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Journal of Geographical Sciences >

2016 , Vol. 26 >Issue 5: 531 - 548

DOI: https://doi.org/10.1007/s11442-016-1284-1

Orginal Article

Spatiotemporal measurement of urbanization levels based on multiscale units: A case study of the Bohai Rim Region in China

  • ZHAO Min , 1, 2 ,
  • *CHENG Weiming , 1, 3 ,
  • LIU Qiangyi 1, 2 ,
  • WANG Nan 1, 2
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  • 1. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China

Author: Zhao Min (1991-), Graduate Student. E-mail:

*Corresponding author: Cheng Weiming (1973-), Professor, specialized in digital geomorphology and GIS. E-mail:

Received date: 2015-08-06

  Accepted date: 2015-12-11

  Online published: 2016-05-25

Supported by

Surveying and Mapping Geoinformation Nonprofit Specific Project, No.201512033

National Natural Science Foundation of China, No.41171332

Major State Basic Research Development Program of China, No.2015CB954101

National Science Technology Basic Special Project, No.2011FY110400-2

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Urbanization is a complex process reflecting the growth, formation and development of cities and their systems. Measuring regional urbanization levels within a long time series may ensure healthy and harmonious urban development. Based on DMSP/OLS nighttime light data, a human-computer interactive boundary correction method was used to obtain information about built-up urban areas in the Bohai Rim region from 1992 to 2012. Consequently, a method was proposed and applied to measure urbanization levels using four measurement scale units: administrative division, land-sea location, terrain feature, and geomorphological types. Our conclusions are: 1) The extraction results based on DMSP/OLS nighttime light data showed substantial agreement with those obtained using Landsat TM/ETM+ data on spatial patterns. The overall accuracy was 97.70% on average, with an average Kappa of 0.79, indicating that the results extracted from DMSP/OLS nighttime light data were reliable and could well reflect the actual status of built-up urban areas. 2) Bohai Rim’s urbanization level has increased significantly, demonstrating a high annual growth rate from 1998 to 2006. Areas with high urbanization levels have relocated evidently from capital to coastal cities. 3) The distribution of built-up urban areas showed a certain degree of zonal variation. The urbanization level was negatively correlated with relief amplitude and altitude. A high level of urbanization was found in low altitude platforms and low altitude plains, with a gradual narrowing of the gap between these two geomorphological types. 4) The measurement method presented in this study is fast, convenient, and incorporates multiple perspectives. It would offer various directions for urban construction and provide reference values for measuring national-level urbanization.

Key words： nighttime light data; urbanization level; multiscale units; Bohai Rim

Cite this article

ZHAO Min , *CHENG Weiming , LIU Qiangyi , WANG Nan . Spatiotemporal measurement of urbanization levels based on multiscale units: A case study of the Bohai Rim Region in China[J]. Journal of Geographical Sciences, 2016 , 26(5) : 531 -548 . DOI: 10.1007/s11442-016-1284-1

1 Introduction

Urbanization is the most intuitive reflection of the urban development process (Lv et al., 2008). Accelerated urbanization in recent years has resulted in land expansion outpacing population growth (Yao et al., 2012), leading to serious problems such as overly rapid and uncontrolled urban spatial development (Yao et al., 2011; Lu et al., 2007). Consequently, obtaining information on urbanization and measuring regional urbanization levels in a quick and efficient way will ensure the adjustment and optimization of regional urbanization patterns, and have both practical value and research significance.
Currently, data sources of China’s urbanization used in scientific research are primarily statistical yearbooks (Guo et al., 2014; Fan et al., 2014; Chen et al., 2010; Ou et al., 2008; Ou et al., 2012; Zhu et al., 2014), and urban land use information extracted from remote sensing images (Li et al., 2007; Chen et al., 2015). Study areas include individual cities (Chen et al., 2015), urban agglomerations (Guo et al., 2014; Li et al., 2007; He et al., 2015), provinces (autonomous regions) (Fan et al., 2014; Ou et al., 2008; Ou et al., 2012), and even the nation as a whole (Chen et al., 2010; Zhu et al., 2014; Chen et al., 2003). Because of limited accessibility of large-scale remote sensing images and the high cost of extracting urban land use information, studies entailing the measurement of long-term regional urbanization are mainly conducted based on statistical data, resulting in a lack of spatial information. Besides, the measurement units are dominated by administrative boundaries, and are lack of natural, cultural and other types of factors, making it difficult to fully capture spatiotemporal variations at long-term regional urbanization levels.
The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/ OLS) can detect city lights and other low intensity signals emanating from small-scale residential areas, traffic, and fishing vessels at night, thereby enabling urban areas to be distinguished from rural areas (Elvidge et al., 2007). This type of sensor is suitable for conducting dynamic surveys of the urbanization process, because its time and spatial resolution are similar to those of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) (Chen et al., 2003). Easy accessibility and timeliness have resulted in the wide-scale use of nighttime light data for extracting urban land use information (He et al., 2006; Cao et al., 2009; Shi et al., 2014; Su et al., 2015), population simulations (Lo, 2001, 2002; Zhuo et al., 2005; Doll and Pachauri, 2006), economic estimations (Doll et al., 2006; Ghosh et al., 2010; Henderson et al., 2012; Li et al., 2013; Elvidge et al., 2009), hazard evaluations (Cahoon et al., 1992; Tuttle, 2007), energy consumption assessments (Amaral et al., 2005; Chand et al., 2009; Kiran et al., 2009; He et al., 2012), and identification of environmental problems (Imhoff et al., 2000, 2004). However, measurement of urbanization levels using nighttime light data is relatively rare in current studies. This has caused a lack of strong data support for making decisions and recommendations on regional urban development.
The Bohai Rim region is an important area of economic growth in northern China, and is also an optimal development zone at national level in relation to the main function zoning. In recent years, rapid urbanization of the Bohai Rim has brought in its wake a series of ecological and environmental problems. Developing a comprehensive understanding of spatial patterns of the Bohai Rim’s urbanization will enable strategic decisions to be made regarding its urban development. This requires measurement and corresponding analysis of the Bohai Rim’s urbanization level based on a scientific and effective measurement method.
Considering 44 cities in the Bohai Rim as objects of our study, we acquired possible threshold intervals of different cities for the year 1992, 1998, 2006, and 2012 using the statistical comparison method. We then applied the best threshold value using the human-computer interactive boundary correction method. We subsequently obtained information regarding built-up urban areas in the study years. Next, we used high spatial resolution Landsat EM/ETM+ data to evaluate the reliability of the extraction results based on DMSP/OLS nighttime light data. This evaluation confirmed that the extracted urban land well reflected the actual status of urban development in the corresponding periods. Accordingly, we developed a method for measuring the urbanization level of the Bohai Rim from 1992 to 2012 using DMSP/OLS nighttime light data and four different measurement scale units including administrative division, land-sea location, terrain feature, and geomorphological types. Unlike previous studies that used measurement units based solely on administrative divisions, we also considered other factors such as land-sea locations, terrain features, and geomorphological types to develop a comprehensive understanding of the urbanization status in the Bohai Rim region. This can facilitate the formulation of policies to promote a sustainable urbanization process. This study filled a research gap on spatiotemporal measurements of regional long-term urbanization levels. It also enabled a rapid measurement of urbanization levels at different scales. Compared to previous similar studies, the method presented in this paper has a stronger application and popularization value.

