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Bailang Yu
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, and Research Center for China Administrative Division, East China Normal University

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Articles
Published: 11 June 2021 in Annals of the American Association of Geographers
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An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales.

ACS Style

Congxiao Wang; Bailang Yu; Zuoqi Chen; Yan Liu; Wei Song; Xia Li; Chengshu Yang; Christopher Small; Song Shu; Jianping Wu. Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data. Annals of the American Association of Geographers 2021, 1 -22.

AMA Style

Congxiao Wang, Bailang Yu, Zuoqi Chen, Yan Liu, Wei Song, Xia Li, Chengshu Yang, Christopher Small, Song Shu, Jianping Wu. Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data. Annals of the American Association of Geographers. 2021; ():1-22.

Chicago/Turabian Style

Congxiao Wang; Bailang Yu; Zuoqi Chen; Yan Liu; Wei Song; Xia Li; Chengshu Yang; Christopher Small; Song Shu; Jianping Wu. 2021. "Evolution of Urban Spatial Clusters in China: A Graph-Based Method Using Nighttime Light Data." Annals of the American Association of Geographers , no. : 1-22.

Journal article
Published: 06 May 2021 in Journal of Remote Sensing
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Building-level population data are of vital importance in disaster management, homeland security, and public health. Remotely sensed data, especially LiDAR data, which allow measures of three-dimensional morphological information, have been shown to be useful for fine-scale population estimations. However, studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution. In this article, we proposed a framework to estimate population at the building level by integrating POI data, nighttime light (NTL) data, and LiDAR data. Building objects were first derived using LiDAR data and aerial photographs. Then, three categories of building-level features, including geometric features, nighttime light intensity features, and POI features, were, respectively, extracted from LiDAR data, Luojia1-01 NTL data, and POI data. Finally, a well-trained random forest model was built to estimate the population of each individual building. Huangpu District in Shanghai, China, was chosen to validate the proposed method. A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R2=0.65 at the building level and R2=0.79 at the community level. The NTL radiance intensity was found to have a positive relationship with population in residential areas, while a negative relationship was found in office and commercial areas. Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data, the accuracy of building-level population estimation can be improved.

ACS Style

Hongxing Chen; Bin Wu; Bailang Yu; Zuoqi Chen; Qiusheng Wu; Ting Lian; Congxiao Wang; Qiaoxuan Li; Jianping Wu. A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data. Journal of Remote Sensing 2021, 2021, 1 -17.

AMA Style

Hongxing Chen, Bin Wu, Bailang Yu, Zuoqi Chen, Qiusheng Wu, Ting Lian, Congxiao Wang, Qiaoxuan Li, Jianping Wu. A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data. Journal of Remote Sensing. 2021; 2021 ():1-17.

Chicago/Turabian Style

Hongxing Chen; Bin Wu; Bailang Yu; Zuoqi Chen; Qiusheng Wu; Ting Lian; Congxiao Wang; Qiaoxuan Li; Jianping Wu. 2021. "A New Method for Building-Level Population Estimation by Integrating LiDAR, Nighttime Light, and POI Data." Journal of Remote Sensing 2021, no. : 1-17.

Data description paper
Published: 05 March 2021 in Earth System Science Data
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The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

ACS Style

Zuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Shenjun Yao; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data 2021, 13, 889 -906.

AMA Style

Zuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Congxiao Wang, Bin Wu, Jianping Wu. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data. 2021; 13 (3):889-906.

Chicago/Turabian Style

Zuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Shenjun Yao; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. 2021. "An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration." Earth System Science Data 13, no. 3: 889-906.

