/Land Cover Mapping in a Tropical Savanna Landscape
DOI:
https://doi.org/10.47514/kjg.2026.08.01.055Keywords:
Random Forest, Sentinel-2, Land use/land cover classification, Tropical savanna, Machine learningAbstract
Accurate land use/land cover (LULC) information is essential for environmental monitoring, agricultural planning, and sustainable land management, particularly in rapidly changing tropical savanna landscapes. This study comparatively assessed the performance of Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) classifiers for LULC mapping in Sabon Gari and Zaria, Kaduna State, Nigeria, using Sentinel-2 spectral bands and derived spectral indices. Sentinel-2 imagery acquired between November 2024 and March 2025 was processed to generate ten spectral bands and four indices (NDVI, NDWI, NDBI, and BSI), which served as predictor variables. Classification was performed for six land cover classes, namely built-up areas, savanna vegetation, dense vegetation, farmland, water bodies, and rock outcrops. Model performance was evaluated using spatially independent validation data and confusion matrix-based metrics. The results revealed notable differences among the classifiers. RF achieved the highest classification accuracy, with an Overall Accuracy of 91.13% and a Kappa coefficient of 0.894, followed by CART (84.62%; κ = 0.832) and SVM (82.89%; κ = 0.809). Savanna vegetation (42.2%) and farmland (35.3%) were identified as the dominant land cover classes in the area. Feature importance analysis showed that shortwave infrared and red-edge bands contributed most strongly to class discrimination, while derived spectral indices provided additional discriminatory power. Visual assessment further demonstrated that RF produced more spatially coherent and realistic land cover patterns than SVM and CART. The findings highlight the effectiveness of Sentinel-2 data for LULC mapping and identify RF as the most suitable classifier for tropical savanna environments characterized by heterogeneous land cover assemblages.
References
Abdulkadir, A., Mohammed, I., & Daudu, C.K. (2021). Organic Carbon in Tropical Soils: Current Trends and Potential for Carbon Sequestration in Nigerian Cropping Systems. In: Luetz, J.M., Ayal, D. (eds) Handbook of Climate Change Management. Springer, Cham. https://doi.org/10.1007/978-3-030-57281-5_307
Akintuyi, A. O., Fasona, M. J., Ayeni, A. O., & Soneye, A. S. O. (2021). Land use/land cover and climate change interaction in the derived savannah region of Nigeria. Environmental Monitoring and Assessment, 193(12), Article 848. https://doi.org/10.1007/s10661-021-09642-6
Arpitha, M., Ahmed, S. A., & Harishnaika, N. (2023). Land use and land cover classification using machine learning algorithms in Google Earth Engine. Earth Science Informatics, 16(4), 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
Awwal, Y. A., Gani, A. T., & Akwanaki, G. L. (2025a). Spatiotemporal dynamics of land use/cover and its impacts on carbon stocks in a Nigerian savanna. Sustainable Geosciences: People, Planet and Prosperity, 1, 100008. https://doi.org/10.1016/j.susgeo.2025.100008
Awwal, Y. A., Gani, A. T., Young, A. C., & Stanley, T. N. (2025b). Soil property responses to land use dynamics in Wukari, Guinea Savanna of northeastern Nigeria. Dutse Journal of Pure and Applied Sciences, 11(4b), 137–147. https://dx.doi.org/10.4314/dujopas.v11i4b.13
Awwal, Y. A., Young, A. C., & Linus, M. (2026). Geospatial analysis of soil quality index in relation to land use/land cover and spectral indices in Wukari, Nigeria. Kuwait Journal of Science, 53(3), 100577. https://doi.org/10.1016/j.kjs.2026.100577
Breiman, L. (2001). Random forests. Machine Learning, 45, pp. 5–32. https://doi.org/10.1023/A:1010933404324
Cai, B., Camarcat, L., Formosa, N., & Quddus, M. (2025). A stacked ensemble model for traffic conflict prediction using emerging sensor data. Transportation Research Interdisciplinary Perspectives, 31, Article 101457. https://doi.org/10.1016/j.trip.2025.101457
Chafiq, T., Hmamou, M., Ouhammou, I., Azmi, R., & Kumar, M. (2024). Modelling change detection for unveiling urban transitions: Using machine learning algorithms and Sentinel-2 data in Larache City, Morocco. Modeling Earth Systems and Environment, 10(2), 1711–1725. https://doi.org/10.1007/s40808-023-01860-w
Gaur, S., & Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15(2), 903. https://doi.org/10.3390/su15020903
He, D., Shi, Q., Xue, J., Atkinson, P. M., & Liu, X. (2023). Very fine spatial resolution urban land cover mapping using an explicable sub-pixel mapping network based on learnable spatial correlation. Remote Sensing of Environment, 299, Article 113884. https://doi.org/10.1016/j.rse.2023.113884
Jodhani, K.H., Patel, D., Madhavan, N., Gupta, N., Singh, S.K., Pandey, M., 2025. ML- based land use and land cover classification: assessing performance and predicting future changes. Journal of Hydrologic Engineering 30 (4). https://doi.org/10.1061/JHYEFF.HEENG- 6416.