2 Data sources and preparation

2.1 Study area

The Bohai Rim region is located within 33°30′N-46°30′N and 104°35′E-125°40′E and covers an area of 52.2×104 km2. Viewed as China’s “third engine,” it encompasses a total of 44 cities, including Beijing and Tianjin Municipalities, and Hebei, Shandong, and Liaoning provinces (Figure 1). The study area is located between mid-temperate and warm-temperate zones, where most of the area is situated in the warm-temperate zone with a semi-humid climate. Landforms and terrains of the Bohai Rim are very complex, including Shandong Hills, Haihe River Plain, Bashang Plateau, Liaohe River Plain, and Eastern Liaoning Hills. Main rivers in the region are the Yellow, Haihe, Liaohe, and Luanhe rivers. Three main railway networks, namely, the Beijing-Guangzhou, Beijing-Kowloon, and Beijing-Shanghai, link Bohai Rim to eastern, south-central, and southern China, respectively, making it a key hub in China’s transportation network.
Figure 1 An overview of the Bohai Rim region

2.2 Data description

2.2.1 Statistical data
The values for built-up urban areas were obtained from statistical yearbooks of Chinese cities (National Bureau of Statistics, 1995, 2000, 2008, 2013). We collected information on the built-up urban areas of 44 cities in the Bohai Rim for the years 1993, 1998, 2007, and 2013. These data sources are the main references when we extract built-up urban areas from DMSP/OLS nighttime light data in this study.
2.2.2 Geomorphological data
Geomorphological data were derived from 1:1,000,000 digital geomorphological database of China. Seven layers were classified based on combinations of landforms’ morphological characteristics and their genesis. These layers are: basic morphology and altitude, genesis, sub-genesis, morphology, micro-morphology, slope and aspect, and material and lithology (Cheng et al., 2011). The digital geomorphological database was created based on visual interpretation of Landsat TM/ETM images, SRTM-DEM, and maps of historical geomorphology (Cheng et al., 2011). In this study, we selected the first layer (Zhou et al., 2009) as the geomorphological unit, and extracted the basic morphological types of the Bohai Rim region (Table 1) by spatial overlay analysis.
Table 1 Basic geomorphological types in the Bohai Rim
Altitude Low altitude Middle altitude
Relief (< 1000 m) (1000-3500 m)
Plain (< 30 m) Low altitude plain (LAP) Middle altitude plain (MAP)
Platform (> 30 m) Low altitude platform (LAPF) Middle altitude platform (MAPF)
Hill (< 200 m) Low altitude hill (LAH) Middle altitude hill (MAH)
Low relief mountain (200-500 m) Low relief low altitude mountain (LRLAM) Low relief middle altitude mountain (LRMAM)
Middle relief mountain (500-1000 m) Middle relief low altitude mountain (MRLAM) Middle relief middle altitude mountain (MRMAM)
High relief mountain (1000-2500 m) — High relief middle altitude mountain (HRMAM)
Highest relief mountain (> 2500 m) — —
2.2.3 DMSP/OLS nighttime light data
DMSP/OLS nighttime light data were obtained from the State Key Laboratory of Resources and Environmental Information System (SKLREIS). The time series data for DMSP/OLS stable nighttime light imagery spanning the years 1992-2012 were initially procured from NOAA’s National Geophysical Data Center and subsequently rectified by applying a second order regression model (Elvidge et al., 2009) to eliminate inter-annual variations and response differences among sensors. The spatial resolution was 30 arc-seconds (approximately 1 km at the equator and 0.8 km at 40°N) (Ma et al., 2012). We extracted DMSP/OLS nighttime light data for 1992, 1998, 2006, and 2012 for 44 cities in the Bohai Rim (Figure 2) by setting the mask using administrative boundary data.
Figure 2 DMSP/OLS images of the Bohai Rim region from 1992 to 2012

2.3 Multiscale measurement units

2.3.1 Measurement units for administrative divisions
Measurement units for administrative divisions were delineated with three different types of measurement units. The first measurement unit was a regional unit comprising the entire region of the Bohai Rim. The second measurement unit was a city comprising the 44 municipal administrative units in the region, each of which constituted a measurement unit. The third measurement unit was an urban agglomeration unit. There were three such units in the Bohai Rim: Beijing-Tianjin-Hebei, East Liaoning Peninsula, and Shandong Peninsula. Each of these urban agglomerations was regarded as a measurement unit (Figure 3).
Figure 3 Urban agglomerations and coastal cities in the Bohai Rim
2.3.2 Measurement units based on land-sea location
Two types of measurement units based on land-sea locations were delineated. The first was comprised of coastal and non-coastal city units. A total of 17 coastal cities were included: Dalian, Dandong, Yingkou, Panjin, Jinzhou, Huludao, Qinhuangdao, Tangshan, Tianjin, Cangzhou, Binzhou, Dongying, Weifang, Yantai, Weihai, Rizhao, and Qingdao. Coastal and non-coastal cities were two main districts, with each of them constituting a measurement unit (Figure 3). The second measurement unit was distance from the coastline. The Bohai Rim region was subdivided into two types of districts located within and beyond 200 km of the coastline, respectively, with each district constituting a unit of measurement.
2.3.3 Measurement units based on terrain features
Two types of measurement units based on terrain features were considered. The first type was relief units. Five relief amplitudes were selected for the Bohai Rim region. These were: <30 m, 30-200 m, 200-500 m, 500-1000 m, and >1000 m. The second type consisted of altitude units. Five levels of altitude were observed for the Bohai Rim region: <50 m, 50-150 m, 150-300 m, 300-1000 m, and >1000 m.
2.3.4 Measurement units based on geomorphological types
The Bohai Rim region has 11 geomorphological types (Figure 4), with each type constituting a measurement unit.
Figure 4 Geomorphological types in the Bohai Rim region

3 Methods for measuring urbanization levels

3.1 Extraction of built-up urban areas

Based on previous studies (e.g. He et al., 2006a), two basic assumptions were used to extract built-up urban areas in this study. The first assumption was that the statistical data published by the National Bureau of Statistics truly reflected built-up urban areas. Thus, the extraction results should be as close as possible to match the statistical data. The second assumption was that raster patches derived from the previous period of nighttime light data should be retained for use during the subsequent period.
Considering threshold diversity among the 44 cities in the Bohai Rim during different periods, we extracted their built-up urban areas for the years 1992, 1998, 2006, and 2012. Three steps were taken for extracting built-up areas as follows:
(1) Dynamic thresholds for cities’ nighttime light data during each period were set using the dichotomy method (He et al., 2006b). Statistical values of built-up urban areas for each dynamic threshold were subsequently compiled. Results that deviated significantly from the statistical data were removed. Lastly, the possible threshold interval was determined.
(2) Boundaries of each built-up urban area under different thresholds were manually modified based on a combination of urban expansion directions and the two previously described basic assumptions. Unreasonable thresholds were excluded and the best threshold of built-up urban areas for each period was determined.
(3) Boundaries for the best thresholds during different periods were manually modified according to the two basic assumptions and referring to the urban expansion directions.
By comparing with the statistical data, we evolved a method to conduct human-computer interactive boundary correction. Consequently, we derived the built-up urban area of the Bohai Rim in 1992, 1998, 2006, and 2012 (Figure 5), with relative error less than ±4% between the extraction results and the statistical data (Table 2).
Figure 5 Built-up urban areas of the Bohai Rim derived from DMSP/OLS data for the years 1992, 1998, 2006, and 2012
Table 2 Relative errors between the extraction results and the statistical data
City Relative error (%) City Relative error (%)
1992 1998 2006 2012 1992 1998 2006 2012
Beijing -1.88 3.03 -1.53 3.91 Liaoning -0.68 2.95 0.29 -0.81
Tianjin -1.91 2.67 2.41 3.18 Panjin -2.88 3.29 -1.11 1.71
Shijiazhuang -1.41 -0.12 -0.15 -0.02 Tieling -1.09 2.81 -3.82 3.77
Tangshan -1.18 0.00 -0.94 0.15 Chaoyang 2.37 0.82 -1.10 2.00
Qinhuangdao 0.65 -1.52 0.43 6.61 Huludao 1.57 -1.23 0.83 -0.81
Handan -0.69 1.26 -0.70 1.12 Jinan -0.60 -0.24 -0.09 0.00
Xingtai 0.38 0.18 0.17 0.82 Qingdao -1.90 1.40 1.06 0.39
Baoding 1.24 0.44 -0.52 -0.20 Zibo -0.42 0.33 0.14 -0.20
Zhangjiakou -2.72 -1.42 1.27 2.29 Zaozhuang 1.58 0.66 1.58 1.29
Chengde -0.77 2.83 -1.05 -0.05 Dongying -1.42 -0.87 1.58 1.75
Cangzhou -2.22 -2.46 0.30 1.40 Yantai -0.02 0.13 0.15 -0.20
Langfang -2.24 -0.07 -0.41 3.68 Weifang -0.22 -0.87 0.46 0.32
Hengshui -0.42 1.32 -0.19 3.65 Jining 0.83 -2.69 1.99 -0.34
Shenyang -3.27 -0.17 -0.17 0.07 Taian -1.84 1.74 -1.13 0.63
Dalian -3.21 -0.79 -0.30 0.06 Weihai 0.49 -2.04 0.34 0.02
Anshan -1.23 -1.02 -0.78 -0.18 Rizhao 1.58 0.17 1.40 1.77
Fushun 0.22 0.35 0.65 2.63 Laiwu -2.90 -0.94 1.04 0.69
Benxi -1.28 1.32 -0.32 2.07 Linyi -0.94 1.74 -0.33 0.23
Dandong -1.44 -1.23 -1.92 1.81 Dezhou -1.10 -1.72 1.86 0.47
Jinzhou -1.04 -2.00 2.18 2.07 Liaocheng 2.43 -0.42 -1.26 1.15
Yingkou 1.07 -0.25 -0.74 2.63 Binzhou -0.94 -2.09 -1.01 -0.16
Fuxin -1.24 0.59 2.42 1.22 Heze 0.17 -1.97 -0.60 1.36