Journal article
Published: 18 December 2020 in Remote Sensing of Environment
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No single satellite remote sensing system is able to provide the observations on the Earth's surface at both high spatial and high temporal resolution due to the general trade-off between orbit revisit frequency and satellite sensor's spatial resolution. This paper presents a spatio-temporal Cokriging (ST-Cokriging) method for assimilating remote sensing data sets acquired by multiple remote sensing systems with different temporal sampling frequencies and different spatial resolutions. By extending the traditional Cokriging technique from a sole spatial domain to a spatio-temporal domain, we derived and implemented ST-Cokriging algorithm that explicitly takes the spatial covariance, temporal covariance and spatio-temporal covariance structures within and between different data sets into account. Compared with previous downscaling methods, such as, STARFM and FSDAF, our ST-Cokriging method produces more accurate and reliable assimilation results for the heterogeneous region, with associated uncertainty estimates. This method has been implemented into a software package using Python language within ArcGIS environment. The advantages and effectiveness of our ST-Cokriging method have been demonstrated through an application example, in which MODIS images (daily, 250 m and 500 m spatial resolution) and Landsat TM/ETM+ images (16 days revisit cycle, 30 m) are assimilated to generate daily spectral bands and NDVI images at 30 m spatial resolution. Our validation and accuracy assessments indicate that our ST-Cokriging method can effectively fill in data gaps due to clouds and generate reliable assimilation results and uncertainty estimates at both high spatial resolution and high temporal frequency

ACS Style

Bo Yang; Hongxing Liu; Emily L. Kang; Song Shu; Min Xu; Bin Wu; Richard A. Beck; Kenneth M. Hinkel; Bailang Yu. Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data. Remote Sensing of Environment 2020, 255, 112190 .

AMA Style

Bo Yang, Hongxing Liu, Emily L. Kang, Song Shu, Min Xu, Bin Wu, Richard A. Beck, Kenneth M. Hinkel, Bailang Yu. Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data. Remote Sensing of Environment. 2020; 255 ():112190.

Chicago/Turabian Style

Bo Yang; Hongxing Liu; Emily L. Kang; Song Shu; Min Xu; Bin Wu; Richard A. Beck; Kenneth M. Hinkel; Bailang Yu. 2020. "Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data." Remote Sensing of Environment 255, no. : 112190.

Journal article
Published: 29 August 2020 in Remote Sensing
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The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.71 and 0.83, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.

ACS Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing 2020, 12, 2805 .

AMA Style

Wei Chen, Yuyu Zhou, Qiusheng Wu, Gang Chen, Xin Huang, Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing. 2020; 12 (17):2805.

Chicago/Turabian Style

Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. 2020. "Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China." Remote Sensing 12, no. 17: 2805.

Journal article
Published: 17 August 2020 in IEEE Geoscience and Remote Sensing Letters
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Remotely sensed nighttime light (NL) data collected by the Suomi National Polar-orbiting Partnership Satellite equipped with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor have proven to be effective for evaluating fossil fuel combustion carbon emissions (CEs). However, few studies have analyzed the relationships between NL and CE originating from different sectors. The effects of impact factors on the NL-CE relationship have not been thoroughly examined and compared. Utilizing the corrected annual composite average of NPP-VIIRS data (NTL), this letter individually investigated the relationships between the NTL and CE from all types of fossil fuels total CE (TCE); CE from gasoline, diesel oil, natural gas, and cement urban carbon emission (UC); and CE from raw coal, cleaned coal, other washed coal, briquette, and coke industrial carbon emission (IC) in China at the provincial level. The impact factors governing the NTL-CE relationship were also examined. The results showed that total NLs (TNLs) may be a more effective means for estimating UC than other types of CE but may not be a good proxy for IC due to the mismatch between their amounts and brightness. The R² values from TNL and TCE analyses were higher than those of TNL and IC within the eastern, central, and western regions. Meanwhile, we found that NTL could more accurately evaluate CE in urban areas with a large population size and a relatively developed social economy. Although the urbanization rate was the most important factor in the assessment of CE from NTL, China's urbanization rate presented an inverted U-shaped impact on the NTL-CE relationship in the long run.

ACS Style

Kaifang Shi; Zuoqi Chen; Yuanzheng Cui; Jianping Wu; Bailang Yu. NPP-VIIRS Nighttime Light Data Have Different Correlated Relationships With Fossil Fuel Combustion Carbon Emissions From Different Sectors. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.

