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.

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 A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China

2019 ◽  
Vol 11 (9) ◽  
pp. 1126 ◽  
Author(s):  
Xiaojiang Liu ◽  
Xiaogang Ning ◽  
Hao Wang ◽  
Chenggang Wang ◽  
Hanchao Zhang ◽  
...  
Keyword(s):  
High Precision ◽  
Mutation Detection ◽  
Detection Method ◽  
Rapid Expansion ◽  
Optimal Threshold ◽  
Threshold Function ◽  
Boundary Extraction ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Relative Errors

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method.

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.

Study on Extraction Methods of Saline-Alkali Land Based on Remote Sensing Image and Influencing Factors

2015 ◽  
Vol 1092-1093 ◽  
pp. 1307-1312
Author(s):  
Jing Chen ◽  
Wei Quan Zhao ◽  
Li Hua Zhao ◽  
Lei Huo
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Unused Land ◽  
The Relationship

Remote sensing image interpretation is one of the commonly methods to extract saline-alkali land. But it can only extract saline-alkali land of unused land, can not divide the types of slightly saline-alkali land, moderately saline-alkali land and severely saline-alkali land. On the basis of remote sensing image interpretation of 2010, the paper calculated the relationship between saline-alkali land degrees and the normalized difference vegetation index (NDVI), elevation, soil type, groundwater, river, urban expansion. Use AHP and Delphi to assign weights, calculate values, and overlay the layers, then concluded the degrees of saline-alkali land.

scholarly journals An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration

2021 ◽  
Vol 13 (3) ◽  
pp. 889-906
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Chengshu Yang ◽  
Yuyu Zhou ◽  
Shenjun Yao ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Infrared Imaging ◽  
Great Accuracy ◽  
Sensor Calibration ◽  
Temporal Consistency ◽  
Extended Time ◽  
Urbanization Process ◽  
Nighttime Light Data ◽  
Nighttime Light

Abstract. 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).

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