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Dr. Jun Zhang
Yunnan University

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Research Keywords & Expertise

0 GIS and Remote Sensing
0 Geography, urban transformation
0 Big Data Analysis
0 Urban and Regional Plannning
0 geo-environmental

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Journal article
Published: 09 July 2021 in Water
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As one of the most important causes of water quality deterioration, NPS (non-point source) pollution has become an urgent environmental and livelihood issue. To date, there have been only a few studies focusing on NPS pollution conforming to the estimation, and the pollution sources are mainly concentrated in nitrogen and phosphorus nutrients. Unlike studies that only consider the intensity of nitrogen and phosphorus loads, the NPS pollution risk for the China’s Fuxian Lake Basin was evaluated in this study by using IECM (Improve Export Coefficient Model) and RUSLE (Revised Universal Soil Loss Equation) models to estimate nitrogen and phosphorus loads and soil loss and by using a multi-factor NPS pollution risk assessment index established on the basis of the data mentioned above. First, the results showed that the load intensity of nitrogen and phosphorus pollution in the Fuxian Lake Basin is low, so agricultural production and life are important sources of pollution. Second, the soil loss degree of erosion in the Fuxian Lake is mild, so topography is one of the most important factors affecting soil erosion. Third, the risk of NPS pollution in the Fuxian Lake Basin is at a medium level and its spatial distribution characteristics are similar to the intensity characteristics of nitrogen and phosphorus loss. Nitrogen, phosphorus, sediment, and mean concentrations are important factors affecting NPS pollution. These factors involve both natural and man-made environments. Therefore, it is necessary to comprehensively consider the factors affecting NPS in order to assess the NPS risk more accurately, as well as to better solve the problem of ecological pollution of water resources and to allow environmental restoration.

ACS Style

Xiaodie Yuan; Zhang Jun. Water Resource Risk Assessment Based on Non-Point Source Pollution. Water 2021, 13, 1907 .

AMA Style

Xiaodie Yuan, Zhang Jun. Water Resource Risk Assessment Based on Non-Point Source Pollution. Water. 2021; 13 (14):1907.

Chicago/Turabian Style

Xiaodie Yuan; Zhang Jun. 2021. "Water Resource Risk Assessment Based on Non-Point Source Pollution." Water 13, no. 14: 1907.

Journal article
Published: 05 July 2021 in International Journal of Environmental Research and Public Health
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As one of the most important methods for limiting urban sprawl, the accurate delineation of the urban–rural boundary not only promotes the intensive use of urban resources, but also helps to alleviate the urban issues caused by urban sprawl, realizing the intensive and healthy development of urban cities. Previous studies on delineating urban–rural boundaries were only based on the level of urban and rural development reflected by night-time light (NTL) data, ignoring the differences in the spatial development between urban and rural areas; so, the comprehensive consideration of NTL and point of interest (POI) data can help improve the accuracy of urban–rural boundary delineation. In this study, the NTL and POI data were fused using wavelet transform, and then the urban–rural boundary before and after data fusion was delineated by multiresolution segmentation. Finally, the delineation results were verified. The verification result shows that the accuracy of delineating the urban–rural boundary using only NTL data is 84.20%, and the Kappa value is 0.6549; the accuracy using the fusion of NTL and POI data on the basis of wavelet transform is 93.2%, and the Kappa value is 0.8132. Therefore, we concluded that the proposed method of using wavelet transform to fuse NTL and POI data considers the differences between urban and rural development, which significantly improves the accuracy of the delineation of urban–rural boundaries. Accurate delineation of urban–rural boundaries is helpful for optimizing internal spatial structure in both urban and rural areas, alleviating environmental problems resulting from urban development, assisting the formulation of development policies for urban and rural fringes, and promoting the intensive and healthy development of urban areas.

ACS Style

Jun Zhang; Xiaodie Yuan; Xueping Tan; Xue Zhang. Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development. International Journal of Environmental Research and Public Health 2021, 18, 7180 .

AMA Style

Jun Zhang, Xiaodie Yuan, Xueping Tan, Xue Zhang. Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development. International Journal of Environmental Research and Public Health. 2021; 18 (13):7180.

