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Shixin Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

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Journal article
Published: 06 April 2021 in Sustainability
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Measuring the regionally coordinated development degree quantitively at an urban agglomeration scale is vital for regional sustainable development. To date, existing studies mainly utilized statistical data to analyze coordinated development degrees between different subsystems, which failed to measure the development gap of subsystems between cities. This study integrated remote sensing and statistical data to evaluate the development degree from six subsystems. The coordinated index (CI) and coordinated development index (CDI) were then promoted to assess the coordinated degree and coordinated development degree. The main findings were: (1) The coordinated development degree of Jing-Jin-Ji (JJJ) had increased from 0.4616 in 2000 to 0.6099 in 2015, with the corresponding grade improvement from “moderate” to “good”; (2) JJJ and six subsystems’ development degree showed an increasing trend. JJJ’s whole development degree had improved from 0.34 to 0.52, and the grade had changed from “fair” to “moderate”; (3) The coordinated degree of JJJ displayed a “V” shape. However, the coordinated degree was lower in 2015 than in 2000.

ACS Style

Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability 2021, 13, 4075 .

AMA Style

Jianwan Ji, Shixin Wang, Yi Zhou, Wenliang Liu, Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability. 2021; 13 (7):4075.

Chicago/Turabian Style

Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. 2021. "Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015." Sustainability 13, no. 7: 4075.

Journal article
Published: 27 December 2019 in Remote Sensing
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As the backbone and arteries of a comprehensive transportation network, highways play an important role in improving people’s living standards and promoting economic growth. However, globally, there is limited quantifiable data evaluating the highway traffic state, characteristics, and performance. From the 1960s to the present, remote sensing has been regarded as the most effective technology for long-term and large-scale monitoring of surface information. However, how to reflect the dynamic “flow” information of traffic with a static remote sensing image has always been a difficult problem that is hard to solve in the field. This study aims to construct a method of evaluating highway traffic prosperity using nighttime remote sensing. First, based on nighttime light data that indicate social and economic activities, a highway-oriented method was proposed to extract highway nighttime light data from 2015 annual nighttime light data of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (SNPP-VIIRS). Subsequently, Pearson correlation analysis was used to fit the relationship between freeway traffic flow volume and freeway nighttime light at the provincial level. The results showed that Pearson Correlation Coefficient of freeway nighttime light and freeway traffic flow volume for coach and truck are 0.905 and 0.731, respectively, which are higher than between freeway traffic flow volume for coach and truck and total nighttime light (0.593 and 0.516, respectively). A new index—Highway Nighttime Traffic Prosperity Index (HNTPI)—was proposed to evaluate highway traffic across China. The results showed that HNTPI has a strong correspondence with socio-economic parameters. The Pearson Correlation Coefficient of HNTPI and gross domestic product (GDP) per capita, consumption per capita, and population are 0.772, 0.895, and 0.968, respectively. There is a huge spatial heterogeneity in China nighttime traffic, the prosperity degree of highway traffic in developed coastal areas is obviously higher than that inland. The national general highway is the most prosperous highway at night and the national general highway nighttime prosperity of Shanghai reached 22.34%. This research provides basic data for the long-term monitoring and evaluation of regional traffic operation at night and research on the correlation between regional highway construction and the economy.

ACS Style

Ying Chang; Shixin Wang; Yi Zhou; Litao Wang; Futao Wang. A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing. Remote Sensing 2019, 12, 102 .

AMA Style

Ying Chang, Shixin Wang, Yi Zhou, Litao Wang, Futao Wang. A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing. Remote Sensing. 2019; 12 (1):102.

Chicago/Turabian Style

Ying Chang; Shixin Wang; Yi Zhou; Litao Wang; Futao Wang. 2019. "A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing." Remote Sensing 12, no. 1: 102.

