Hoa Thuy Quynh, Nguyen Thanh Hoan, Nguyen Anh Tuan, Ho Le Thu, Nguyen Van Dung, Vo Trong Hoang

Main Article Content

Abstract

This study investigates changes in vegetation cover and their relationship with urbanization in Hanoi from 2000 to 2023. Landsat optical imagery was used to derive Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) indices, while statistical analyses (trend, correlation, and regression) assessed spatiotemporal dynamics. Results reveal significant vegetation loss in peri-urban districts such as Nam Tu Liem
(–690 ha), Hoai Duc (–650 ha), Ung Hoa (–629 ha), and Ha Dong (–588 ha), whereas increases were observed in Ba Vi (+807 ha), Gia Lam (+664 ha), and Son Tay (+230 ha). Inner-city districts (e.g., Hai Ba Trung, Hoan Kiem, Ba Dinh) experienced slight greening. NDVI–NDBI correlations show that over 56% of the city’s area exhibits a strong negative relationship (r < –0.5), underscoring the close link between urban expansion and vegetation decline. These findings highlight areas most affected by urbanization and provide a scientific basis for urban planning, sustainable development, and environmental protection in Hanoi.


 

Keywords: Vegetation change; NDVI; urbanization; NDBI; Hanoi.

References

[1] G. M. Gandhi, S. Parthiban, N. Thummalu, A. Christy, Ndvi: Vegetation Change Detection Using Remote Sensing and Gis–A Case Study of Vellore District, Procedia Computer Science, Vol. 57, 2015, pp. 1199-1210.
[2] Y. Yang, J. Zhu, C. Zhao, S. Liu, X. Tong, The Spatial Continuity Study of NDVI Based on Kriging and BPNN Algorithm, Mathematical and Computer Modelling, Vol. 54, No. 3-4, 2011, pp. 1138-1144.
[3] Y. Lan, Development of an Integration Sensor and Instrumentation System for Measuring Crop Conditions, Agricultural Engineering International: CIGR Journal, 2009.
[4] T. R. Tenreiro, M. G. Vila, J. A. Gómez, J. A. J. Berni, E. Fereres, Using NDVI for the Assessment of Canopy Cover in Agricultural Crops Within Modelling Research, Computers and Electronics in Agriculture, Vol. 182, 2021, pp. 106038.
[5] R. G. Allen, L. S. Pereira, Estimating Crop Coefficients from Fraction of Ground Cover and Height, Irrigation Science, Vol. 28, 2009, pp. 17-34.
[6] J. A. Gómez, T. A. Sobrinho, J. V. Giráldez, E. Fereres, Soil Management Effects on Runoff, Erosion and Soil Properties in An Olive Grove of Southern Spain, Soil and Tillage Research, Vol. 102, No. 1, 2009, pp. 5-13.
[7] R. Almalki, M. Khaki, P. M. Saco, J. F. Rodriguez, Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-arid Areas Using Remote Sensing Technology: A Review, Remote Sensing, Vol. 14, No. 20, 2022, pp. 5143.
[8] X. Cui, C. Gibbes, J. Southworth, P. Waylen, Using Remote Sensing to Quantify Vegetation Change and Ecological Resilience in A Semi-arid System, Land, Vol. 2, No. 2, 2013, pp. 108-130.
[9] Z. G. Bai, D. L. Dent, L. Olsson, M. E. Schaepman, Proxy Global Assessment of Land Degradation, Soil Use and Management, Vol. 24, No. 3, 2008, pp. 223-234.
[10] J. Li, J. Lewis, J. Rowland, G. Tappan, L. Tieszen, Evaluation of Land Performance in Senegal Using Multi-Temporal NDVI and Rainfall Series, Journal of Arid Environments, Vol. 59, No. 3, 2004, pp. 463-480.
[11] C. Yang, J. H. Everitt, D. Murden, Evaluating High Resolution SPOT 5 Satellite Imagery for Crop Identification, Computers and Electronics in Agriculture, Vol. 75, No. 2, 2011, pp. 347-354.
[12] Y. Ding, Y. Feng, K. Chen, X. Zhang, Analysis of Spatial and Temporal Changes in Vegetation Cover and Its Drivers in the Aksu River Basin, China, Scientific Reports, Vol. 14, No. 1, 2024, pp. 10165.
[13] H. A. Huy, Assessment of Vegetation Cover Changes in Some Urban and Peri-urban Areas of Hanoi Using Multispectral And Multitemporal LANDSAT Satellite Imagery, VNU Journal of Science: Earth and Environmental Sciences, 2016.
[14] P. V. Khoa, N. Q. Hieu, N. T. T. An, P. D. Son, P. V. Duan, Application of the Normalized Difference Vegetation Index (NDVI) for Rapid Identification of Selected Forest Types in the Central Highlands, Vietnam, Journal of Forestry Science and Technology, Vol. 5, 2019, pp. 81-89.
[15] D. N. B. Toan, D. T. Nhung, N. T. D. My, N. N. Thach, P. V. Manh, Monitoring Mangrove Vegetation Using Remote Sensing and Machine Learning Models: A Case Study in Quy Nhon City, Binh Dinh Province, Vietnam, Journal of Surveying and Mapping Science, Vol. 50, 2021, pp. 29-38.
[16] N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, R. Moore, Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone, Remote Sensing of Environment,
Vol. 202, 2017, pp. 18-27.
[17] F. J. Kriegler, Preprocessing transformations and Their Effects on Multspectral Recognition, In Proceedings of the Sixth International Symposium on Remote Sesning of Environment, 1969,
pp. 97-131.
[18] Y. Zha, J. Gao, S. Ni, Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery, International Journal of Remote Sensing, Vol. 24, No. 3, 2003, pp. 583-594.
[19] J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, L. Eklundh, A Simple Method for Reconstructing A High-Quality NDVI Time-series Data Set Based on the Savitzky–golay Filter, Remote sensing of Environment, Vol. 91, No. 3-4, 2004, pp. 332-344, https://doi.org/10.1016/j.rse.2004.03.014.
[20] K. Pearson, VII. Mathematical Contributions to the Theory of Evolution.—III. Regression, Heredity, and Panmixia, Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of A Mathematical or Physical Character, No. 187, 1896, pp. 253-318.