Land Use and Land Cover Dynamics in Dinajpur District: Spatio-Temporal Trends and Future Scenario Using Google Earth Engine and CA–Markov Modeling

Öz

Investigation on the detection and prediction of changes in land use and land cover (LULC) is essential for reinforcing sustainable management and planning. As a result, the purpose of this research was to discover LULC changes in the Dinajpur area of Bangladesh between 2000 and 2020, as well as anticipate future changes for 2040. Landsat-5, 7, 8, and 9 pictures, as well as vegetation indices and topographic parameters, were used to classify LULCs in 2000, 2005, 2010, and 2020. Classification was performed using the random forest (RF) machine learning method, which is embedded into the cloud-based platform Google Earth Engine (GEE). The classification accuracy evaluation found excellent agreement between the classified maps and the validation dataset, with kappa coefficients of 0.904, 0.919, 0.924, and 0.901 for the LULC maps of 2000, 2005, 2010, and 2020, respectively. The Spatio-Temporal Trends study shows that from 2000 to 2020, built-up land grew by 82.8%, barren land grew by 41.1%, and vegetation and agriculture shrank by 74.1% and 15.1%, respectively. The LULC state in 2040 was simulated using Cellular Automata-Markov. CA-Markov forecasts indicate that agricultural land would decline by 18%, while built-up and barren areas will rise by 27% and 22.6%, respectively. Furthermore, declining forest cover raised concerns about long-term ecological balance. The recent developments in Dinajpur highlight the need for a unified land strategy that balances urban growth and environmental protection.

Anahtar Kelimeler

• LULC
• Dinajpur
• GEE
• CA–Markov
• Spatio-temporal Change

Kaynakça

1. [1] Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101-112.
2. [2] Garedew, E., Sandewall, M., Söderberg, U., & Campbell, B. M. (2009). Land-use and land-cover dynamics in the central rift valley of Ethiopia. Environmental management, 44(4), 683-694.
3. [3] Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in physical geography, 21(3), 375-393.
4. [4] Rogan, J., & Mietkiewicz, N. (2024). Land cover change detection. In Remote Sensing Handbook, Volume IV (pp. 311-342). CRC Press.
5. [5] Tassi, A., & Vizzari, M. (2020). Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms. Remote Sensing, 12(22), 3776.
6. [6] 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.
7. [7] Rahman, M. M., Nuhash, M. M. R., & Ray, S. (2025, December). Effect of Land Use/land Cover (LULC) changes on land surface temperature for Chuadanga, Bangladesh. In Proceedings of International Conference on Civil Engineering Research & Innovations (Vol. 12, p. 14).
8. [8] Noszczyk, T. (2019). A review of approaches to land use changes modeling. Human and Ecological Risk Assessment: An International Journal, 25(6), 1377-1405.

