21 June 2012 Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam
Author Affiliations +
Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)
Nguyen-Thanh Son, Nguyen-Thanh Son, Chi-Farn Chen, Chi-Farn Chen, Li-Yu Chang, Li-Yu Chang, Cheng-Ru Chen, Cheng-Ru Chen, Bui-Xuan Thanh, Bui-Xuan Thanh, } "Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam," Journal of Applied Remote Sensing 6(1), 063543 (21 June 2012). https://doi.org/10.1117/1.JRS.6.063543 . Submission:


Techniques For Multispectral Image Classification
Proceedings of SPIE (July 21 1985)
Parcel-based change detection
Proceedings of SPIE (December 29 1994)

Back to Top