AMA Style

Kaifang Shi, Zuoqi Chen, Yuanzheng Cui, Jianping Wu, Bailang Yu. NPP-VIIRS Nighttime Light Data Have Different Correlated Relationships With Fossil Fuel Combustion Carbon Emissions From Different Sectors. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.

Chicago/Turabian Style

Kaifang Shi; Zuoqi Chen; Yuanzheng Cui; Jianping Wu; Bailang Yu. 2020. "NPP-VIIRS Nighttime Light Data Have Different Correlated Relationships With Fossil Fuel Combustion Carbon Emissions From Different Sectors." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Research article
Published: 07 August 2020 in GIScience & Remote Sensing
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Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly, an approach based on mobile LiDAR point clouds has been developed to map the three-dimensional visibility at the street level. The method consists of five steps: voxelization of point-cloud data, construction of lines-of-sight, construction of sectors of sight, construction of three-dimensional visible space, and calculation of volume index. The proposed approach is able to automatically measure the volume of visible space and openness at any viewpoint along a street. This approach has been applied to three study areas. The results indicated that the proposed approach enables accurate simulation of visible space as well as high-resolution (1 m × 1 m) mapping of the visible volume index. The proposed approach can make a contribution to the improvement of urban planning and design processes that aim at developing more sustainable built environments.

ACS Style

Yi Zhao; Bin Wu; Jianping Wu; Song Shu; Handong Liang; Min Liu; Vladimir Badenko; Alexander Fedotov; Shenjun Yao; Bailang Yu. Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds. GIScience & Remote Sensing 2020, 57, 797 -812.

AMA Style

Yi Zhao, Bin Wu, Jianping Wu, Song Shu, Handong Liang, Min Liu, Vladimir Badenko, Alexander Fedotov, Shenjun Yao, Bailang Yu. Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds. GIScience & Remote Sensing. 2020; 57 (6):797-812.

Chicago/Turabian Style

Yi Zhao; Bin Wu; Jianping Wu; Song Shu; Handong Liang; Min Liu; Vladimir Badenko; Alexander Fedotov; Shenjun Yao; Bailang Yu. 2020. "Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds." GIScience & Remote Sensing 57, no. 6: 797-812.

Journal article
Published: 30 July 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth’s surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

ACS Style

Song Shu; Hongxing Liu; Frederic Frappart; Emily Lei Kang; Bo Yang; Min Xu; Yan Huang; Bin Wu; Bailang Yu; Shujie Wang; Richard Beck; Kenneth Hinkel. Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 4733 -4748.

AMA Style

Song Shu, Hongxing Liu, Frederic Frappart, Emily Lei Kang, Bo Yang, Min Xu, Yan Huang, Bin Wu, Bailang Yu, Shujie Wang, Richard Beck, Kenneth Hinkel. Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (6):4733-4748.

Chicago/Turabian Style

Song Shu; Hongxing Liu; Frederic Frappart; Emily Lei Kang; Bo Yang; Min Xu; Yan Huang; Bin Wu; Bailang Yu; Shujie Wang; Richard Beck; Kenneth Hinkel. 2020. "Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm." IEEE Transactions on Geoscience and Remote Sensing 59, no. 6: 4733-4748.

Research article
Published: 16 June 2020 in International Journal of Geographical Information Science
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Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena.

ACS Style

Bin Wu; Bailang Yu; Song Shu; Qiusheng Wu; Yi Zhao; Jianping Wu. A spatiotemporal structural graph for characterizing land cover changes. International Journal of Geographical Information Science 2020, 35, 397 -425.

AMA Style

Bin Wu, Bailang Yu, Song Shu, Qiusheng Wu, Yi Zhao, Jianping Wu. A spatiotemporal structural graph for characterizing land cover changes. International Journal of Geographical Information Science. 2020; 35 (2):397-425.

Chicago/Turabian Style

Bin Wu; Bailang Yu; Song Shu; Qiusheng Wu; Yi Zhao; Jianping Wu. 2020. "A spatiotemporal structural graph for characterizing land cover changes." International Journal of Geographical Information Science 35, no. 2: 397-425.

