ABSTRACT Impervious surface plays an important role in monitoring urbanization and related enviro... more ABSTRACT Impervious surface plays an important role in monitoring urbanization and related environmental changes. CBERS and HJ-1 satellite images were employed to impervious surface extraction. Xuzhou City, located in the northwestern of Jiangsu Province, China, was chosen as the case study area. Using linear spectral mixture model (LSMM) and multi-layer perception (MLP) neural network, all pixels were decomposed to the four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects and soil. Then, the impervious surface area was derived by the combination of high- and low-albedo fraction images after removing the influence of water body. Furthermore, some high spatial resolution images were selected to validate the impervious surface estimation results of the two methods. Experimental results indicate that the accuracy of MLP neural network is higher than LSMM. By comparing the urban impervious surface area based on the MLP neural network from three remote sensing images, the change pattern of impervious surface area was studied. In the past years, the impervious surface has increased rapidly in Xuzhou City, especially in the northeast and southeast regions.
ABSTRACT The change of ecological environment resulted from urbanization is being paid more and m... more ABSTRACT The change of ecological environment resulted from urbanization is being paid more and more attentions. In this paper, Landsat TM/ETM+ remote sensing images captured on 1992, 2001 and 2006 were selected to investigate the urban growth and the corresponding ecological responses. Index-based Built-up Index (IBI) was used to extract urban built-up area and derive built-up area density. The quantitative analyzing among urban growth, urban heat island (UHI) and vegetation environment was explored based on urban-heat-island ratio index (URI) and vegetation eco-environment evaluation model. The results indicate that built-up area has increased rapidly in recent years, UHI effect has become more significant, and vegetation environment was subject to the breakage to a certain extent. The distribution of UHI and urban growth were highly consistent with the damage intensity of vegetation environment, which confirms that the main driving force of urban heat island and degradation of vegetation environment is urban growth.
In this paper, Pudong New Area in Shanghai was selected as the study area, and medium resolution ... more In this paper, Pudong New Area in Shanghai was selected as the study area, and medium resolution Landsat TM/ETM+ and CBERS (China-Brazil Earth Resources Satellite) images were used as data source. Two classification methods were applied to generate land cover maps: Maximum Likelihood Classifier (MLC) and a hierarchical method based on the V-I-S model (H-VIS). After comparing the results derived from these two methods, H-VIS model provides more accurate results than MLC. By analyzing the land cover change from 1989 to 2008, it was found that agricultural land has decreased greatly, while impervious surface area (ISA, including residential and commercial/industrial/traffic land) has increased year by year. In order to better monitor urbanization, diversity index, shape index, fractal dimension and isolation were selected to analyze the landscape pattern in the study area. The results show that the complexity of landscape structure and the fragmentation of the landscape increased from 1989 to 2008, however, the intensity and tendency of the landscape changes varied during the two comparative periods: 1989-2001 and 2001-2008. Finally, using data obtained from image interpretation and other data source, land cover change patterns and their driving forces, including economy, population and policies were analyzed.
Land cover in urban areas in China is changing rapidly during the past years as a result of urban... more Land cover in urban areas in China is changing rapidly during the past years as a result of urbanization. Changes detected from multi-temporal remote sensing images may help significantly in understanding urban development and supporting urban planning. Indeed, differences in reflectance spectra, easily obtained by satellite sensors, are important indicators for characterizing these changes. Although many algorithms were proposed to generate difference images, the results are usually greatly inconsistent. In this work, a complete procedure for land cover change detection by fusing change information obtained from multiple difference images is designed and implemented. Measurement and decision level fusion techniques are used to combine multiple difference images, and support vector machine (SVM) is selected to detect the changes. Multi-temporal CBERS images acquired in 2002 and 2008 are used to detect land cover changes and urban expansion in Shanghai, and experimental results confirm the effectiveness of the proposed approach. Using more change information, both the omission error and commission error could be reduced.
Decision level fusion, using a specific criterion or algorithm to integrate the classified result... more Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. In this paper, three decision level fusion methods and four schemes for input data are used to hyperspectral remote sensing image classification. Different feature combination and decision level fusion approaches are experimented and analyzed, and the results show that decision level fusion is effective to improve the performance of hyperspectral remote sensing image classification.