3.2 Accuracy assessment of the extraction results

The high resolution Landsat TM/ETM+ data were used to verify the accuracy and reliability of urban land extraction results. Given that the spatial resolution of the Landsat TM/ETM+ was 30 m, which was well above the 1 km spatial resolution of the DMSP/OLS nighttime light data, we assume that the use of Landsat TM/ETM+ data to evaluate the accuracy of the urban land information derived from the DMSP/OLS nighttime light data was feasible and reliable (Yang et al., 2013; Henderson et al., 2003). Considering data collection and cost factors, we selected Beijing as a representative example for calculating the overall accuracy (OA) and the Kappa index for the evaluation of accuracy (Nishii et al., 1999).

3.3 Construction of indexes on urbanization levels

The level of urbanization can be evaluated using the ratio between urban land and total land (Pan et al., 2014). Accordingly, after deriving the built-up urban area from nighttime light data, we constructed an index to measure levels of urbanization in the Bohai Rim from 1992 to 2012. This urbanization level index, U, was calculated as:
where C is the region’s built-up urban area (km2) and L is the region’s total land area (km2).
To analyze long-term variations of the urbanization level, we introduced the average annual urbanization growth rate, V, calculated as:
where and are urbanization levels for the year tb and ta, respectively. The time interval during the measurement period was expressed by tb-ta.

4 Analysis of urbanization measurement levels

Considering data collection and cost factors, we selected Beijing as a representative example to verify the accuracy of our extraction of urban land, and to evaluate the reliability of our results. Our assessment showed that the results extracted using DMSP/OLS nighttime light data could well reflect the actual status of urban development. We used four different measurement scale units, namely, administrative division, land-sea location, terrain feature, and geomorphological types, to measure Bohai Rim’s urbanization level and to analyze its spatiotemporal variation from 1992 to 2012. Overall, urban expansion was clearly evident for each measurement scale unit from 1998 to 2006. Gaps in urbanization levels, based on the different measurement scale units, were significant and showed an increasing trend.

4.1 Evaluation of accuracy

High resolution remote sensing images—namely, Landsat 4/5 TM data for Beijing, taken in June 1992, November 1998, and April 2006, and Landsat 7 ETM+ SLC-off data for Beijing, taken in May 2012—were obtained from the United States Geological Survey (http://glovis.usgs.gov/). After preprocessing reference data (e.g., destriping and geometric correction), we applied visual interpretation as our primary method and supervised classification as our supplementary method to extract information of built-up urban areas from Landsat TM/ETM+ data. Assuming that the above information accurately reflected the actual status of the built-up urban areas for the corresponding period, we compared this information with our extraction results based on DMSP/OLS nighttime light data. We subsequently calculated the overall accuracy and Kappa index of the extraction results based on DMSP/OLS nighttime light data for Beijing during the different study periods (Table 2 and Figure 6). The accuracy evaluation revealed that the average value of the overall accuracy and the Kappa index were 97.70% and 0.79, respectively. The result obtained from DMSP/OLS nighttime light data showed substantial agreement with information based on Landsat TM/ETM+ data on spatial patterns. Therefore, the built-up urban area extracted from DMSP/OLS nighttime light data could be a good reflection of the actual status of urban development in the Bohai Rim region in 1992, 1998, 2006, and 2012.
Figure 6 Accuracy assessment of selected urban areas in the Bohai Rim using Landsat TM/ETM+ data
Table 3 Accuracy assessment of selected urban areas in the Bohai Rim using Landsat TM/ETM+ data
Landsat extraction results Urban (%) Non-urban (%) Total (%)
DMSP/OLS extraction results
Urban (%) 2.19 0.53 2.72
1992 Non-urban (%) 0.59 96.69 97.28
Total (%) 2.78 97.22 100.00
OA = 98.88%; Kappa = 0.79
Urban (%) 2.76 0.31 3.07
1998 Non-urban (%) 1.26 95.67 96.93
Total (%) 4.02 95.98 100.00
OA = 98.43%; Kappa = 0.77
Urban (%) 6.62 0.75 7.37
2006 Non-urban (%) 2.34 90.29 92.63
Total (%) 8.96 91.04 100.00
OA = 96.91%; Kappa = 0.79
Urban (%) 7.50 0.50 8.00
2012 Non-urban (%) 2.93 89.07 92.00
Total (%) 10.43 89.57 100.00
OA = 96.57%; Kappa = 0.80

4.2 Units based on administrative division

From 1992 to 2012, the total built-up area of the Bohai Rim region increased from 3104.58 km2 to 8060.51 km2, while the total urbanization level rose from 0.60% to 1.56%. The urbanization level rose annually with an average of 0.05%, and the average annual growth rate was 4.89%. Between 1992 and 1998, Bohai Rim’s urbanization proceeded at a slow pace with an average annual growth rate of 3.16%. However, between 1998 and 2006, it showed a sharp increase with an average annual growth rate of 6.50%. The growth rate decreased slightly from 2006 to 2012 with an average annual growth rate of 4.50%. Variations in the urbanization level based on municipal administrative units are provided in Figure 7.
Figure 7 Urbanization levels of different cities in the Bohai Rim region from 1992 to 2012
Different cities showed varying degrees of evolution in their urbanization levels. Urbanization levels in Beijing and Tianjin have always been much higher than those of other cities. High value areas of urbanization were initially distributed in metropolitan locations concentrated in capitals. However, these have recently extended to coastal city areas, with Qingdao as the center, and ultimately giving rise to four nuclear types of urbanization patterns centering on Beijing, Shenyang, Jinan, Shijiazhuang, and Qingdao. The shift of high value areas has evidenced the spatial characteristic of being located close to the coast.
Based on the comparison of urbanization levels among urban agglomeration units, relevant characteristics of the three major urban agglomerations in the Bohai Rim region is shown in Figure 8a. The urbanization level of Beijing-Tianjin-Hebei urban agglomeration was always higher than that of the entire region. Between 1992 and 2002, urbanization level of the Shandong Peninsula urban agglomeration was below that of the Bohai Rim. However, urbanization gradually increased over time, and had surpassed the level of entire region after 2002. The disparity in the urbanization levels between Shandong Peninsula and the Bohai Rim as a whole showed a trend of first decreasing and then increasing. In the last two decades, urbanization level of the East Liaoning Peninsula urban agglomeration has shown a slight increase. During the first decade, the urbanization level of this area was higher than that of the whole region, and the gap between them was shrinking. However, during the latter decade, the gap increased again, and the urbanization level of this agglomeration dipped below that of the region. During the period from 1992 to 2012, the Shandong Peninsula urban agglomeration evidenced the highest average annual growth rate (7.25%), followed by the Beijing-Tianjin-Hebei urban agglomeration (4.55%). The East Liaoning Peninsula urban agglomeration had the lowest rate (3.14%). During the last two decades, the average annual growth rate in the East Liaoning Peninsula urban agglomeration maintained a steady rise, while those of the Beijing-Tianjin-Hebei and Shandong Peninsula urban agglomerations first increased and subsequently decreased.
Figure 8 Urbanization levels of different regional units in the Bohai Rim

