scholarly journals Evaluating Urbanization and Spatial-Temporal Pattern Using the DMSP/OLS Nighttime Light Data: A Case Study in Zhejiang Province

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Pengfei Xu ◽  
Pingbin Jin ◽  
Yangfan Yang ◽  
Quan Wang
Keyword(s):  
Rapid Development ◽  
Urban Expansion ◽  
Zhejiang Province ◽  
Effective Measure ◽  
Nighttime Light Data ◽  
Economic Statistics ◽  
Urban Planning And Development ◽  
Nighttime Light ◽  
High Level ◽  
Nighttime Light Imagery

The application of DMSP/OLS nighttime light data provides an effective measure for characterizing urbanization and its spatial-temporal changes. Combined with the social economic statistics and calibrated nighttime data, the nighttime light imagery of Zhejiang province was fully intercalibrated during the period 1992–2013. The backgrounds were explained and the model of region light index (RLI) was built to make further research. The methods of mutation detection, regression analysis, and spatial analysis were adopted in this study. The results show that the urbanization progress of Zhejiang experienced a transformation from rapid development to steady improvement and was accompanied by a changing direction of urban expansion from coastal to inland areas from 2000. Further research indicated that Zhejiang province possessed a relative high level of urbanization, where a spatial pattern of urbanization with one center and four axes was initially formed. It is a novel attempt to investigate the urbanization of Zhejiang province on the basis of the DMSP/OLS night-lighting data, which may provide a significant guideline for the urban planning and development.

Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008

2012 ◽  
Vol 106 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Zhifeng Liu ◽  
Chunyang He ◽  
Qiaofeng Zhang ◽  
Qingxu Huang ◽  
Yang Yang
Keyword(s):  
Urban Expansion ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Measurement of Urban Expansion and Spatial Correlation of Central Yunnan Urban Agglomeration Using Nighttime Light Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Enwei Zhang ◽  
Huamei Feng ◽  
Shuangyun Peng
Keyword(s):  
Urban Expansion ◽  
Urban Agglomeration ◽  
Coordinated Development ◽  
Gravity Center ◽  
Changing Trends ◽  
Stellar Radiation ◽  
Nighttime Light Data ◽  
Western Development ◽  
Nighttime Light ◽  
Statistical Yearbook

The Central Yunnan Urban Agglomeration (CYUA) is an important zone of western development in China. The clarification of the spatial structure and changing trends in CYUA could help promote the coordinated development of the CYUA and enhance the overall competitiveness of the region. Based on data from the Yunnan Statistical Yearbook and the nighttime light data, this paper extracts the urban built-up area of the CYUA and analyzes the urban expansion and urban spatial connection intensity of the CYUA from 2000 to 2018 by using the urban gravity center model and the gravity model. The results show the following: (1) From 2000 to 2018, the urban built-up area of the CYUA expanded rapidly, and the urban built-up area increased by 369.35%, with Kunming accounting for 45.41% of the increased area. Kunming was the main contributor to the increase in the urban built-up area in the CYUA. From 2000 to 2018, the urban built-up areas of the CYUA were scattered in various mountain basins. (2) Overall, the urban gravity center of the CYUA has moved to Kunming, and the distance of the urban gravity center has increased since 2005, indicating that urban expansion has accelerated since 2005. (3) The development of the CYUA is extremely unbalanced. The urban spatial connection intensity between Kunming city, Yuxi city, and Qujing city, and Yi Autonomous Prefecture of Chuxiong is relatively strong, while the urban spatial connection intensity among cities other than Kunming is weak. Overall, the CYUA is characterized by stellar radiation with Kunming city as the core and Yuxi city as the secondary core.

scholarly journals An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3810
Author(s):  
Xiuxiu Chen ◽  
Feng Zhang ◽  
Zhenhong Du ◽  
Renyi Liu
Keyword(s):  
Clustering Algorithm ◽  
Urban Expansion ◽  
Spatial Context ◽  
Support Vector ◽  
Free Access ◽  
Urban Edge ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Mapping ◽  
Urban Extent

