Historical Analysis of Land-use Changes in Vietnam's Red River Delta: Bayesian Network Approach to Land Policies and Sustainable Development
DOI:
https://doi.org/10.47654/v30y2026i2p1-38Keywords:
LUCC, Bayesian Network, Red River Delta, agricultural policy, sustainable land useAbstract
Purpose: Vietnam’s Red River Delta experiences rapid and uneven land-use transformations driven by market liberalization and urban expansion, severely impacting agricultural land and rural livelihoods. Accurate modeling of these changes is critical for sustainable land governance. To address this gap, this study uses Bayesian network modeling to retrospectively investigate LUCC in the Red River Delta, clarifying how land-policy transitions and agricultural expansion have influenced land-use decision-making and highlighting sustainable development implications.
Design/methodology/approach: This study proposes a Bayesian network-based panel decision support framework that synthesizes (i) multi-temporal satellite-derived spatial data (1979–2022), (ii) farmer land-use decision behavior, and (iii) historical land-policy and institutional change to evaluate and project LULC dynamics in the Red River Delta. To the best of our knowledge, this is the first region-wide, long-horizon application that explicitly links LUCC trajectories to policy shifts, quantifies transformation trends, and identifies the key drivers shaping land-use change.
Findings: The Red River Delta has undergone a clear shift from rice-based agriculture toward urban–industrial land uses between 2008 and 2022. Agricultural land declined sharply (7%), while forest land decreased only modestly (0.8%) and pastureland expanded (6.3%). The Bayesian network results indicate that industrial land prices are among the most influential economic drivers of these transitions, while the strongest governance levers relate to land-use zoning and conversion controls that steer agricultural-to-urban/industrial reallocations. In addition, the slight rebound of previously diminishing undefined agricultural zones suggests a move toward more structured land management.
Research limitations/implications: Limitations include medium-resolution satellite imagery potentially overlooking small-scale features and classification uncertainties from traditional algorithms.
Practical implications and Originality/value: This study presents a Bayesian network panel decision support framework that enables ex ante policy evaluation of land governance in the Red River Delta by simulating policy scenarios before implementation and estimating their likely effects on LUCC. It tests how changes in land use zoning, conversion controls, and industrial land prices shift the probability of major transitions, especially the conversion of rice-based agricultural land to urban and industrial uses, and highlights the most influential governance levers. The region-wide, long-horizon application in the Red River Delta offers a transferable approach for Asian delta regions facing similar trade-offs between urban industrial expansion, agricultural protection, and sustainability goals.
References
Aalders, I. (2008). Modeling land-use decision behavior with Bayesian belief networks. Ecology and Society, 13(1).
ADB. (2022). Agriculture, Natural Resources and Rural Development Sector Assessment, Strategy and Road Map - Viet Nam 2021–2025. Available at: https://www.adb.org/sites/default/files/institutional-document/763181/viet-nam-2021-2025-agriculture-sector-assessment-strategy-road-map.pdf.
Aguilar, G. R., Swetschinski, L. R., Weaver, N. D., Ikuta, K. S., Mestrovic, T., Gray, A. P., Chung, E., Wool, E. E., Han, C., & Hayoon, A. G. (2023). The burden of antimicrobial resistance in the Americas in 2019: a cross-country systematic analysis. The Lancet Regional Health–Americas, 25.
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón, A. (2011). Bayesian networks in environmental modelling. Environmental Modelling & Software, 26(12), 1376–1388.
Almazyad, T., Zakuan, N., Alrubaiee, L., Butt, S., Ashaari, A., & Esmaeel, R. I. (2024). Bibliometric Insights into Crisis Management: A Review of Key Literature. Advances in Decision Sciences, 28(2), 1-34.
Angelsen, A. (2010). Policies for reduced deforestation and their impact on agricultural production. Proceedings of the National Academy of Sciences, 107(46), 19639-19644.
Anh, N. T. (2022). Việt Nam Thời Pháp Đô Hộ Nhà Xuất Bản Khoa học xã hội.
Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275.
BayesFusion. (2018). GeNIe 2.2 with hybrid Bayesian networks. Retrieved from https://www.bayesfusion.com/2017/08/17/genie-2-2-with-hybrid-bayesian-networks/.
Budianta, D., & Gunawan, D. (2024). Assessing Climate-Smart Agriculture Adoption: Enhancing Rice Production Resilience in South Sumatra, Indonesia. Journal of Smart Agriculture and Environmental Technology, 2(3), 93-99.
Celio, E., Koellner, T., & Grêt-Regamey, A. (2014). Modeling land use decisions with Bayesian networks: Spatially explicit analysis of driving forces on land use change. Environmental Modelling & Software, 52, 222-233.
Cheng, Y., Hui, Y., Liu, S., & Wong, W. K. (2022). Could significant regression be treated as insignificant: An anomaly in statistics?. Communications in Statistics: Case Studies, Data Analysis and Applications, 8(1), 133-151.