4.3 Units based on land-sea location

Figure 8b shows the characteristics of urbanization in coastal and non-coastal cities in the Bohai Rim region. The urbanization levels of coastal cities were always higher than those of the non-coastal cities and of the region as a whole. However, from 1992 to 2012, the average annual growth rate of urbanization in non-coastal cities (5%) was slightly higher than the rate in coastal cities (4.73%). Specifically, the average annual growth rate of the non-coastal cities (7.63%) was higher than that of coastal cities (4.81%) between 1998 and 2006. However, the rate was lower than that of coastal cities during the other two periods.
Figure 8c shows the characteristics of areas located at different distances from the coastline in the Bohai Rim. Levels of urbanization in areas within 200 km of the coastline were much higher than those located beyond 200 km. The average annual growth rate of urbanization in areas within 200 km of the coastline was slightly greater than that of areas beyond 200 km, and the rate of both areas showed a trend of first increasing and then decreasing.

4.4 Units based on terrain features

Our distribution analysis of the built-up urban areas in locations with different reliefs (Figures 9a and 10a) revealed that built-up urban areas were mainly distributed in locations where the relief was below 30 m. Locations where the relief ranged from 30 m to 200 m evidenced some built-up urban areas, while there were fewer such areas in locations where the relief was above 200 m. The urbanization growth trends in areas with varying relief units differed, with the growth trend being more apparent in locations where the relief was below 30 m. The level of urbanization was thus negatively correlated with relief amplitude. Promotion of urban development in locations where relief ranges from 30 m to 200 m could thus be of considerable value in helping to ensure sustainable development of the urbanization spatial pattern in the Bohai Rim.
Our distribution analysis of the built-up urban areas in locations at different altitudes (Figures 9b and 10b) revealed that more than 93.5% of the built-up urban areas were distributed in location at altitudes below 150 m, and especially in locations at altitudes below 50 m. Levels of urbanization in areas below 50 m were slightly higher than those of the areas at altitudes ranging between 50 m and 150 m, with this gap evidencing an increasing trend. Urbanization levels in areas above 150 m were evidently lower than those in low altitude areas. Thus, the level of urbanization was negatively correlated with altitude. In recent years, areas at altitudes below 50 m have undergone a phase of accelerated urbanization. Consequently, promotion of urban development in areas at altitudes ranging from 50 m to 150 m could be an important direction for urban construction.
Figure 9 Built-up urban areas of different topographical units in the Bohai Rim from 1992 to 2012
Figure 10 Urbanization levels of different topographical units in the Bohai Rim from 1992 to 2012

4.5 Units based on geomorphological types

Our analysis results revealed that the built-up urban areas were distributed in locations characterized by nine geomorphological types (Figures 9c and 10c). These included low altitude plains, low altitude platforms, low altitude hills, low relief and low altitude mountains, middle relief and low altitude mountains, middle altitude plains, low relief and middle altitude mountains, middle relief and middle altitude mountains, and high relief and middle altitude mountains. The majority of the built-up urban areas were located in low altitude plains, and some also found in low altitude platforms and hills. The level of urbanization in low altitude platforms was higher than that in locations characterized by other geomorphological types. However, the level of urbanization in low altitude plains rose faster, and the gap between these two geomorphological types shrunk evidently. Slowing down the pace of urbanization in low altitude platforms and stimulating the urbanization process in low altitude hilly areas could facilitate timely and proper control of this process in the Bohai Rim region.

5 Discussion

Comparing with the statistical data for the 44 cities in the Bohai Rim region, we determined optimal thresholds and obtained spatial information of the built-up area for each city for the years 1992, 1998, 2006 and 2012 by applying a human-computer interactive boundary correction method. Compared with the built-up urban areas extracted from Landsat TM/ETM+ data, the built-up urban area extracted from DMSP/OLS nighttime light data had an average overall accuracy of 97.70% and an average Kappa coefficient of 0.79. This indicated that the method has a high reliability and could well reflect the actual status of the built-up urban areas. Consequently, we developed a method for measuring urbanization levels using DMSP/OLS nighttime light data, and comprehensively explored urbanization levels in the Bohai Rim using four measurement scale units: administrative division, land-sea location, terrain feature, and geomorphological type. Given the difficulty of obtaining high quality, multi-temporal Landsat TM/ETM+ data for the 44 cities in the Bohai Rim region, and the cost of extracting urban land use information from Landsat TM/ETM+ data of these cities for the four different time periods, we did not evaluate the accuracy of extracted built-up urban areas of all cities in the Bohai Rim. Instead, we used only the Landsat TM/ETM+ data of Beijing for the years 1992, 1998, 2006, and 2012 to conduct the accuracy assessment. The use of more Landsat TM/ETM+ images of other cities to evaluate the accuracy of extracted urban areas would make our conclusions much more convincing. However, it should be noted that this study has succeeded in measuring the urbanization level in a fast, efficient, and multidimensional way using DMSP/OLS nighttime light data. It measured the level of urbanization in different locations and under different natural conditions. This can provide data support when analyzing the progress of urbanization and can facilitate discovery of potential land issues in different places, and therefore helps to provide suggestions on future directions and paces of urban expansion in the Bohai Rim. It also provided a methodology and technical support for measuring urbanization at the national level for a long time series. This will facilitate regulation of wide areas, even nationwide urban land use patterns, and promote healthy and harmonious urbanization in China.

6 Conclusions

We evolved a human-computer interactive boundary correction method to determine built-up urban areas and subsequently proposed a method for measuring levels of urbanization based on DMSP/OLS nighttime light data. To comprehensively measure the level of urbanization in the Bohai Rim region from 1992 to 2012, we established four different measurement scale units, namely, administrative division, land and sea location, terrain feature, and geomorphological type. Our major findings were as follows.
First, the extraction results based on DMSP/OLS nighttime light data showed substantial agreement with those based on Landsat TM/ETM+ data on spatial patterns, with an average overall accuracy of 97.70% and an average Kappa coefficient of 0.79. This finding demonstrated a high level of reliability of the extraction results that could well reflect the actual status of built-up urban areas in the region.
Second, the Bohai Rim’s urbanization level has increased annually, demonstrating a significant growth rate during the period from 1998 to 2006. The urbanization level of the Beijing-Tianjin-Hebei urban agglomeration was always higher than that of the entire Bohai Rim region, while the urbanization level of the Shandong Peninsula urban agglomeration showed a marked increase, exceeding the level of the Beijing-Tianjin-Hebei urban agglomeration in 2002. Areas with high urbanization levels have evidenced movement from capital to coastal cities, indicating a spatial characteristic of being located close to the coast.
Third, the distribution of the built-up urban areas showed a certain degree of zonal variation. The urbanization level was negatively correlated with relief amplitude and altitude. A high level of urbanization was found in low altitude platforms and low altitude plains, with a gradually narrowing of the gap between these two geomorphological types.
Fourth, based on comprehensive measurements and results of multiscale units of the urbanization level of the Bohai Rim, we conclude that in the foreseeable future, low altitude hills—especially those at altitudes between 50 m and 150 m, and with reliefs ranging from 30 m to 200 m—will be key areas of urban construction in the region. The pace of urbanization in low altitude platforms, especially in locations below 50 m should be reduced.