Research article
Published: 26 November 2019 in Earth Science Informatics
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Road blockage information extraction from a single-phase postdisaster image is difficult because roads are narrow and easily covered by vegetation. The traditional object-oriented image analysis method is restrictive, and its detection is slow. A deep learning algorithm, i.e., the convolution neural network (CNN), is applied to rapidly extract road blockage information. An algorithm for sample generation is designed to construct a typical sample library for CNN training, and an appropriate CNN structure and a complete detection process are designed to extract road blockage information. Finally, by taking the Jiuzhaigou earthquake on August 8, 2017, as an example, experimental verification is carried out. The kappa coefficient and the F1 score of the results are 77.60% and 87.95%, respectively. The extraction of road blockages can be completed with an efficiency of 14.59 km2 per hour. The requirements for disaster emergency monitoring can be met by the accuracy and efficiency of this method, which are better than those of the traditional object-oriented method.

ACS Style

Baolin Yang; Shixin Wang; Yi Zhou; Futao Wang; Qiao Hu; Ying Chang; Qing Zhao. Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images. Earth Science Informatics 2019, 13, 115 -127.

AMA Style

Baolin Yang, Shixin Wang, Yi Zhou, Futao Wang, Qiao Hu, Ying Chang, Qing Zhao. Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images. Earth Science Informatics. 2019; 13 (1):115-127.

Chicago/Turabian Style

Baolin Yang; Shixin Wang; Yi Zhou; Futao Wang; Qiao Hu; Ying Chang; Qing Zhao. 2019. "Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images." Earth Science Informatics 13, no. 1: 115-127.

Journal article
Published: 04 November 2019 in Global Ecology and Conservation
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ACS Style

Litao Wang; Shixin Wang; Yi Zhou; Jinfeng Zhu; Jiazhen Zhang; Yanfang Hou; Wenliang Liu. Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China. Global Ecology and Conservation 2019, 21, 1 .

AMA Style

Litao Wang, Shixin Wang, Yi Zhou, Jinfeng Zhu, Jiazhen Zhang, Yanfang Hou, Wenliang Liu. Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China. Global Ecology and Conservation. 2019; 21 ():1.

Chicago/Turabian Style

Litao Wang; Shixin Wang; Yi Zhou; Jinfeng Zhu; Jiazhen Zhang; Yanfang Hou; Wenliang Liu. 2019. "Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China." Global Ecology and Conservation 21, no. : 1.

Journal article
Published: 23 January 2019 in Remote Sensing
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Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis.

ACS Style

Yao Yao; Shixin Wang. Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern. Remote Sensing 2019, 11, 231 .

AMA Style

Yao Yao, Shixin Wang. Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern. Remote Sensing. 2019; 11 (3):231.

Chicago/Turabian Style

Yao Yao; Shixin Wang. 2019. "Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern." Remote Sensing 11, no. 3: 231.

Journal article
Published: 22 June 2018 in Sensors
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With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.

ACS Style

Yi Zhou; Rui Zhang; Shixin Wang; Futao Wang. Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors 2018, 18, 2013 .

AMA Style

Yi Zhou, Rui Zhang, Shixin Wang, Futao Wang. Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors. 2018; 18 (7):2013.

Chicago/Turabian Style

Yi Zhou; Rui Zhang; Shixin Wang; Futao Wang. 2018. "Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy." Sensors 18, no. 7: 2013.

Journal article
Published: 09 June 2018 in Sustainability
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The Chinese government has promulgated a de-capacity policy for economic growth and environmental sustainability, especially for the iron and steel industry. With these policies, this study aimed to monitor the economic activities and evaluate the production conditions of an iron and steel factory based on satellites via Landsat-8 Thermal Infrared Sensor (TIRS) data and high-resolution images from January 2013 to October 2017, and propel next economic adjustment and environmental protection. Our methods included the construction of a heat island intensity index for an iron and steel factory (ISHII), a heat island radio index for an iron and steel factory (ISHRI) and a dense classifying approach to monitor the spatiotemporal changes of the internal heat field of an iron and steel factory. Additionally, we used GF-2 and Google Earth images to identify the main production area, detect facility changes to a factory that alters its heat field and verify the accuracy of thermal analysis in a specific time span. Finally, these methods were used together to evaluate economic activity. Based on five iron and steel factories in the Beijing-Tianjin-Hebei region, when the ISHII curve is higher than the seasonal changes in a time series, production is normal; otherwise, there is a shut-down or cut-back. In the spatial pattern analyses, the ISHRI is large in normal production and decreases when cut-back or shut-down occurs. The density classifying images and high-resolution images give powerful evidence to the above-mentioned results. Finally, three types of economic activities of normal production, shut-down or cut-back were monitored for these samples. The study provides a new perspective and method for monitoring the economic activity of an iron and steel factory and provides supports for sustainable development in China.