ABSTRACT Impervious surface plays an important role in monitoring urbanization and related enviro... more ABSTRACT Impervious surface plays an important role in monitoring urbanization and related environmental changes. CBERS and HJ-1 satellite images were employed to impervious surface extraction. Xuzhou City, located in the northwestern of Jiangsu Province, China, was chosen as the case study area. Using linear spectral mixture model (LSMM) and multi-layer perception (MLP) neural network, all pixels were decomposed to the four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects and soil. Then, the impervious surface area was derived by the combination of high- and low-albedo fraction images after removing the influence of water body. Furthermore, some high spatial resolution images were selected to validate the impervious surface estimation results of the two methods. Experimental results indicate that the accuracy of MLP neural network is higher than LSMM. By comparing the urban impervious surface area based on the MLP neural network from three remote sensing images, the change pattern of impervious surface area was studied. In the past years, the impervious surface has increased rapidly in Xuzhou City, especially in the northeast and southeast regions.
ABSTRACT The change of ecological environment resulted from urbanization is being paid more and m... more ABSTRACT The change of ecological environment resulted from urbanization is being paid more and more attentions. In this paper, Landsat TM/ETM+ remote sensing images captured on 1992, 2001 and 2006 were selected to investigate the urban growth and the corresponding ecological responses. Index-based Built-up Index (IBI) was used to extract urban built-up area and derive built-up area density. The quantitative analyzing among urban growth, urban heat island (UHI) and vegetation environment was explored based on urban-heat-island ratio index (URI) and vegetation eco-environment evaluation model. The results indicate that built-up area has increased rapidly in recent years, UHI effect has become more significant, and vegetation environment was subject to the breakage to a certain extent. The distribution of UHI and urban growth were highly consistent with the damage intensity of vegetation environment, which confirms that the main driving force of urban heat island and degradation of vegetation environment is urban growth.
In this paper, Pudong New Area in Shanghai was selected as the study area, and medium resolution ... more In this paper, Pudong New Area in Shanghai was selected as the study area, and medium resolution Landsat TM/ETM+ and CBERS (China-Brazil Earth Resources Satellite) images were used as data source. Two classification methods were applied to generate land cover maps: Maximum Likelihood Classifier (MLC) and a hierarchical method based on the V-I-S model (H-VIS). After comparing the results derived from these two methods, H-VIS model provides more accurate results than MLC. By analyzing the land cover change from 1989 to 2008, it was found that agricultural land has decreased greatly, while impervious surface area (ISA, including residential and commercial/industrial/traffic land) has increased year by year. In order to better monitor urbanization, diversity index, shape index, fractal dimension and isolation were selected to analyze the landscape pattern in the study area. The results show that the complexity of landscape structure and the fragmentation of the landscape increased from 1989 to 2008, however, the intensity and tendency of the landscape changes varied during the two comparative periods: 1989-2001 and 2001-2008. Finally, using data obtained from image interpretation and other data source, land cover change patterns and their driving forces, including economy, population and policies were analyzed.
Land cover in urban areas in China is changing rapidly during the past years as a result of urban... more Land cover in urban areas in China is changing rapidly during the past years as a result of urbanization. Changes detected from multi-temporal remote sensing images may help significantly in understanding urban development and supporting urban planning. Indeed, differences in reflectance spectra, easily obtained by satellite sensors, are important indicators for characterizing these changes. Although many algorithms were proposed to generate difference images, the results are usually greatly inconsistent. In this work, a complete procedure for land cover change detection by fusing change information obtained from multiple difference images is designed and implemented. Measurement and decision level fusion techniques are used to combine multiple difference images, and support vector machine (SVM) is selected to detect the changes. Multi-temporal CBERS images acquired in 2002 and 2008 are used to detect land cover changes and urban expansion in Shanghai, and experimental results confirm the effectiveness of the proposed approach. Using more change information, both the omission error and commission error could be reduced.
Decision level fusion, using a specific criterion or algorithm to integrate the classified result... more Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. In this paper, three decision level fusion methods and four schemes for input data are used to hyperspectral remote sensing image classification. Different feature combination and decision level fusion approaches are experimented and analyzed, and the results show that decision level fusion is effective to improve the performance of hyperspectral remote sensing image classification.
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Papers by Junshi Xia