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Wenliang Liu
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: 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: 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.

Journal article
Published: 01 April 2018 in ISPRS International Journal of Geo-Information
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Identifying urban built-up area boundaries is critical to urban data statistics, size measurement, and spatial control. However, previous methods of extracting urban built-up area boundaries based on low-resolution remote-sensing data are frequently constrained by data accuracy. In this paper, a new method for extracting urban built-up area boundaries using high-resolution remote sensing images based on scale effects is proposed. Firstly, we generate a number of different levels of edge-multiplied hexagonal vector grids. Secondly, the impervious surface densities are calculated based on the hexagonal vector grids with the longest edge. Then, the hexagonal grids with higher impervious surface densities are extracted as the built-up area of the first level. Thirdly, we gradually reduce the spatial scale of the hexagonal vector grid and repeat the extraction process based on the extracted built-up area in the previous step. Eventually, we obtain the urban built-up area boundary at the smallest scale. Plausibility checks indicate that the suggested method not only guarantees the spatial continuity of the resultant urban built-up area boundary, but also highlights the prevailing orientation of urban expansion. The extracted Beijing built-up area boundary can serve as a reference in decision-making for space planning and land-use control.

ACS Style

Yi Zhou; Mingguang Tu; Shixin Wang; Wenliang Liu. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS International Journal of Geo-Information 2018, 7, 135 .

AMA Style

Yi Zhou, Mingguang Tu, Shixin Wang, Wenliang Liu. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS International Journal of Geo-Information. 2018; 7 (4):135.

Chicago/Turabian Style

Yi Zhou; Mingguang Tu; Shixin Wang; Wenliang Liu. 2018. "A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect." ISPRS International Journal of Geo-Information 7, no. 4: 135.

Journal article
Published: 23 November 2016 in Remote Sensing
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Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B1–4) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B1–4, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B1–4, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features.

ACS Style

Yi Zhou; Chenxi Lin; Shixin Wang; Wenliang Liu; Ye Tian. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data. Remote Sensing 2016, 8, 969 .

AMA Style

Yi Zhou, Chenxi Lin, Shixin Wang, Wenliang Liu, Ye Tian. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data. Remote Sensing. 2016; 8 (11):969.

Chicago/Turabian Style

Yi Zhou; Chenxi Lin; Shixin Wang; Wenliang Liu; Ye Tian. 2016. "Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data." Remote Sensing 8, no. 11: 969.

Journal article
Published: 21 October 2016 in Sensors
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Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.

ACS Style

Shixin Wang; Ye Tian; Yi Zhou; Wenliang Liu; Chenxi Lin. Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings. Sensors 2016, 16, 1755 .

AMA Style

Shixin Wang, Ye Tian, Yi Zhou, Wenliang Liu, Chenxi Lin. Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings. Sensors. 2016; 16 (10):1755.

Chicago/Turabian Style

Shixin Wang; Ye Tian; Yi Zhou; Wenliang Liu; Chenxi Lin. 2016. "Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings." Sensors 16, no. 10: 1755.

Journal article
Published: 07 September 2015 in International Journal of Environmental Research and Public Health
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According to the framework of “Pressure-State-Response”, this study established an indicator system which can reflect comprehensive risk of environment and health for an area at large scale. This indicator system includes 17 specific indicators covering social and economic development, pollution emission intensity, air pollution exposure, population vulnerability, living standards, medical and public health, culture and education. A corresponding weight was given to each indicator through Analytical Hierarchy Process (AHP) method. Comprehensive risk assessment of the environment and health of 58 counties was conducted in the Jiangsu province, China, and the assessment result was divided into four types according to risk level. Higher-risk counties are all located in the economically developed southern region of Jiangsu province and relatively high-risk counties are located along the Yangtze River and Xuzhou County and its surrounding areas. The spatial distribution of relatively low-risk counties is dispersive, and lower-risk counties mainly located in the middle region where the economy is somewhat weaker in the province. The assessment results provide reasonable and scientific basis for Jiangsu province Government in formulating environment and health policy. Moreover, it also provides a method reference for the comprehensive risk assessment of environment and health within a large area (provinces, regions and countries).

ACS Style

Shujie Zhang; ZhengZheng Wei; Wenliang Liu; Ling Yao; Wenyu Suo; Jingjing Xing; Bingzhao Huang; Di Jin; Jiansheng Wang. Indicators for Environment Health Risk Assessment in the Jiangsu Province of China. International Journal of Environmental Research and Public Health 2015, 12, 11012 -11024.

AMA Style

Shujie Zhang, ZhengZheng Wei, Wenliang Liu, Ling Yao, Wenyu Suo, Jingjing Xing, Bingzhao Huang, Di Jin, Jiansheng Wang. Indicators for Environment Health Risk Assessment in the Jiangsu Province of China. International Journal of Environmental Research and Public Health. 2015; 12 (9):11012-11024.

Chicago/Turabian Style

Shujie Zhang; ZhengZheng Wei; Wenliang Liu; Ling Yao; Wenyu Suo; Jingjing Xing; Bingzhao Huang; Di Jin; Jiansheng Wang. 2015. "Indicators for Environment Health Risk Assessment in the Jiangsu Province of China." International Journal of Environmental Research and Public Health 12, no. 9: 11012-11024.