ACS Style

Yi Zhou; Fei Zhao; Shixin Wang; Wenliang Liu; Litao Wang. A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites. Sustainability 2018, 10, 1935 .

AMA Style

Yi Zhou, Fei Zhao, Shixin Wang, Wenliang Liu, Litao Wang. A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites. Sustainability. 2018; 10 (6):1935.

Chicago/Turabian Style

Yi Zhou; Fei Zhao; Shixin Wang; Wenliang Liu; Litao Wang. 2018. "A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites." Sustainability 10, no. 6: 1935.

Original articles
Published: 31 May 2018 in International Journal of Digital Earth
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Many experiments of object-based image analysis have been conducted in remote sensing classification. However, they commonly used high-resolution imagery and rarely focused on suburban area. In this research, with the Landsat-8 imagery, classification of a suburban area via the object-based approach is achieved using four classifiers, including decision tree (DT), support vector machine (SVM), random trees (RT), and naive Bayes (NB). We performed feature selection at different sizes of segmentation scale and evaluated the effects of segmentation and tuning parameters within each classifier on classification accuracy. The results showed that the influence of shape on overall accuracy was greater than that of compactness, and a relatively low value of shape should be set with increasing scale size. For DT, the optimal maximum depth usually varied from 5 to 8. For SVM, the optimal gamma was less than or equal to 10−2, and its optimal C was greater than or equal to 102. For RT, the optimal active variables was less than or equal to 4, and the optimal maximum tree number was greater than or equal to 30. Furthermore, although there was no statistically significant difference between some classification results produced using different classifiers, SVM has a slightly better performance.

ACS Style

Ming Shang; Shixin Wang; Yi Zhou; Cong Du; Wenliang Liu. Object-based image analysis of suburban landscapes using Landsat-8 imagery. International Journal of Digital Earth 2018, 12, 720 -736.

AMA Style

Ming Shang, Shixin Wang, Yi Zhou, Cong Du, Wenliang Liu. Object-based image analysis of suburban landscapes using Landsat-8 imagery. International Journal of Digital Earth. 2018; 12 (6):720-736.

Chicago/Turabian Style

Ming Shang; Shixin Wang; Yi Zhou; Cong Du; Wenliang Liu. 2018. "Object-based image analysis of suburban landscapes using Landsat-8 imagery." International Journal of Digital Earth 12, no. 6: 720-736.

Journal article
Published: 14 May 2017 in Remote Sensing
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Estimates of regional net primary productivity (NPP) are useful in modeling regional and global carbon cycles, especially in karst areas. This work developed a new method to study NPP characteristics and changes in Chongqing, a typical karst area. To estimate NPP accurately, the model which integrated an ecosystem process model (CEVSA) with a light use efficiency model (GLOPEM) called GLOPEM-CEVSA was applied. The fraction of photosynthetically active radiation (fPAR) was derived from remote sensing data inversion based on moderate resolution imaging spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values, which were simulated by the model, matched the observed data well. The values of other relevant NPP models, as well as the MOD17A3 NPP products (NPP MOD17), were compared. In terms of spatial distribution, NPP decreased from northeast to southwest in the Chongqing region. The annual average NPP in the study area was approximately 534 gC/m2a (Std. = 175.53) from 2001 to 2011, with obvious seasonal variation characteristics. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. NPP changed with the fraction of absorbed PAR, and NPP was also significantly correlated to precipitation and temperature at monthly temporal scales, and showed stronger sensitivity to interannual variation in temperature.

ACS Style

Rui Zhang; Yi Zhou; Hongxia Luo; Futao Wang; Shixin Wang. Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area. Remote Sensing 2017, 9, 477 .

AMA Style

Rui Zhang, Yi Zhou, Hongxia Luo, Futao Wang, Shixin Wang. Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area. Remote Sensing. 2017; 9 (5):477.

Chicago/Turabian Style

Rui Zhang; Yi Zhou; Hongxia Luo; Futao Wang; Shixin Wang. 2017. "Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area." Remote Sensing 9, no. 5: 477.