Journal article
Published: 03 April 2020 in Science of The Total Environment
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With more record-breaking skyscrapers built in big cities around the world, horizontal urban sprawl no longer dominates the research of urbanization rather than the vertical growth of cities. In such a context, the urban heat island problem cannot be understood by solely studying the impact of the horizontal urban expansion because the 3D structure of the urban landscape could severely alter the natural heat flux transport over the land surface and thus lead to bigger heat island problems. In addition to our current knowledge of impact of 2D landscape changes on urban thermal dynamics, it is crucial to understand the effects of 3D landscape pattern on the thermal environment, in order to maintain a sustainable and eco-friendly urban development. This study investigated the 2D/3D landscape pattern metrics and their association with the land surface temperature (LST) changes in a case study area of Shanghai City using the extreme gradient boosting (XGBoost) regression model and Sharpley Additive exPlanations (SHAP) interpretation method based on datasets of land cover and digital surface model (DSM). Major findings include, 1) 3D landscape pattern metrics could better describe the undulation and heterogeneity of urban surface and were essential when explaining the variation of LST compared with conventional 2D landscape pattern metrics, 2) Low-rise and high-rise buildings tend to alleviate LST while buildings with medium height heating the surroundings; 3) the cooling effect of vegetation was significantly strong; 4) different urban functional types impact the surface temperature in the way determined by their 3D urban landscape pattern. These findings may help urban planners and landscape designers achieve the goal of minimizing urban heat island using computer models of 3D urban structure.

ACS Style

Siyi Yu; Zuoqi Chen; Bailang Yu; Lei Wang; Bin Wu; Jianping Wu; Feng Zhao. Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China. Science of The Total Environment 2020, 725, 138229 .

AMA Style

Siyi Yu, Zuoqi Chen, Bailang Yu, Lei Wang, Bin Wu, Jianping Wu, Feng Zhao. Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China. Science of The Total Environment. 2020; 725 ():138229.

Chicago/Turabian Style

Siyi Yu; Zuoqi Chen; Bailang Yu; Lei Wang; Bin Wu; Jianping Wu; Feng Zhao. 2020. "Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China." Science of The Total Environment 725, no. : 138229.

Journal article
Published: 25 February 2020 in Journal of Environmental Management
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Effectively evaluating the effects of urban forms on CO2 emissions has become a hot topic in socioeconomic sustainable development; however, few studies have been able to explore the urban form-CO2 emission relationships from a multi-perspective view. Here, we attempted to analyze the relationships between urban forms and CO2 emissions in 264 Chinese cities, with explicit consideration of the government policies, urban area size, population size, and economic structure. First, urban forms were calculated using the urban land derived from multiple-source remote sensing data. Second, we collected and processed CO2 emissions and three control variables. Finally, a correlation analysis was implemented to explore whether and to what extent the spatial patterns of urban forms were associated with CO2 emissions. The results show that urban form irregularity had a more significant impact on CO2 emissions in low-carbon pilot cities than in non-pilot cities. The impact of the complexity of urban forms on CO2 emissions was relatively significant in the small- and large-sized cities than in the medium-sized cities. Moreover, urban form complexity had a significant correlation with CO2 emissions in all of the cities, the level of which basically increased with the population size. This study provides scientific bases for use in policy-making to prepare effective policies for developing a low-carbon economy with consideration of the associations between urban forms and CO2 emissions in different scenarios.

ACS Style

Kaifang Shi; Tao Xu; Yuanqing Li; Zuoqi Chen; Wenkang Gong; Jianping Wu; Bailang Yu. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis. Journal of Environmental Management 2020, 262, 110300 .

AMA Style

Kaifang Shi, Tao Xu, Yuanqing Li, Zuoqi Chen, Wenkang Gong, Jianping Wu, Bailang Yu. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis. Journal of Environmental Management. 2020; 262 ():110300.

Chicago/Turabian Style

Kaifang Shi; Tao Xu; Yuanqing Li; Zuoqi Chen; Wenkang Gong; Jianping Wu; Bailang Yu. 2020. "Effects of urban forms on CO2 emissions in China from a multi-perspective analysis." Journal of Environmental Management 262, no. : 110300.