The authors have declared that no competing interests exist.

References

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1
Amaral S, Camara G, Monteiro A M Vet al., 2005. Estimating population and energy consumption in Brazilian Amazonia using DMSP nighttime satellite data. Computers,Environment and Urban Systems, 29(2): 179-195.ABSTRACT This paper describes a methodology to assess the evidence of human presence and human activities in the Brazilian Amazonia region using DMSP/OLS night-time satellite sensor imagery. It consists on exploring the potential of the sensor data for regional studies analysing the correlation between DMSP night-time light foci and population, and the correlation between DMSP night-time light foci and electrical power consumption. In the mosaic of DMSP/OLS night-time light imagery from September 1999, 248 towns were detected from a total of 749 munic pios in Amazonia. It was found that the night-time light foci were related to human presence in the region, including urban settlements, mining, industries, and civil construction, observed in ancillary Landsat TM and JERS imagery data. The analysis considering only the state of Par&aacute; revealed a linear relation (R2=0.79) between urban population from the 1996 census data and DMSP night-time light foci. Similarly, electrical power consumption for 1999 was linearly correlated with DMSP night-time light foci. Thus the DMSP/OLS imagery can be used as an indicator of human presence in the analysis of spatial&ndash;temporal patterns in the Amazonia region. These results are very useful considering the continental dimension of Amazonia, the absence of demographic information between the official population census (every 10 years), and the dynamics and complexity of human activities in the region. Therefore DMSP night-time light foci are a valuable data source for global studies, modelling, and planning activities when the human dimension must be considered throughout Amazonia.

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2
Cahoon D R, Stocks B J, Levine J Set al., 1992. Seasonal distribution of African Savanna Fires.Nature, 359: 812-815.SAVANNAS consist of a continuous layer of grass interspersed with scattered trees or shrubs, and cover ~10 million square kilometres of tropical Africa. African savanna fires, almost all resulting from human activities, may produce as much as a third of the total global emissions from biomass burning. Little is known, however, about the frequency and location of these fires, and the area burned each year. Emissions from African savanna burning are known to be transported over the mid-Atlantic, south Pacific and Indian oceans; but to study fully the transport of savanna fire emissions, the spatial and temporal variations in regional savanna burning and the seasonality of the atmospheric circulation must be considered simultaneously. Here we describe the temporal and spatial distribution of savanna fires over the entire African continent, as determined from night-time satellite imagery. We find that, contrary to expectations, most fires are left to burn uncontrolled, so that there is no strong diurnal cycle in the fire frequency. The knowledge gained from this study regarding the distribution and variability of fires will aid monitoring of the climatically important trace gases emitted from burning biomass.

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3
Cao Xin, Chen Jin, Imura Het al., 2009. A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data.Remote Sensing of Environment, 113(10): 2205-2209.Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.

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4
Chand T R K, Badarinath K V S, Elvidge C Det al., 2009. Spatial characterization of electrical power consumption patterns over India using temporal DMSP/OLS nighttime satellite data.International Journal of Remote Sensing, 30(3): 647-661.Not Available

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Chen Jin, Zhuo Li, Shi Peijunet al., 2003. The study on urbanization process in China based on DMSP/OLS data: Development of a light index for urbanization level estimation. Journal of Remote Sensing, 7(3): 168-175+241. (in Chinese)Urbanization, stimulated by striking economic development, has been proceeded in China on a large scale and with striking rapidity in the past two decades. It is necessary to monitor and model urbanization process of China for its sustainable development. This paper presents a new light index for regional urbanization level estimation considering the light spatial distribution and intensity based on DMSP/OLS data, which was pre-processed by Japan National Institute of Environmental Studies. The correlation analysis between light index and composite urbanization index was carried out in province scale. The result shows that there is significant relationship between two indexes. The regression model for composite urbanization index estimation using light index was also developed with R 2 equal to 0.793. It suggests that light index is an effective and applicable index for regional urbanization analysis and monitoring. Through the analysis of light index change in China during 1992 to 1998, it is shown that the urbanization level in China is different from high level in East China to low level in West China, and urbanization level was improved largely during 1992 to 1998, especially in provinces of East China.

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Chen Mingxing, Lu Dadao, Liu Hui, 2010. The provincial pattern of the relationship between China’s urbanization and economic development.Journal of Geographical Sciences, 65(12): 1443-1453. (in Chinese)

7
Chen Yonglin, Xie Bingkang, Li Xiaoqinget al., 2015. The preliminary research on relationship between the change of land use and urbanization in Changsha from 2003 to 2013.Economic Geography, 35(1): 149-154. (in Chinese)It aims at revealing land use and land cover changes in Changsha from 2003 to 2013. Combining the regression analytical method with GIS and ENVI technology, based on the classification and interpretation of TM images in 2003, 2006,2009 and 2013, the changes of land use and relationship between land use change and urbanization response to the industrial structure evolvement are analyzed in this paper in the city of Changsha. It has used the area ratio of land use types, the land use dynamic index, the matrix of the change of land use types and the overall development of the urbanization level. The results show that the areas of land have changed obviously in Changsha City from 2003 to 2013. It presents following several characteristics: the areas of arable land have decreased steadily, but the reduce amplitude have declined; the areas of forest land firstly have increased and then reduced; The areas of building land had increased steadily, and the increase amplitude had extended. The forest land and arable land are changing into building land mostly mean while, the change of land use types is obvious at urban fringe; The number of change of land use types are correlating with the degree of urbanization linearly. The increase of urbanization will result in the reduce of agricultural land and the Increase of building land. There is positive feedback relationship among land urbanization,industry urbanization and population urbanization. The development of the relationship between the change of land use and the urbanization experiences three periods specifically, the early spread period of the city space, the accelerated expansion period of the city space and the rapid expansion period of the city space.

8
Cheng Weiming, Zhou Chenghu, Chai Huixiaet al., 2011. Research and compilation of the Geomorphologic Atlas of the People’s Republic of China (1:1,000,000).Journal of Geographical Sciences, 21(1): 89-100.Geomorphologic maps are one of the most fundamental materials of the natural environment. They have been widely used in scientific research, resource exploration and extraction, education and military affairs etc. An editorial committee was established in 2001 to collect materials for researching and compiling a set of new 1:1,000,000 geomorphologic atlas of China. A digital geomorphologic database was created with visual interpretation from Landsat TM/ETM imageries and SRTM-DEM etc. The atlas compiled from the database was finished. The main characteristics of the atlas are as follows: Firstly, Landsat TM/ETM imageries, published geomorphologic maps or sketches, geographical base maps, digital geological maps, and other thematic maps were collected, which were uniformly geometrically rectified, clipped into uniform sheets, and stored in the foundation database. Secondly, based on the legends of 15 sheets 1:1,000,000 maps published in the 1980s, a geomorphologic classification system was built by combining morphology and genesis types. The system comprised seven hierarchical layers: basic morphology, genesis, sub-genesis, morphology, micro-morphology, slope and aspect, material composition and lithology. These layers were stored in the database during visual image interpretation. About 2000 kinds of morphogenesis and 300 kinds of morpho-structure were interpreted. Thirdly, the legend system was built, which included color, symbol bases and note bases etc., compilation standards and procedures were developed, 74 sheets of 1:1,000,000 covering all land and sea territories of China were compiled, the 1:1,000,000 geomorphologic atlas of the People&#8217;s Republic of China was finished and published. The atlas will fill the blanks in national basic scale thematic maps, and the geomorphologic database could be applied widely in many fields in the future.

