Hoang Anh Huy

Main Article Content


The objective of the study is to assess changes of FVC in some urban and sub-urban
areas of Hanoi city 2007 to 2015 based on a two endmember spectral mixture analysis (SMA) model
using multi-spectral and multi-temporal LANDSAT TM and OLI images. FVC was estimated for the
years of 2007 and 2015 by means of two endmember SMA based on NDVI, the assessment of FVC
changes was finally carried out. The study results show that: FVC was decreased with the total area of
699.8 km2, accounting for 75.5% of total area, decreased by 87.5 km2 per year in Soc Son’s south,
Dong Anh’s east, Gia Lam’s east and Thanh Tri’s west; some areas had medium and weak decrease
rate such as Cau Giay, North and South -Tu Liem and Soc Son’s west; total area of almost unchange
in FVC was 184.5 km2, accounting for 19.9% , occurring mainly in Ba Dinh, Dong Da, Hoan Kiem;
only 44.9 km2 was increased, accounting for 4.9% of total area, only 5.6 km2 per year, mainly
concentrated in the district of Hoang Mai, noth-eastern Soc Son, Dong Anh’s south.

LANDSAT images; fractional vegetation cover change, Ha Noi city


[1] Hoffmann, W. A., & Jackson, R. (2000). Vegetation-climate feedbacks in the conversion of tropical savanna to grassland. Journal of Climate, 13, 1593–1602.
[2] Ward, R. C., & Robinson, M. (2000). Principles of Hydrology (4th edition). McGraw hill. pp 450.
[3] Gutman, G.; Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, International Journal of Remote Sensing 1998, 19 (8), 1533-1543.
[4] Zeng, X., Dickinson, R. E., Walker, A., & Shaikh, M. (2000). Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. Journal of Applied Meteorology, 39, 826–839.
[5] Avissar, R; Pielke, R. A. A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Monthly Weather Review 1989, 117, 2113-2136.
[6] Trimble, S. W. Geomorphic effects of vegetation cover and management: some time and space considerations in prediction of erosion and sediment yield, in Vegetation and Erosion, edited by J. B. Thornes, London, John Wiley & Sons, 1990, pp. 55-66.
[7] Juan C. Jiménez-Muñoz, José A. Sobrino, Antonio Plaza, Luis Guanter, José Moreno and Pablo Martínez . Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area. Sensors, 2009, 9, 768–793.
[8] Ying Li, Hong Wang and Xiao Bing Li. Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model. PLoS One. 2015; 10(4): e0124608
[9] Zhang Y, Li X, Chen Y. Overview of field and multi-scale remote sensing measurement approaches. Advance Earth Sci. 2003; 18 (1): 85–93.
[10] Silván-Cárdenas JL, Wang L. Retrieval of subpixel Tamarix canopy cover from LANDSAT data along the Forgotten River using linear and nonlinear spectral mixture models. Remote Sens Environ. 2010; 114 (8): 1777–1790.
[11] Chen F, Qiu Q, Xiong Y, Huang S. Pixel unmixing based on linear spectral mixture model: methods and comparison. Remote Sens Info. 2010; (4): 22–28
[12] Xing Z, Feng Y, Yang G, Wang P, Huang W. Method of estimating vegetation coverage based on remote sensing. Remote Sens Tech Appl. 2009; 24 (6): 849–854.
[13] Li M. The method of vegetation fraction estimation by remote sensing. Beijing: Chinese Academy of Sciences; 2003.
[14] Li X. Quantitive retrieval of sparse vegetation cover in arid regions using hyperspectral data. Beijing: Chinese Acanemy of Forestry; 2008.
[15] Small, C. (2001). Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22, 1305–1334.
[16] Theseira, M. A., Thomas, G., & Sannier, C. A. D. (2002). An evaluation of spectral mixture modeling applied to a semi-arid environment. International Journal of Remote Sensing, 23, 687–700.
[17] https://www.usgs.gov/
[18] Chavez, P. S. Jr. (1996). Image-Based Atmospheric Corrections – Revisited and Improved. Photogrammetric Engineering and Remote Sensing 62(9), 1025-1036.
[19] Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P. and Scott, A. M. (2001). Classification and Change Detection Using LANDSAT TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment 75, 230-244.
[20] Van der Meer, F. 1999. Image classification through spectral unmixing. In: Spatial Statistics for Remote Sensing, Stein, A., Van der Meer, F. & Gorte, B. (Eds.) Kluwer Academic Publishers, Dordrecht, pp. 185-193.
[21] Deardorff, J. W. (1978). Efficient prediction of ground temperature and moisture with inclusion of a layer of vegetation. Journal of Geophysical Research, 83, 1889– 1903.
[22] Wittich, K. P., & Hansing, O. (1995). Area-averaged vegetative cover fraction estimated from satellite data. International Journal of Biometeorology, 38, 209–215.
[23] Rouse, J.W.; Haas (Jr.), R. H.; Schell, J. A.; Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. In Proc. ERTS-1 Symposium 3rd, Greenbelt, MD. 10–15 Dec. 1973. Vol. 1. NASA SP-351. NASA: Washington, DC, 1974.
[24] Sobrino, J. A., Raissouni, N. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco, International Journal of Remote Sensing 2000, 21 (2), 353-366.