Review articles
Published: 05 February 2020 in Journal of Spatial Science
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Automatic building rooftop extraction is of great importance to many applications including building reconstruction, solar energy supply, and disaster management. This study proposes a building rooftop extraction method using DSM data generated from aerial stereo images and vegetation cover vector data. The method consists of five steps: noise filtering, dilation reconstruction, vegetation and terrain region removal, region growing and merging, and post-processing. We applied the proposed method to the centre of Shanghai, China, a typical urban area. Experimental results show that the proposed method can successfully extract building rooftops, with an approximately 82.6% quality percentage and 96.2% matched overlay.

ACS Style

Bin Wu; Siyuan Wu; Yong Li; Jianping Wu; Yan Huang; Zuoqi Chen; Bailang Yu. Automatic building rooftop extraction using a digital surface model derived from aerial stereo images. Journal of Spatial Science 2020, 1 -20.

AMA Style

Bin Wu, Siyuan Wu, Yong Li, Jianping Wu, Yan Huang, Zuoqi Chen, Bailang Yu. Automatic building rooftop extraction using a digital surface model derived from aerial stereo images. Journal of Spatial Science. 2020; ():1-20.

Chicago/Turabian Style

Bin Wu; Siyuan Wu; Yong Li; Jianping Wu; Yan Huang; Zuoqi Chen; Bailang Yu. 2020. "Automatic building rooftop extraction using a digital surface model derived from aerial stereo images." Journal of Spatial Science , no. : 1-20.

Journal article
Published: 04 February 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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To realize energy conservation and environmental protection, solar street lights have been widely used in urban areas in China. To reasonably and effectively utilize solar street lights, the original street lights must be located, and the solar street light potential must be assessed. The Jilin1-03B (JL1-3B) satellite provides next generation nighttime light data with a high spatial resolution and in three spectral bands. Consequently, the street lights can be extracted from the nighttime light data. We used the road network dataset from the open street map (OSM) with a specific buffer to extract the road area as a constraint region. Next, the grayscale brightness of JL1-3B images was obtained by integrating all the three bands to locate the street light by using a local maximum algorithm. Then, the values of the original three bands were utilized to classify the types of street lights as high-pressure sodium (HPS) lamps or light-emitting diode (LED) lamps. Finally, we simulated the replacement of all the HPS lamps with solar street lights and assessed the corresponding solar energy potential by using the digital surface model data and hourly cloud cover data through the SHORTWAVE-C model. The accuracy of location of the street lights was approximately 90%. Replacing an HPS lamp by one solar street light for 20 y can save $1.85 \times 10^4 \text{kWh}$ of electrical energy, 7.41 t of standard coal, 5.03 t of C emissions, 18.47 t of CO2 emissions, 0.55 t of SO2 emissions, and 0.28 t of NOX emissions.

ACS Style

Bin Cheng; Zuoqi Chen; Bailang Yu; Qiaoxuan Li; Congxiao Wang; Beibei Li; Bin Wu; Yong Li; Jianping Wu. Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 675 -684.

AMA Style

Bin Cheng, Zuoqi Chen, Bailang Yu, Qiaoxuan Li, Congxiao Wang, Beibei Li, Bin Wu, Yong Li, Jianping Wu. Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):675-684.

Chicago/Turabian Style

Bin Cheng; Zuoqi Chen; Bailang Yu; Qiaoxuan Li; Congxiao Wang; Beibei Li; Bin Wu; Yong Li; Jianping Wu. 2020. "Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 675-684.