Cheng, Y., Hui, Y., McAleer, M., & Wong, W. K. (2021). Spurious relationships for nearly non-stationary series. Journal of Risk and Financial Management, 14(8), 366.
Coase, R. H. (2013). The problem of social cost. The Journal of Law and Economics, 56(4), 837-877.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.
Costanza, R., Farber, S. C., & Maxwell, J. (1989). Valuation and management of wetland ecosystems. Ecological economics, 1(4), 335-361.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108-1111.
Deininger, K., & Binswanger, H. (1999). The Evolution of the World Bank's Land Policy: Principles, Experience, and Future Challenges. The World Bank Research Observer, 14(2), 247-276. https://doi.org/10.1093/wbro/14.2.247
Dominguez Almela, V., Croker, A. R., & Stafford, R. (2024). Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks. PLoS One, 19(12), e0305882.
Duteurtre, G., Cesaro, J. D., & Ives, S. (2020). Livestock development, land-use reforms and the disinterest for pastures in the Northern highlands of Vietnam. Livestock policy, 237-246.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S., Ghosh, S., Mitra, D., & Ghosh, T. (2017). Application of Cellular automata and Markov-chain model in geospatial environmental modeling-A review. Remote Sensing Applications: Society and Environment, 5, 64–77.
Guo, W., Teng, Y., Li, J., Yan, Y., Zhao, C., Li, Y., & Li, X. (2024). A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China. Science of the Total Environment, 912, 169088.
Hagen, A. (2003). Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science, 17(3), 235-249.
Hui, Y., Wong, W. K., Bai, Z., & Zhu, Z. Z. (2017). A new nonlinearity test to circumvent the limitation of Volterra expansion with application. Journal of the Korean Statistical Society, 46, 365-374.
Hütt, C., Koppe, W., Miao, Y., & Bareth, G. (2016). Best accuracy land use/land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sensing, 8(8), 684.
Kerkvliet, B. J. T. (1995). Village-state relations in Vietnam: The effect of everyday politics on decollectivization. The Journal of Asian Studies, 54(2), 396-418.
Khải, V. T. (2008). Tích tụ ruộng đất–trang trại và nông dân. Tạp chí Nghiên cứu Kinh tế.
Khánh, N. V. (1999). Chính sách ruộng đất của thực dân Pháp ở Việt Nam: Nội dung và hệ quả. Tạp chí Nghiên cứu Lịch sử, 6.
Khánh, N. V. (2023). Cơ cấu và tình hình sử dụng ruộng đất ở Châu thổ sông Hồng trong thời kỳ đổi mới. Trường Đại học Khoa học Xã Hội và Nhân Văn.
Kjærulff, U., & Van Der Gaag, L. C. (2013). Making sensitivity analysis computationally efficient. arXiv preprint arXiv:1301.3868.
Kocabas, V., & Dragicevic, S. (2006). Coupling Bayesian networks with GIS-based cellular automata for modeling land use change. 217–233.
Laila, F. N., Rahayu, P., & Widodo, C. E. (2024). The Development of Industrial Agglomeration in Industrial Designation Areas and its Impact on Land Use Change (Case Study: Pringsurat Subdistrict and Kranggan Subdistrict, Temanggung Regency). Desa-Kota: Jurnal Perencanaan Wilayah, Kota, dan Permukiman, 6(2), 1-15.
Li, Q., Chen, X., Jiao, S., Song, W., Zong, W., & Niu, Y. (2022). Can mixed land use reduce CO2 emissions? A case study of 268 Chinese cities. Sustainability, 14(22), 15117.
Mariye, M., Jianhua, L., & Maryo, M. (2022). Land use land cover change analysis and detection of its drivers using geospatial techniques: a case of south-central Ethiopia. All Earth, 34(1), 309-332.
Marsh, S. P., MacAulay, T. G., & Van Hung, P. (2007). Agricultural development and land policy in Vietnam: policy briefs. Australian Centre for International Agricultural Research.
Nadoushan, M. A., Soffianian, A., & Alebrahim, A. (2015). Modeling land use/cover changes by the combination of Markov chain and cellular automata Markov (CA-Markov) models. Earth Environ. Health Sci, 1(1), 16.
Nascimento, N., West, T. A., Biber-Freudenberger, L., Sousa-Neto, E. R. d., Ometto, J., & Börner, J. (2020). A Bayesian network approach to modelling land-use decisions under environmental policy incentives in the Brazilian Amazon. Journal of Land Use Science, 15(2-3), 127-141.
Niculescu, S., & Lam, N. C. (2019). Geographic object-based image analysis of changes in land cover in the coastal zones of the Red River Delta (Vietnam). Journal of Environmental Protection, 10(3), 413-430.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.
