21 January 2015 Monitoring urban impervious surface area change using China-Brazil Earth Resources Satellites and HJ-1 remote sensing images
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J. of Applied Remote Sensing, 9(1), 096094 (2015). doi:10.1117/1.JRS.9.096094
Impervious surface area (ISA) plays an important role in monitoring urbanization and related environmental changes, and has become a hotspot in urban and environmental studies. Xuzhou City, located in northwest Jiangsu Province, China, is chosen as the study area, and two scenes of China-Brazil Earth Resources Satellites images and one scene of HJ-1 image are employed to estimate ISA percentage and analyze the change trend from 2001 to 2009. Using a linear spectral mixture model (LSMM) and nonlinear backpropagation neural network (BPNN) method, all pixels are decomposed to derive four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects, and soil. The ISA percentage is then derived by the combination of high- and low-albedo fraction images after removing the influence of water. Some high spatial resolution images are selected to validate the ISA estimation results, and the experimental results indicate that the accuracy of BPNN is higher than LSMM. By comparing the urban ISA abundances derived by BPNN from three dates, it is found that the ISA of Xuzhou City has increased rapidly from 2001 to 2009, especially in the northeast and southeast regions, corresponding to the urban planning scheme and fast urbanization. Compared to other medium remote sensing images, the revisit cycle of HJ-1 multispectral image is only two days, demonstrating the potential of such data for ISA extraction in urbanization, disaster, and other related applications.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Peijun Du, Junshi Xia, Li Feng, "Monitoring urban impervious surface area change using China-Brazil Earth Resources Satellites and HJ-1 remote sensing images," Journal of Applied Remote Sensing 9(1), 096094 (21 January 2015). https://doi.org/10.1117/1.JRS.9.096094

Remote sensing


Spectral models

Spatial resolution

Satellite imaging

Data modeling


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