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.

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 A time series of urban extent in China using DSMP/OLS nighttime light data

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0198189 ◽  
Author(s):  
Yao Yao ◽  
Dongsheng Chen ◽  
Le Chen ◽  
Huan Wang ◽  
Qingfeng Guan
Keyword(s):  
Time Series ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Extent

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.

Detecting China’s Urban Expansion Over the Past Three Decades Using Nighttime Light Data

2014 ◽  
Vol 7 (10) ◽  
pp. 4095-4106 ◽  
Author(s):  
Pengfeng Xiao ◽  
Xiaohui Wang ◽  
Xuezhi Feng ◽  
Xueliang Zhang ◽  
Yongke Yang
Keyword(s):  
Urban Expansion ◽  
The Past ◽  
Nighttime Light Data ◽  
Nighttime Light

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.

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