Estimation of Impervious Surface Distribution by Linear Spectral Mixture Analysis: A Case Study in Nantong, China

2019 ◽  
pp. 41-51
Author(s):  
Ping Duan ◽  
Jia Li ◽  
Xiu Lu ◽  
Cheng Feng
Keyword(s):  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Surface Distribution ◽  
Linear Spectral Mixture Analysis ◽  
Spectral Mixture

scholarly journals Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2873 ◽  
Author(s):  
Rudong Xu ◽  
Jin Liu ◽  
Jianhui Xu
Keyword(s):  
High Precision ◽  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Mixture Analysis ◽  
Spectral Bands ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

Analysis on Spatial Pattern of Urban Heat Island and Impervious Surface Using Linear Spectral Mixture Analysis

2011 ◽  
Vol 216 ◽  
pp. 600-604
Author(s):  
Jing Hu Pan ◽  
Pei Ji Shi ◽  
Feng Juan Zheng
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Heat Island ◽  
Mixture Analysis ◽  
Linear Spectral Mixture Analysis ◽  
Urban Heat ◽  
Space Dependence ◽  
Spectral Mixture

Based on Landsat ETM+ data within the metropolitan area of Lanzhou, China, green vegetation(GV) and impervious surface was extracted by a constrained linear spectral mixture analysis (LSMA),together with single window algorithm to invert land surface temperature ,and the correlation analysis was then conducted to examine the relationship between urban heat island (UHI) effect and impervious surface. Four types of end members with high albedo, GV, soil and low albedo are selected to model complicated urban land cover, estimation accuracy is assessed using Root-Mean-Square (RMS)error and color aerial images, with the help of Mantel and Partial Mantel. Spatial relationship of land surface temperatures (LST), impervious surface and GV were analyzed. Results indicate that impervious surface distribution and GV can be derived from Landsat TM/ETM+ images with satisfactory precision. Impervious surface and GV were positively correlated with UHI, while LST has space dependence, it has high space dependence, and was higher correlated with impervious surface than GV.

scholarly journals Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

2019 ◽  
Vol 11 (22) ◽  
pp. 6227
Author(s):  
Xiaodong Huang ◽  
Wenkai Liu ◽  
Yuping Han ◽  
Chunying Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Impervious Surface ◽  
Thematic Mapper ◽  
Spectral Mixture Analysis ◽  
Landsat 8 ◽  
Heat Island ◽  
Mixture Analysis ◽  
Linear Spectral Mixture Analysis ◽  
Urban Heat ◽  
Spectral Mixture

Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.

scholarly journals Impervious Surface Extraction by Linear Spectral Mixture Analysis with Post-Processing Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128476-128489
Author(s):  
Yi Zhao ◽  
Jianhui Xu ◽  
Kaiwen Zhong ◽  
Yunpeng Wang ◽  
Hongda Hu ◽  
...  
Keyword(s):  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Post Processing ◽  
Mixture Analysis ◽  
Surface Extraction ◽  
Linear Spectral Mixture Analysis ◽  
Spectral Mixture
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