9. [9] Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421.
10. [10] Pontius, G. R., & Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243-265.
11. [11] Hassan, M. M. (2017). Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in Bangladesh. Remote Sensing Applications: Society and Environment, 7, 69-83.
12. [12] Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S., & Prodhan, F. A. (2022). Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 25(1), 173-180.
13. [13] Alam, K. F., & Ahamed, T. (2022). Assessment of land use land cover changes for predicting vulnerable agricultural lands in river basins of Bangladesh using remote sensing and a fuzzy expert system. Remote Sensing, 14(21), 5582.
14. [14] Bhattacharjee, S., Islam, M. T., Kabir, M. E., & Kabir, M. M. (2021). Land-use and land-cover change detection in a north-eastern wetland ecosystem of Bangladesh using remote sensing and GIS techniques. Earth Systems and Environment, 5(2), 319-340.
15. [15] Abdullah, A. Y. M., Masrur, A., Adnan, M. S. G., Baky, M. A. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7), 790.
16. [16] Rahman, M. T. U., Tabassum, F., Rasheduzzaman, M., Saba, H., Sarkar, L., Ferdous, J., ... & Zahedul Islam, A. Z. M. (2017). Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environmental monitoring and assessment, 189(11), 565.
17. [17] Dewan, A. M., & Yamaguchi, Y. (2009). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environmental monitoring and assessment, 150(1), 237-249.
18. [18] Rana, M. M. S. P., Hossain, M. A., & Nasher, N. M. R. (2022). Identification of groundwater potential zone using geospatial techniques of agriculture dominated area in Dinajpur district, Bangladesh. Environmental Challenges, 7, 100475.
19. [19] Rahman, M. M. (2025). GIS based allowable bearing capacity thematic maps of shallow foundation for Bogura District, Bangladesh. International Journal of Engineering and Geosciences, 10(3), 329-338.
20. [20] Rahman, M. M., & Sarder, M. (2026). GIS-based semi-variogram model selection for the preparation of Modified Mercalli Intensity map of Dinajpur Sadar, Bangladesh. International Journal of Engineering and Geosciences, 11(1), 136-148.
21. [21] Rahman, M. M., Thakur, S., Ahmed, S. T., & Yasmin, R. (2025). GIS-based development of liquefaction hazard and soil distribution maps for Dinajpur Sadar, Bangladesh. International Journal of Engineering and Geosciences, 11(2), 263-273.
22. [22] Hossain, M. B., Roknuzzaman, M., & Rahman, M. M. (2022). Liquefaction potential evaluation by deterministic and probabilistic approaches. Civil Engineering Journal, 8(7), 1459-1481.
23. [23] Rahman, M. M., Islam, M. T., Roknuzzaman, M., Ansary, S., & Hossain, M. N. (2025). GIS-Based Physiochemical and Regression Analysis of Drinking Water Quality in Dhaka South City Corporation, Bangladesh. International Journal of Engineering and Geosciences, 11(2), 321-335.
24. [24] Rahman, M. M., Hossain, M. B., & Roknuzzaman, M. (2023, April). Effect of peak ground acceleration (PGA) on liquefaction behavior of subsoil: A case study of Dinajpur Sadar Upazila, Bangladesh. In AIP Conference Proceedings (Vol. 2713, No. 1). AIP Publishing.
25. [25] Hossain, M. B., & Rahman, M. M. (2025). Seismic microzonation and probability of ground failure assessment caused by liquefaction for Bogura District, Bangladesh. Journal of Rehabilitation in Civil Engineering, 13(2), 218-242.
26. [26] Rahman, M. M., Thakur, S., Ahmed, S. T., & Yasmin, R. (2025). GIS-based development of liquefaction hazard and soil distribution maps for Dinajpur Sadar, Bangladesh. International Journal of Engineering and Geosciences, 11(2), 263-273.
27. [27] Rahman, M. M., & Hossain, M. B. (2026). Assessment and Mitigation of Liquefaction Vulnerability for Selected Zones of Bogura Sadar, Bangladesh. Journal of Brilliant Engineering, 2, 41092.
28. [28] Rahman, M. M., Alam, M. N., & Sarder, M. GIS-Based Estimation of Corrected Standard Penetration Number Using the Best Semi-Variogram Model for Naogaon Sadar. International Journal of Engineering Technologies IJET, (Advanced Online Publication), 1-16.
29. [29] Biswas, J., Jobaer, M. A., Haque, S. F., Islam Shozib, M. S., & Limon, Z. A. (2023). Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh. Heliyon, 9(11), e21245.
30. [30] Lou, C., Al-qaness, M. A. A., AL-Alimi, D., Dahou, A., Abd Elaziz, M., Abualigah, L., & Ewees, A. A. (2025). Land use/land cover (LULC) classification using hyperspectral images: A review. Geo-Spatial Information Science, 28(2), 345–386.
31. [31] Afroz, T., Miah, M. G., Abdullah, H. M., Islam, M. R., & Rahman, Md. M. (2022). Monitoring of LULC Changes and Forest Loss Using Geospatial Technique: A Case Study from Northern Region of Bangladesh. American Journal of Geospatial Technology, 1(2), 1–9.