Journal article
Published: 24 January 2020 in Journal of Cleaner Production
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Poverty is a chronic worldwide dilemma that can seriously hamper human sustainable development, which is closely related to economic growth, environmental protection, ecological restoration, and sustainable utilization of resources. Accurately and effectively identifying and evaluating poverty has become an important prerequisite for allowing Chinese governments to make reasonable poverty reduction and alleviation policies. Thus, using Chongqing as a study area, the purpose of this study was to analyze poverty from multiple viewpoints based on multiple data sources. First, a comprehensive poverty index (CPI) was developed by combining nighttime light data, the digital elevation model (DEM), the normalized differential vegetation index (NDVI), and point of interest (POI) data to map poverty at a 500-m spatial resolution. Then, the performance of the CPI was validated with poverty-stricken villages, Google Earth images, and the multidimensional poverty index (MPI). Finally, spatial autocorrelation analysis was used to explore the spatial distribution of poverty across county and town levels. The results revealed that the CPI could provide an effective way of identifying the spatial distribution of poverty when compared with three validated indexes. Most of the rich counties were in the center of Chongqing, whereas the poor counties were located in the northeast and southeast of Chongqing. The Global Moran’s I index showed that there were significantly positive spatial autocorrelations of poverty, and that the spatial autocorrelation of poverty was more significant at the town level compared to the county level. Among the selected factors, the POI cost distance was the most import factor for assessing poverty. Our study will be valuable for providing scientific references for the government to implement precise poverty alleviation methods with differentiated policies in China.

ACS Style

Kaifang Shi; Zhijian Chang; Zuoqi Chen; Jianping Wu; Bailang Yu. Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China. Journal of Cleaner Production 2020, 255, 120245 .

AMA Style

Kaifang Shi, Zhijian Chang, Zuoqi Chen, Jianping Wu, Bailang Yu. Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China. Journal of Cleaner Production. 2020; 255 ():120245.

Chicago/Turabian Style

Kaifang Shi; Zhijian Chang; Zuoqi Chen; Jianping Wu; Bailang Yu. 2020. "Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China." Journal of Cleaner Production 255, no. : 120245.

Journal article
Published: 12 November 2019 in International Journal of Applied Earth Observation and Geoinformation
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Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indices at national and regional scales. However, few studies analyzed the factors that may explain NTL variations at a fine scale due to the limited resolution of existing NTL data. As a new generation NTL satellite, Luojia 1-01 provides NTL data with a finer spatial resolution of ∼130 m and can be used to assess the relationship between NTL intensity and artificial surface features on an unprecedented scale. This study represents the first efforts to assess the relationship between Luojia 1-01 NTL intensity and artificial surface features at the parcel level in comparison to the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. Points-of-interest (POIs) and land-use/land-cover (LULC) data were used in random forest (RF) regression models for both Luojia 1-01 and NPP-VIIRS to analyze the feature contribution of artificial surface features to NTL intensity. The results show that luminosity variations in Luojia 1-01 data for different land-use types were more significant than those in NPP-VIIRS data because of the finer spatial resolution and wider measurement range. Seventeen variables extracted from POI and LULC data explained the Luojia 1-01 and NPP-VIIRS NTL intensity, with a good out-of-bag score of 0.62 and 0.76, respectively. Moreover, Luojia 1-01 data had fewer “blooming” phenomena than NPP-VIIRS data, especially for cropland, water body, and rural area. Luojia 1-01 is more suitable for estimating socioeconomic activities and can attain more comprehensive information on human activities, since the feature contribution of POI variables is more sensitive to NTL intensity in the Luojia 1-01 RF regression model than that in the NPP-VIIRS RF regression model.

ACS Style

Congxiao Wang; Zuoqi Chen; Chengshu Yang; Qiaoxuan Li; Qiusheng Wu; Jianping Wu; Guo Zhang; Bailang Yu. Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data. International Journal of Applied Earth Observation and Geoinformation 2019, 85, 101989 .

AMA Style

Congxiao Wang, Zuoqi Chen, Chengshu Yang, Qiaoxuan Li, Qiusheng Wu, Jianping Wu, Guo Zhang, Bailang Yu. Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data. International Journal of Applied Earth Observation and Geoinformation. 2019; 85 ():101989.

Chicago/Turabian Style

Congxiao Wang; Zuoqi Chen; Chengshu Yang; Qiaoxuan Li; Qiusheng Wu; Jianping Wu; Guo Zhang; Bailang Yu. 2019. "Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data." International Journal of Applied Earth Observation and Geoinformation 85, no. : 101989.