Kılıç, M. (2026). Mapping and modelling land degradation vulnerability in a semi-arid region: a case study from Battalgazi District, Turkiye. PeerJ 14:e20606 https://doi.org/10.7717/peerj.20606
Koko, A. F., Yue, W., Abubakar, G. A., Hamed, R., & Alabsi, A. A. N. (2020). Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov). Sustainability, 12(24), 10452. https://doi.org/10.3390/su122410452
Lin, R., Chen, H., Wei, Z., Li, Y., Zhang, B., Sun, H., & Cheng, M. (2022). Improved Surface Soil Moisture Estimation Model in Semi-Arid Regions Using the Vegetation Red-Edge Band Sensitive to Plant Growth. Atmosphere, 13(6), 930. https://doi.org/10.3390/atmos13060930
Obade, V. P., & Lal, R. (2013). Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). Catena, 104, 77–92. https://doi.org/10.1016/j.catena.2012.10.014
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12(14), 2291. https://doi.org/10.3390/rs12142291
Salleh, S. A., Khalid, N., Danny, N., Zaki, N. A. M., Ustuner, M., Latif, Z. A., & Foronda, V. (2024). Support Vector Machine (SVM) and object-based classification in earth linear features extraction: A comparison. Revue Internationale de Géomatique, 33, 183–199. https://doi.org/10.32604/rig.2024.050723
Seyam, M. M. H., Haque, M. R., & Rahman, M. M. (2023). Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh. Case Studies in Chemical and Environmental Engineering, 7, Article 100293. https://doi.org/10.1016/j.cscee.2022.100293
Solgi, S., Ahmadi, S. H., & Seidel, S. J. (2023). Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields. Agricultural Water Management, 280, Article 108226. https://doi.org/10.1016/j.agwat.2023.108226
Tesfaye, W., Elias, E., Warkineh, B., Tekalign, M., & Abebe, G. (2024). Modeling of land use and land cover changes using Google Earth Engine and machine learning approach: Implications for landscape management. Environmental Systems Research, 13(1), Article 31. https://doi.org/10.1186/s40068-024-00366-3
Xiao, C., Li, P., Feng, Z., Liu, Y., & Zhang, X. (2020). Sentinel-2 red-edge spectral indices (RESI) suitability for mapping rubber boom in Luang Namtha Province, northern Lao PDR. International Journal of Applied Earth Observation and Geoinformation, 93, Article 102171. https://doi.org/10.1016/j.jag.2020.102171
Yassin, W., Abdollah, M. F., Ismail, A., Kamis, N. H., Razak, S. F. A., & Joy, H. K. (2026). Multi-stage spatial temporal ensemble model with integrated learning methods for robust deepfake detection. Discover Computing, 29(1), Article 291. https://doi.org/10.1007/s10791-026-10093-1
Yu, X., Lu, D., Jiang, X., Li, G., Chen, Y., Li, D., & Chen, E. (2020). Examining the Roles of Spectral, Spatial, and Topographic Features in Improving Land-Cover and Forest Classifications in a Subtropical Region. Remote Sensing, 12(18), 2907. https://doi.org/10.3390/rs12182907
Zafar, Z., Zubair, M., Zha, Y., Fahd, S., & Nadeem, A. A. (2024). Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 27(2), 216–226. https://doi.org/10.1016/j.ejrs.2024.03.003
Zhao, Z., Islam, F., Waseem, L. A., Tariq, A., Nawaz, M., Islam, I. U., Bibi, T., Rehman, N. U., Ahmad, W., Aslam, R. W., Raza, D., & Hatamleh, W. A. (2024). Comparison of three machine learning algorithms using Google Earth Engine for land use land cover classification. Rangeland Ecology & Management, 92, 129–137. https://doi.org/10.1016/j.rama.2023.10.007
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