An accelerating trend of global urbanization accompanying various environmental and urban issues makes frequently urban mapping. Nighttime light data (NTL) has shown great advantages in urban mapping at regional and global scales over long time series because of its appropriate spatial and temporal resolution, free access, and global coverage. However, the existing urban extent extraction methods based on nighttime light data rely on auxiliary data and training samples, which require labor and time for data preparation, leading to the difficulty to extract urban extent at a large scale. This study seeks to develop an unsupervised method to extract urban extent from nighttime light data rapidly and accurately without ancillary data. The clustering algorithm is applied to segment urban areas from the background and multi-scale spatial context constraints are utilized to reduce errors arising from the low brightness areas and increase detail information in urban edge district. Firstly, the urban edge district is detected using spatial context constrained clustering, and the NTL image is divided into urban interior district, urban edge district and non-urban interior district. Secondly, the urban edge pixels are classified by an adaptive direction filtering clustering. Finally, the full urban extent is obtained by merging the urban inner pixels and the urban pixels in urban edge district. The proposed method was validated using the urban extents of 25 Chinese cities, obtained by Landsat8 images and compared with two common methods, the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM). The Kappa coefficient ranged from 0.687 to 0.829 with an average of 0.7686 (1.80% higher than LOT and 4.88% higher than INNL-SVM). The results in this study show that the proposed method is a reliable and efficient method for extracting urban extent with high accuracy and simple operation. These imply the significant potential for urban mapping and urban expansion research at regional and global scales automatically and accurately.

scholarly journals Planar Block Adjustment for China’s Land Regions with LuoJia1-01 Nighttime Light Imagery

2019 ◽  
Vol 11 (18) ◽  
pp. 2097 ◽  
Author(s):  
Xin Li ◽  
Taoyang Wang ◽  
Guo Zhang ◽  
Boyang Jiang ◽  
Peng Jia ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
High Sensitivity ◽  
Experimental Results ◽  
Mean Square ◽  
Ground Control Points ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Nighttime Light Imagery

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method.

scholarly journals A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate

2019 ◽  
Vol 8 (9) ◽  
pp. 419 ◽  
Author(s):  
Xiaole Ji ◽  
Xinze Li ◽  
Yaqian He ◽  
Xiaolong Liu
Keyword(s):  
Growth Rate ◽  
Remotely Sensed ◽  
Gdp Growth ◽  
County Level ◽  
Remotely Sensed Data ◽  
Simple Method ◽  
Gdp Growth Rate ◽  
Nighttime Light Data ◽  
Economic Statistics ◽  
Nighttime Light

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

scholarly journals Coupling Coordination Relationship between Urban Sprawl and Urbanization Quality in the West Taiwan Strait Urban Agglomeration, China: Observation and Analysis from DMSP/OLS Nighttime Light Imagery and Panel Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3217
Author(s):  
Chunyan Lu ◽  
Lin Li ◽  
Yifan Lei ◽  
Chunying Ren ◽  
Ying Su ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Driving Forces ◽  
Urban Expansion ◽  
Taiwan Strait ◽  
Urban Agglomeration ◽  
The West ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Urban sprawl is the most prominent characteristic of urbanization, and increasingly affects local and regional sustainable development. The observation and analysis of urban sprawl dynamics and their relationship with urbanization quality are essential for framing integrative urban planning. In this study, the urban areas of the West Taiwan Strait Urban Agglomeration (WTSUA) were extracted using nighttime light imagery from 1992 to 2013. The spatio-temporal characteristics and pattern of urban sprawl were quantitatively analyzed by combining an urban expansion rate index and a standard deviation ellipse model. The urbanization quality was assessed using an entropy weight model, and its relationship with urban sprawl was calculated by a coupling coordination degree model. The results showed that the urban area in the WTSUA experienced a significant increase, i.e., 18,806.73 km2, during the period 1992–2013. The central cities grew by 11.08% and noncentral cities by 27.43%, with a general uneven city rank-size distribution. The urban sprawl showed a circular expansion pattern, accompanied by a gradual centroid migration of urban areas from the southeast coast to the central-western regions. The coupling coordination level between urban expansion and urbanization quality increased from serious incoordination in 1992 to basic coordination in 2013. Dual driving forces involving state-led policies and market-oriented land reform had a positive influence on the harmonious development of urban sprawl and urbanization quality of the WTSUA. This research offers an effective approach to monitor changes in urban sprawl and explore the coupling coordination relationship between urban sprawl and urbanization quality. The study provides important scientific references for the formulation of future policies and planning for sustainable development in urban agglomerations.

scholarly journals An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data

2021 ◽  
Vol 13 (9) ◽  
pp. 5042
Author(s):  
Chengming Li ◽  
Xiaoyan Wang ◽  
Zheng Wu ◽  
Zhaoxin Dai ◽  
Jie Yin ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Urban Expansion ◽  
Social Factor ◽  
Road Networks ◽  
Extraction Methods ◽  
Boundary Extraction ◽  
Points Of Interest ◽  
Nighttime Light Data ◽  
The Social ◽  
Nighttime Light

Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.

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