Journal article
Published: 16 October 2019 in Remote Sensing
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Urban development status is closely related to the urban economy, environment, ecology, and health. Spatial and socioeconomic processes are the two key aspects of urban development, so the absence of any of them will affect the assessment of urban development status. In this study, using both spatial and socioeconomic information from land cover data and nighttime light data, respectively, we proposed an exponential model, Spatial–Socioeconomic Urban Development Curve (SSUDC), to provide a quantitative expression of the relationship between the two key processes of urban development and analyze urban development status. The SSUDC was calculated from the artificial surface ratio at 1% intervals obtained from Globeland30 land cover data and the corresponding average NPP-VIIRS nighttime light radiance data, using a nonlinear least-squares method. We generated SSUDCs for 330 prefecture-level cities in Mainland China, 208 of which had coefficients of determination (R2) greater than 0.6. Taking Ordos and Guiyang as two typical examples, we analyzed the importance and advantages of SSUDC. The coefficients α and β of the exponential SSUDC were shown to indicate the base intensity socioeconomic activity and the concentration of socioeconomic activities, respectively, and can be used to reveal the urban socioeconomic development status and functional type of cities. At the internal urban level, the residuals of SSUDC can imply the demand for urban physical or economic construction in different areas of the city, and even the urban growth type, together with the distribution of the artificial surface ratio. In summary, the proposed SSUDC provides a simple way to combine the spatial and socioeconomic processes of urban development, which is beneficial to the analysis of urban development at different scales and a rewarding tool for urban planning.

ACS Style

Chengshu Yang; Bailang Yu; Zuoqi Chen; Wei Song; Yuyu Zhou; Xia Li; Jianping Wu. A Spatial-Socioeconomic Urban Development Status Curve from NPP-VIIRS Nighttime Light Data. Remote Sensing 2019, 11, 2398 .

AMA Style

Chengshu Yang, Bailang Yu, Zuoqi Chen, Wei Song, Yuyu Zhou, Xia Li, Jianping Wu. A Spatial-Socioeconomic Urban Development Status Curve from NPP-VIIRS Nighttime Light Data. Remote Sensing. 2019; 11 (20):2398.

Chicago/Turabian Style

Chengshu Yang; Bailang Yu; Zuoqi Chen; Wei Song; Yuyu Zhou; Xia Li; Jianping Wu. 2019. "A Spatial-Socioeconomic Urban Development Status Curve from NPP-VIIRS Nighttime Light Data." Remote Sensing 11, no. 20: 2398.

Review
Published: 21 August 2019 in Remote Sensing
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Nighttime light observations from remote sensing provide us with a timely and spatially explicit measure of human activities, and therefore enable a host of applications such as tracking urbanization and socioeconomic dynamics, evaluating armed conflicts and disasters, investigating fisheries, assessing greenhouse gas emissions and energy use, and analyzing light pollution and health effects. The new and improved sensors, algorithms, and products for nighttime lights, in association with other Earth observations and ancillary data (e.g., geo-located big data), together offer great potential for a deep understanding of human activities and related environmental consequences in a changing world. This paper reviews the advances of nighttime light sensors and products and examines the contributions of nighttime light remote sensing to perceiving the changing world from two aspects (i.e., human activities and environmental changes). Based on the historical review of the advances in nighttime light remote sensing, we summarize the challenges in current nighttime light remote sensing research and propose four strategic directions, including: Improving nighttime light data; developing a long time series of consistent nighttime light data; integrating nighttime light observations with other data and knowledge; and promoting multidisciplinary and interdisciplinary analyses of nighttime light observations.

ACS Style

Min Zhao; Yuyu Zhou; Xuecao Li; Wenting Cao; Chunyang He; Bailang Yu; Christopher D. Elvidge; Weiming Cheng. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives. Remote Sensing 2019, 11, 1971 .