Paudel, B., Zhang, Y.-l., Li, S.-c., Liu, L.-s., Wu, X., & Khanal, N. R. (2016). Review of studies on land use and land cover change in Nepal. Journal of Mountain Science, 13, 643-660.
Pontius Jr, R. G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International journal of remote sensing, 32(15), 4407-4429.
Schaefer, M., & Thinh, N. X. (2019). Evaluation of land cover change and agricultural protection sites: a GIS and remote sensing approach for Ho Chi Minh City, Vietnam. Heliyon, 5(5).
Schultz, T. W. (1964). Transforming traditional agriculture. Yale University Press.
Seto, K. C., & Kaufmann, R. K. (2003). Modeling the drivers of urban land use change in the Pearl River Delta, China: Integrating remote sensing with socioeconomic data. Land Economics, 79(1), 106-121.
Siswanto, S. Y., & Francés, F. (2019). How land use/land cover changes can affect water, flooding and sedimentation in a tropical watershed: a case study using distributed modeling in the Upper Citarum watershed, Indonesia. Environmental Earth Sciences, 78(17), 550.
Soares-Filho, B., Rodrigues, H., & Follador, M. (2013). A hybrid analytical-heuristic method for calibrating land-use change models. Environmental Modelling & Software, 43, 80-87.
Thien, B. B., & Phuong, V. T. (2024). Assessing the impact of land use/land cover changes on agricultural land in the Red River Delta, Vietnam. Vegetos, 37(2), 606-617.
Tin, H. C., Uyen, N. T., Tu, N. H. C., Binh, N. H., & Ni, T. N. K. (2023). Dynamics of seagrass beds and land use–land cover characteristics in Vietnamese Marine protected areas. Regional Studies in Marine Science, 59, 102794.
Tong, H. (1983). Threshold Models in Nonlinear Time Series Analysis, Vol. 21 of Lecture Notes in Statistics, Springer-Verlag, Heidelberg.
Tong, H. & Lim, K.S. (1980). Threshold Autoregression, Limit Cycles and Data. Journal of the Royal Statistical Society Serie B, 42, 245-292.
Torbick, N., Chowdhury, D., Salas, W., & Qi, J. (2017). Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sensing, 9(2), 119.
Toure, S. I., Stow, D. A., Shih, H. C., Weeks, J., & Lopez-Carr, D. (2018). Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sensing of Environment, 210, 259-268.
Tuan, N. T. (2022). Urbanization and land use change: A study in Vietnam. Environmental & Socio-economic Studies, 10(2), 19-29.
Ustaoglu, E., & Williams, B. (2017). Determinants of urban expansion and agricultural land conversion in 25 EU countries. Environmental Management, 60(4), 717-746.
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3-4), 312-318.
White, C. K. P. (1981). Agrarian reform AND national liberation in the Vietnamese Revolution: 1920-1957. Cornell University.
Wong, W. K., Cheng, Y., & Yue, M. (2024). Could regression of stationary series be spurious?. Asia-Pacific Journal of Operational Research, 2440017.
Wong, W.-K., & Pham, M. T. (2022a). Could the test from the standard regression model could make significant regression with autoregressive noise become insignificant?. The International Journal of Finance, 34, 1–18.
Wong, W.-K., & Pham, M. T. (2022b). Could the test from the standard regression model could make significant regression with autoregressive noise become insignificant – a note. The International Journal of Finance, 34, 19-39.
Wong, W.-K., & Pham, M. T. (2023a). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant?. The International Journal of Finance, 35, 1–19.
Wong, W.-K., & Pham, M. T. (2023b). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant – a note. The International Journal of Finance, 35, 20-41.
Wong, W. K., & Pham, M. T. (2025). Could the correlation of a stationary series with a non-stationary series obtain meaningful outcomes?. Annals of Financial Economics, forthcoming.
World Bank. (2022). Transforming Vietnamese Agriculture: Gaining More from Less. Washington, DC: World Bank Group.
Xie, Y., Hunter, M., Sorensen, A., Nogeire-McRae, T., Murphy, R., Suraci, J. P., Lischka, S., & Lark, T. J. (2023). US farmland under threat of urbanization: Future development scenarios to 2040. Land, 12(3), 574.
Yagoub, M., & Al Bizreh, A. A. (2014). Prediction of land cover change using Markov and cellular automata models: Case of Al-Ain, UAE, 1992-2030. Journal of the Indian Society of Remote Sensing, 42(3), 665–671.
Yang, Z., & Solangi, Y. A. (2024). Analyzing the relationship between natural resource management, environmental protection, and agricultural economics for sustainable development in China. Journal of Cleaner Production, 450, 141862.
Yuen, K. W., Hanh, T. T., Quynh, V. D., Switzer, A. D., Teng, P., & Lee, J. S. H. (2021). Interacting effects of land-use change and natural hazards on rice agriculture in the Mekong and Red River deltas in Vietnam. Natural Hazards and Earth System Sciences, 21(5), 1473-1493.
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