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Cheng Weiming, Zhou Chenghu, Li Bingyuanet al., 2011. Structure and contents of layered classification system of digital geomorphology for China.Journal of Geographical Sciences, 21(5): 771-790.This paper presents the structure and contents of a standardized layered classification system of digital geomorphology for China. This digital classification method combines landforms characteristics of morphology with genesis. A total of 15 categories of exogenic and endogenic forces are divided into two broad categories: morpho-genetic and morpho-structural landforms. Polygon patches are used to manage the morpho-genetic types, and solitary points, lines and polygons are used to manage the morpho-structural types. The classification method of digital morpho-genetic types can be divided into seven layers, i.e. basic morphology and altitude, genesis, sub-genesis, morphology, micro-morphology, slope and aspect, material and lithology. The method proposes combinations of matrix forms based on layered indicators. The attributes of every landform types are obtained from all or some of the seven layers. For the 15 forces categories, some classification indicators and calculation methods are presented for the basic morphology, the morphologic and sub-morphologic landforms of the morpho-genetic types. The solitary polygon, linear and point types of morpho-structural landforms are presented respectively. The layered classification method can meet the demands of scale-span geomorphologic mapping for the national primary scales from 1:500,000 to 1:1,000,000. The layers serve as classification indicators, and therefore can be added and reduced according to mapping demands, providing flexible expandability.

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Doll C, Muller J P, Morley J G., 2006. Mapping regional economic activity from nighttime light satellite imagery.Ecological Economics, 57(1): 75-92.The recognition that the elements of the nthropocene play a critical role in global change processes means that datasets describing elements of the socio-economic environment are becoming increasingly more desirable. The ability to present these data in a gridded format as opposed to the traditionally reported administrative units is advantageous for incorporation with other environmental datasets. Night-time light remote sensing data has been shown to correlate with national-level figures of Gross Domestic Product (GDP). Night-time radiance data is analysed here along with regional economic productivity data for 11 European Union countries along with the United States at a number of sub-national levels. Night-time light imagery was found to correlate with Gross Regional Product (GRP) across a range of spatial scales. Maps of economic activity at 5 km resolution were produced based on the derived relationships. To produce these maps, certain areas had to be excluded due to their anomalously high levels of economic activity for the amount of total radiance present. These areas were treated separately from other areas in the map. These results provide the first detailed examination of night-time light characteristics with respect to local economic activity and highlight issues, which should be considered when undertaking such analysis.

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Doll C, Pachauri S, 2010. Estimating rural populations without access to electricity in developing countries through night-time satellite imagery.Energy Policy, 38(10): 5661-5670.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">A lack of access to energy and, in particular, electricity is a less obvious manifestation of poverty but arguably one of the most important. This paper investigates the extent to which electricity access can be investigated using night-time light satellite data and spatially explicit population datasets to compare electricity access between 1990 and 2000. We present here the first satellite derived estimates of rural population without access to electricity in developing countries to draw insights on issues surrounding the delivery of electricity to populations in rural areas. The paper provides additional evidence of the slow progress in expansion of energy access to households in Sub-Saharan Africa and shows how this might be ascribed in part due to the low population densities in rural areas. The fact that this is a continent with some of the lowest per-capita income levels aggravates the intrinsic difficulties associated with making the investments needed to supply electricity in areas with low population density and high dispersion. Clearly, these spatial dimensions of the distributions of the remaining unelectrified populations in the world have an impact on what options are considered the most appropriate in expanding access to these households and the relative attractiveness of decentralized options.</p>

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Elvidge C D, Cinzano P, Pettit D Ret al., 2007. The nightsat mission concept.International Journal of Remote Sensing, 28(12): 2645-2670.Not Available

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Elvidge C D, Sutton P C, Ghosh Tet al., 2009. A global poverty map derived from satellite data.Computer and Geosciences, 35(8): 1652-1660.A global poverty map has been produced at 30 arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.

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Elvidge C D, Ziskin D, Baugh K Eet al., 2009. A fifteen year record of global natural gas flaring derived from satellite data.Energies, 2(3): 595-622.We have produced annual estimates of national and global gas flaring and gas flaring efficiency from 1994 through 2008 using low light imaging data acquired by the Defense Meteorological Satellite Program (DMSP). Gas flaring is a widely used practice for the disposal of associated gas in oil production and processing facilities where there is insufficient infrastructure for utilization of the gas (primarily methane). Improved utilization of the gas is key to reducing global carbon emissions to the atmosphere. The DMSP estimates of flared gas volume are based on a calibration developed with a pooled set of reported national gas flaring volumes and data from individual flares. Flaring efficiency was calculated as the volume of flared gas per barrel of crude oil produced. Global gas flaring has remained largely stable over the past fifteen years, in the range of 140 to 170 billion cubic meters (BCM). Global flaring efficiency was in the seven to eight cubic meters per barrel from 1994 to 2005 and declined to 5.6 m 3 per barrel by 2008. The 2008 gas flaring estimate of 139 BCM represents 21% of the natural gas consumption of the USA with a potential retail market value of $68 billion. The 2008 flaring added more than 278 million metric tons of carbon dioxide equivalent (CO 2e) into the atmosphere. The DMSP estimated gas flaring volumes indicate that global gas flaring has declined by 19% since 2005, led by gas flaring reductions in Russia and Nigeria, the two countries with the highest gas flaring levels. The flaring efficiency of both Russia and Nigeria improved from 2005 to 2008, suggesting that the reductions in gas flaring are likely the result of either improved utilization of the gas, reinjection, or direct venting of gas into the atmosphere, although the effect of uncertainties in the satellite data cannot be ruled out. It is anticipated that the capability to estimate gas flaring volumes based on satellite data will spur improved utilization of gas that was simply burnt as waste in previous years.

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15
Fan Hui, Liu Weidong, Wu Zebinet al., 2014. The coupling coordination evaluation between population urbanization and land urbanization in Zhejiang Province.Economic Geography, 34(12): 21-28. (in Chinese)

16
Fan Junfu, Ma Ting, Zhou Chenghuet al., 2013. Changes in spatial patterns of urban landscape in Bohai Rim from 1992 to 2010 using DMSP-OLS data.Journal of Geo-Information Science, 15(2): 280-288. (in Chinese)The photoelectric amplification characteristics of Operational Linescan System(OLS) sensors on board of the Defense Meteorological Satellite Program's(DMSP) satellites make the instruments sensitive to low visible lights in the night which can distinguish the differences of light signals between urban and rural areas.Remotely sensed nighttime lights datasets derived from the DMSP-OLS sensors have been extensively applied to assess and monitor the process of urbanization and human activities,which has become an important data source for studies on regional urbanization and human activities.Methods used to extract urban built-up areas from DMSP-OLS data,such as empirical global thresholding-based methods and the sudden detection method,cannot avoid their own defects.The experience thresholding values are not universal in different regions and the sudden detection method cannot be applied in large scales.In this study,we corrected the experience thresholding values by introducing statistical data of some sample cities in the research area which combined with a calibration process to DMSP-OLS time serial data for extracting urban built-up area from satellite-based nighttime light data at large temporal and spatial scales.Nine landscape metrics: the number of patches(NP),the landscape total area(TA),the mean patch size(MPS),the largest patch index(LPI),the patches density of per hundred km2(PDh),the landscape shape index(LSI),the total edge length(TE),the edge density(ED) and the radius of gyration(GYRATE) are calculated by the FRAGSTATS3.3 software to analysis the spatial pattern change characteristics of urban area in Bohai Rim.The study showed that from 1992 to 2010,the urbanization in Bohai Rim experienced a continuing and rapid process.In this region,the total urban built-up areas expanded for 2.14 times,the average built-up area of cities increased for 76%,the gyrate of extracted urban patches expanded about 26.5% which suggested that the complexity of urban patch shapes were increased.The amount of detected urban patches got 82% increase but the number of isolate cities in each 100 km2 were decreased by about 76% which implied that the expansion of traditional cities was the dominant factor of the area increasing rather than continuously emerging towns.The expansions of metropolises were slower than small cities,and the overall landscape fragmentation degree was decreased gradually with the trend of urban area connection between core cities and their exurbs.