AMA Style

Min Zhao, Yuyu Zhou, Xuecao Li, Wenting Cao, Chunyang He, Bailang Yu, Christopher D. Elvidge, Weiming Cheng. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives. Remote Sensing. 2019; 11 (17):1971.

Chicago/Turabian Style

Min Zhao; Yuyu Zhou; Xuecao Li; Wenting Cao; Chunyang He; Bailang Yu; Christopher D. Elvidge; Weiming Cheng. 2019. "Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives." Remote Sensing 11, no. 17: 1971.

Journal article
Published: 29 May 2019 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The nighttime light (NTL) remote-sensing data have been widely applied in several applications for analyzing the urbanization process. The relationship between NTL intensity and human activity becomes a solid foundation for the applications using NTL data. However, there is no research, so far, revealing how the human activity seasonality could impact the seasonal change of NTL intensity. In this paper, a comparative analysis, box plot, and random forest algorithm were applied to NTL remote-sensing data and points of interest (POIs) data within Shanghai, China. The results show that in spring and autumn, the NTL is much brighter than that in summer and winter, especially within high human activity density area. The NTL intensity can be partly (approximately 40%) explained as the joint effects of the five POI categories. By analyzing the contributions of each POI category to NTL intensity, we found that the National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) could be used to dig more information about gross domestic product (GDP) and traffic-based applications with consideration of NTL seasonality.

ACS Style

Zuoqi Chen; Bailang Yu; Na Ta; Kaifang Shi; Chengshu Yang; Congxiao Wang; Xizhi Zhao; Shunqiang Deng; Jianping Wu. Delineating Seasonal Relationships Between Suomi NPP-VIIRS Nighttime Light and Human Activity Across Shanghai, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 4275 -4283.

AMA Style

Zuoqi Chen, Bailang Yu, Na Ta, Kaifang Shi, Chengshu Yang, Congxiao Wang, Xizhi Zhao, Shunqiang Deng, Jianping Wu. Delineating Seasonal Relationships Between Suomi NPP-VIIRS Nighttime Light and Human Activity Across Shanghai, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (11):4275-4283.

Chicago/Turabian Style

Zuoqi Chen; Bailang Yu; Na Ta; Kaifang Shi; Chengshu Yang; Congxiao Wang; Xizhi Zhao; Shunqiang Deng; Jianping Wu. 2019. "Delineating Seasonal Relationships Between Suomi NPP-VIIRS Nighttime Light and Human Activity Across Shanghai, China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 11: 4275-4283.

Research articles
Published: 01 April 2019 in International Journal of Geographical Information Science
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Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.

ACS Style

Bin Wu; Bailang Yu; Shenjun Yao; Qiusheng Wu; Zuoqi Chen; Jianping Wu. A surface network based method for studying urban hierarchies by night time light remote sensing data. International Journal of Geographical Information Science 2019, 33, 1377 -1398.

AMA Style

Bin Wu, Bailang Yu, Shenjun Yao, Qiusheng Wu, Zuoqi Chen, Jianping Wu. A surface network based method for studying urban hierarchies by night time light remote sensing data. International Journal of Geographical Information Science. 2019; 33 (7):1377-1398.

Chicago/Turabian Style

Bin Wu; Bailang Yu; Shenjun Yao; Qiusheng Wu; Zuoqi Chen; Jianping Wu. 2019. "A surface network based method for studying urban hierarchies by night time light remote sensing data." International Journal of Geographical Information Science 33, no. 7: 1377-1398.

Journal article
Published: 13 March 2019 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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ACS Style

Zuoqi Chen; Bailang Yu; Yuyu Zhou; Hongxing Liu; Chengshu Yang; Kaifang Shi; Jianping Wu. Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 1143 -1153.

AMA Style

Zuoqi Chen, Bailang Yu, Yuyu Zhou, Hongxing Liu, Chengshu Yang, Kaifang Shi, Jianping Wu. Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (4):1143-1153.

Chicago/Turabian Style

Zuoqi Chen; Bailang Yu; Yuyu Zhou; Hongxing Liu; Chengshu Yang; Kaifang Shi; Jianping Wu. 2019. "Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 4: 1143-1153.