Chicago/Turabian Style

Jun Zhang; Xiaodie Yuan; Xueping Tan; Xue Zhang. 2021. "Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development." International Journal of Environmental Research and Public Health 18, no. 13: 7180.

Journal article
Published: 09 April 2021 in Sustainability
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As the most infectious disease in 2020, COVID-19 is an enormous shock to urban public health security and to urban sustainable development. Although the epidemic in China has been brought into control at present, the prevention and control of it is still the top priority of maintaining public health security. Therefore, the accurate assessment of epidemic risk is of great importance to the prevention and control even to overcoming of COVID-19. Using the fused data obtained from fusing multi-source big data such as POI (Point of Interest) data and Tencent-Yichuxing data, this study assesses and analyzes the epidemic risk and main factors that affect the distribution of COVID-19 on the basis of combining with logistic regression model and geodetector model. What’s more, the following main conclusions are obtained: the high-risk areas of the epidemic are mainly concentrated in the areas with relatively dense permanent population and floating population, which means that the permanent population and floating population are the main factors affecting the risk level of the epidemic. In other words, the reasonable control of population density is greatly conducive to reducing the risk level of the epidemic. Therefore, the control of regional population density remains the key to epidemic prevention and control, and home isolation is also the best means of prevention and control. The precise assessment and analysis of the epidemic conducts by this study is of great significance to maintain urban public health security and achieve the sustainable urban development.

ACS Style

Jun Zhang; Xiaodie Yuan. COVID-19 Risk Assessment: Contributing to Maintaining Urban Public Health Security and Achieving Sustainable Urban Development. Sustainability 2021, 13, 4208 .

AMA Style

Jun Zhang, Xiaodie Yuan. COVID-19 Risk Assessment: Contributing to Maintaining Urban Public Health Security and Achieving Sustainable Urban Development. Sustainability. 2021; 13 (8):4208.

Chicago/Turabian Style

Jun Zhang; Xiaodie Yuan. 2021. "COVID-19 Risk Assessment: Contributing to Maintaining Urban Public Health Security and Achieving Sustainable Urban Development." Sustainability 13, no. 8: 4208.

Journal article
Published: 25 January 2021 in IEEE Access
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With the urban built-up area becoming one of the most prominent forms of urban expansion, accurately extracting the urban built-up area is becoming more and more important to judge the urbanization process and evaluate the urban environment. Since it is difficult to significantly improve the accuracy of single satellite data in the extraction of urban built-up areas, this study proposes a method integrating POI (Point of Interest) data and Luojia-1A data to improve the extraction accuracy of urban built-up areas. In this study, integrated Density Graph, OSTU and geometric mean are used to extract urban built-up areas respectively. The highest precision of urban built-up areas extracted before data integration is 74.3% with the highest Kappa value of 0.54; while the highest precision of urban built-up area extracted after data integration is 91.4%, with the highest Kappa value of 0.91. Therefore, it can be concluded that compared with the existing widely used night-light-based methods, this method can integrate the advantages of POI data and Luojia-1A data, that is, it can not only solve the long-term oversaturation phenomenon of night light data, but also can extract urban built-up areas in a more refined way. The method used in this study, which integrates POI data and Luojia-1A data, can not only provide a new method for urban built-up area extraction, but also can play an active guiding role in urban planning and construction.

ACS Style

Zhang Jun; Yuan Xiao-Die; Lin Han. The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China. IEEE Access 2021, 9, 22417 -22429.

AMA Style

Zhang Jun, Yuan Xiao-Die, Lin Han. The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China. IEEE Access. 2021; 9 ():22417-22429.

Chicago/Turabian Style

Zhang Jun; Yuan Xiao-Die; Lin Han. 2021. "The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China." IEEE Access 9, no. : 22417-22429.

Journal article
Published: 27 November 2020 in Remote Sensing
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Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.

ACS Style

Xiong He; Chunshan Zhou; Jun Zhang; Xiaodie Yuan. Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas. Remote Sensing 2020, 12, 3887 .

AMA Style

Xiong He, Chunshan Zhou, Jun Zhang, Xiaodie Yuan. Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas. Remote Sensing. 2020; 12 (23):3887.

Chicago/Turabian Style

Xiong He; Chunshan Zhou; Jun Zhang; Xiaodie Yuan. 2020. "Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas." Remote Sensing 12, no. 23: 3887.