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Ghosh T, Powell R L, Elvidge C Det al., 2010. Shedding light on the global distribution of economic activity.The Open Geography Journal, 7(3): 148-161.Collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Nighttime lights satellite imagery and the LandScan population grid provide an alternative means for measuring economic activity. We have developed a model for creating a disaggregated map of estimated total (formal plus informal) economic activity for countries and states of the world. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for China, India, Mexico, and the United States and at the national level for other countries of the world, and subsequently unique coefficients were derived. Multiplying the unique coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a spatially disaggregated 1 km 2 map of total economic activity.

18
Guo Shihong, Wang Fuxi, Gao Ming, 2014. Spatial–temporal coupled coordination between population urbanization and land urbanization in Shandong Peninsula.Economic Geography, 34(3): 72-78. (in Chinese)Shandong Peninsula was taken as the study area,and the index system of population urbanization and land urbanization was established,then the coupled coordination model was introduced to calculate the quality index of population urbanization and land urbanization,the coupled degree and coordinated development degree of 8 cities in Shandong Peninsula by using the variance of the weight method. The coupled coordination between population urbanization and land urbanization were analyzed spatial-temporally based on the results obtained from the calculation. The results show that the level of urbanization can only reflect the pace of urbanization,which is not equivalent to the quality of urbanization; in the aspect of temporal characteristics,the rate of land urbanization was faster than the rate of population urbanization significantly in Shandong Peninsula from 1999 to 2010,and the area of urban land increased rapidly from 2004,which led to the quality of land urbanization lower than the quality of population urbanization significantly; in the aspect of spatial characteristics,the general coordinated development level is low in Shandong Peninsula,and the coupled degree is low in Yantai,Rizhao,and Weihai City.

19
He Chunyang, Li Jinggang, Chen Jinet al., 2015. The urbanization model and process in Bohai Sea surrounding area in the 1990s by using DMSP/OLS data.Acta Geographica Sinica, 60(3): 409-417. (in Chinese)The paper firstly derived urban information from Defense Meteorologica l Satellite Program/Operational Linescan System (DMSP/OLS) data in 1992, 1996 an d 1998 with the support of statistical data, then developed three basic urbaniza tion models of polygon-urbanization, line-urbanization and point-urbanization in urban agglomerations from viewpoint of spatial analysis. The conclusions are as follows: (1) Urban patch numbers in the Bohai Sea surrounding area increased fr om 1659 to 2053 with the average increased number of about 66. Meanwhile, the sm all urban patches took over a large percentage in the region and the patch densi ty increased fast. In addition, the urban barycenter of the region showed the tr end to northeast from 1992 to 1998. The urbanization in the Bohai Sea surroundin g area in the 1990s is fast and obvious. (2) The urbanization in the Bohai Sea s urrounding area can be reflected by the polygon-urbanization around the big citi es, the line-urbanization around the transportation lines and the point-urbaniza tion emerging in the wide region. Of them, the polygon-urbanization was dominant . It is obvious within the 3-4 km areas surrounding the urban patches. The line- urbanization and point-urbanization in the region was relatively small, but both of them showed the obvious increasing trend.

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He Chunyang, Li Jinggang, Chen Jinget al., 2006. The urbanization process of Bohai Rim in the 1990s by using DMSP/OLS data.Journal of Geographical Sciences, 16(2): 174-182.<a name="Abs1"></a>This paper firstly derived urban information from Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) data in 1992, 1996 and 1998 with the support of statistical data, and then developed three basic urban models of polygon-urbanization, line-urbanization and point-urbanization in urban agglomerations from viewpoint of spatial analysis. The conclusions are as follows: (1) Urban patch numbers in the Bohai Rim increased from 1659 to 2053 with an annual average increase number of about 66. Meanwhile, the small urban patches accounted for a larger proportion in the region and the patch density increased fast. In addition, the urban barycenter of the region showed a moving trend toward northwest from 1992 to 1998. Urbanization in the Bohai Rim in the 1990s is fast and obvious. (2) Urbanization in the Bohai Rim can be reflected by three basic processes, i.e., the polygon-urbanization around the big cities, the line-urbanization around the transportation lines and the point-urbanization emerging in large areas. Of them, the polygon-urbanization has been in dominance. It is obviously within an effective range of 3&#8211;4 km surrounding the urban patches. The line-urbanization and point-urbanization in the region was relatively small, both of which showed an obvious increasing trend.

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He Chunyang, Ma Qun, Li Tonget al., 2012. Spatial-temporal dynamics of electric power consumption in Chinese Mainland from 1995 to 2008 modeled using DMSP/OLS stable nighttime lights data.Journal of Geographical Sciences, 22(1): 125-136.Abstract<br/><p class="a-plus-plus">Electric power consumption (EPC) is one of the basic indices for evaluating electric power use. Obtaining timely and accurate data on the spatiotemporal dynamics of EPC is crucial for understanding and practical deployment of electric power resources. In this study, an EPC model was developed using stable nighttime lights time-series data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS). The model was used to reconstruct the spatial patterns of EPC in Chinese Mainland at the county level from 1995 to 2008. In addition, the spatiotemporal dynamics of EPC were analyzed, and the following conclusions were drawn. (1) The EPC model reliably represented the spatiotemporal dynamics of EPC in Chinese Mainland with approximately 70% accuracy. (2) The EPC in most regions of Chinese Mainland was at low to moderate levels, with marked temporal and spatial variations; of high-level EPC, 58.26% was concentrated in eastern China. Six urban agglomerations (Beijing-Tianjin-Tangshan region, Shanghai-Nanjing-Hangzhou region, Pearl River Delta, Shandong Peninsula, middle-south of Liaoning Province, and Sichuan Basin) accounted for 10.69% of the total area of Chinese Mainland but consumed 39.23% of the electricity. (3) The EPC of most regions in Chinese Mainland increased from 1995 to 2008, and 64% of the mainland area showed a significant increase in EPC. Moderate increases in EPC were found in 61.62% of eastern China and 80.65% of central China from 1995 to 2008, whereas 75.69% of western China showed no significant increase in EPC. Meanwhile, 77.27%, 89.35%, and 66.72% of the Shanghai-Nanjing-Hangzhou region, Pearl River Delta, and Shandong Peninsula, respectively, showed high-speed increases in EPC. Moderate increases in EPC occurred in 71.12% and 72.13% of the Beijing-Tianjin-Tangshan region and middle-south of Liaoning Province, respectively, while no significant increase occurred in 56.34% of the Sichuan Basin.</p><br/>

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He Chunyang, Shi Peijun, Li Jingganget al., 2006a. The study on the reconstruction of urbanization process in China during the 1990s based on DMSP/OLS data and statistics.Chinese Science Bulletin, 51(7): 856-861. (in Chinese)

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He Chunyang, Shi Peijun, Li Jingganget al., 2006b. Restoring urbanization process in China in the 1990s by using non-radiance calibrated DMSP/OLS nighttime light imagery and statistical data.Chinese Science Bulletin, 51(13): 1614-1620.

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Henderson J V, Storeygard A, Weil D N, 2012. Measuring economic growth from outer space.American Economic Review, 102(2): 994-1028.Abstract GDP growth is often measured poorly for countries and rarely measured at all for cities or subnational regions. We propose a readily available proxy: satellite data on lights at night. We develop a statistical framework that uses lights growth to augment existing income growth measures, under the assumption that measurement error in using observed light as an indicator of income is uncorrelated with measurement error in national income accounts. For countries with good national income accounts data, information on growth of lights is of marginal value in estimating the true growth rate of income, while for countries with the worst national income accounts, the optimal estimate of true income growth is a composite with roughly equal weights. Among poor-data countries, our new estimate of average annual growth differs by as much as 3 percentage points from official data. Lights data also allow for measurement of income growth in sub- and supranational regions. As an application, we examine growth in Sub Saharan African regions over the last 17 years. We find that real incomes in non-coastal areas have grown faster by 1/3 of an annual percentage point than coastal areas; non-malarial areas have grown faster than malarial ones by 1/3 to 2/3 annual percent points; and primate city regions have grown no faster than hinterland areas. Such applications point toward a research program in which "empirical growth" need no longer be synonymous with "national income accounts."

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25
Henderson M, Yeh E T, Gong Pet al., 2003. Validation of urban boundaries derived from global night-time satellite imagery.International Journal of Remote Sensing, 24(3): 595-609.Not Available

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Imhoff M L, Bounoua L, Defries Ret al., 2004. The consequences of urban land transformation on net primary productivity in the United States.Remote Sensing of Environment, 89(4): 434-443.We use data from two satellites and a terrestrial carbon model to quantify the impact of urbanization on the carbon cycle and food production in the US as a result of reduced net primary productivity (NPP). Our results show that urbanization is taking place on the most fertile lands and hence has a disproportionately large overall negative impact on NPP. Urban land transformation in the US has reduced the amount of carbon fixed through photosynthesis by 0.04 pg per year or 1.6% of the pre-urban input. The reduction is enough to offset the 1.8% gain made by the conversion of land to agricultural use, even though urbanization covers an area less than 3% of the land surface in the US and agricultural lands approach 29% of the total land area. At local and regional scales, urbanization increases NPP in resource-limited regions and through localized warming “urban heat” contributes to the extension of the growing season in cold regions. In terms of biologically available energy, the loss of NPP due to urbanization of agricultural lands alone is equivalent to the caloric requirement of 16.5 million people, or about 6% of the US population.

27
Imhoff M L, Tucker C J, Lawrence W Tet al., 2000. The use of multisource satellite and geospatial data to study the effect of urbanization on primary productivity in the United States. IEEE,Transactions on Geoscience and Remote Sensing, 38(6): 2549-2556.

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Kiran Chand T R, Badarinath K V S, Elvidge C Det al., 2009. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS nighttime satellite data.International Journal of Remote Sensing, 30(3): 647-661.Not Available

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Li Jialin, Xu Jiqin, Li Weifanget al., 2007. Spatio-temporal characteristics of urbanization area growth in the Yangtze River Delta.Journal of Geographical Sciences, 62(4): 437-447. (in Chinese)The Yangtze River Delta is one of the economically developed coastal areas. From the late 1970s, its urbanization process has been quickened greatly, which resulted in the number increase and the spatial expansion of urban areas. The Landsat MSS, TM/ETM satellite images, which were respectively acquired in 5 periods of 1979, 1990, 1995, 2000 and 2005, were used to extract urban land information and analyze urban growth data with the help of remote sensing and GIS softwares. We analyzed the spatio-temporal characteristics including urban growth speed, growth intensity, fractal dimension and urban growth pattern. Additionally, dynamics of urban expansion in the Yangtze River Delta were also analyzed. The results are drawn as follows: (1) From 1979 to 2005, the growth speed of urbanization area was accelerating obviously. The quantities of increasing area of urbanized land were 37.66 km2, 112.43 km2, 274.86 km2 and 421.73 km2 in the past four periods (1979-1990, 1990-1995, 1995-2000 and 2000-2005), respectively. Meanwhlie, the growth intensities of urbanized land enhanced gradually. From 1979 to 1990, the growth intensity was only 0.03, then reaching 0.10, 0.24 and 0.37 in the following three periods. (2) The spatial structure of urbanization area in the Yangtze River Delta was fractal. The fractal dimension and stability coefficient of urbanized land structure fluctuated to a certain extent. From 1979 to 2000, the fractal dimension of urbanized land structure decreased yearly. The shape of urbanized land tended to be regular. After 2000, the area increase of urbanized land on a large scale led to more complicated shape of urbanized land. The stability coefficient also had similar characteristics to that of fractal dimension. So the change of urbanized land in spatial structure was relating to the growth process of urbanized land. (3) The growth process of urban agglomeration in the Yangtze River Delta was from one pole and two belts to five poles and five belts. From 1979 to 1990, Shanghai was the only first-grade growth pole of urbanized land and Shanghai-Nanjing railway and Shanghai-Hangzhou railway were the two first-grade growth belts of urbanized land in the Yangtze River Delta. At the latest period (from 2000 to 2005), the first-grade growth poles included 5 cities, i.e., Shanghai, Nanjing, Hangzhou, Suzhou and Ningbo. Besides Shanghai-Nanjing railway and Shanghai-Hangzhou railway, Shanghai-Jingjiang railway, Hangzhou-Ningbo railway and the highway linking Nanjing to Gaochun also became growth belts of urbanized land in the Yangtze River Delta in that period.

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Li Xi, Xu Huimin, Chen Xiaolinget al., 2013. Potential of NPP-VIRS nighttime light imagery for modeling the regional economy of China.Remote Sensing, 5(6): 3057-3081.Historically, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R-2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

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Lo C P, 2001. Modeling the population of China using DMSP operational linescan system nighttime data.Photogrammetric Engineering and Remote Sensing, 67(9): 1037-1047.Radiance-calibrated DMSP-OLS nighttime lights data of China acquired between March 1996 and January-February 1997 were evaluated for their potential as a source of population data at the provincial, county, and city levels. The light clusters were classified into six categories of light intensity, and their areal extents were extracted from the image. Mean pixel values of light clusters corresponding to the settlements were also extracted. A light volume measure was developed to gauge the three-dimensional capacity of a settlement. A density of light cluster measure known as percent light area was also calculated for each spatial unit. Allometric growth models and linear regression models were developed to estimate the Chinese population and population densities at the three spatial levels using light area, light volume, pixel mean, and percent light area as independent variables. It was found that the DMSP-OLS nighttime data produced reasonably accurate estimates of non-agricultural (urban) population at both the county and city levels using the allometric growth model and the light area or light volume as input. Non-agricultural population density was best estimated using percent light area in a linear regression model at the county level. The total sums of the estimates for non-agricultural population and even population overall closely approximated the true values given by the Chinese statistics at all three spatial levels. It is concluded that the 1-km resolution radiance-calibrated DMSP-OLS nighttime lights image has the potential to provide population estimates of a country and shed light on its urban population from space.

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Lo C P, 2002. Urban indicators of China from radiance-calibrated digital DMSP-OLS nighttime image.Annals of the Association of American Geographers, 92(2): 225-240.A composite of cloud-free radiance-calibrated Defense Meteorological Satellite Program (DMSP)-Operational Linescan System (OLS) nighttime images of China acquired between March 1996 and January February 1997 in 30-arc-second grids in byte format is evaluated for its usefulness in extracting urban indicators data of China. With the aid of image-processing and Environmental Systems Research Institute ArcView GIS software with 3-D Analysis extension, zonal variations of radiance were extracted and three-dimensional models created using Triangulated Irregular Network (TIN) functionality for thirty-five cities. Two variables were extracted from the TIN model: surface area and volume. These were used separately as independent variables in the form of an allometric growth model to estimate the following urban indicators for individual Chinese cities: nonagricultural population, gross domestic product (GDP), built-up area, and electricity consumption. The estimates obtained were checked against data supplied by the Statistical Bureau of China for 1997, the same period during which the DMSP-OLS images were acquired. It was found that urban indicators of acceptable accuracy could be obtained, provided that cities designated as Special Economic Zones (SEZs) were excluded. Volume appeared to be slightly better than surface area in estimating these urban indicators. In all cases, total nonagricultural population for all the cities combined could be very accurately estimated, indicating the usefulness of the radiance-calibrated DMSP-OLS nighttime images to determine the level of urbanization in China. The three-dimensional TIN model portrays an illuminated urban area (IUA) dome, the shape of which is affected by the internal structure of the city. The core of the city invariably exhibits the highest level of light intensity in the DMSP-OLS nighttime image. However, only a very limited amount of land-use information could be extracted. The image was also found to be useful in detecting the special urban phenomenon in China known as the extended metropolitan region, in which rural and urban uses intermixed and small towns developed. In conclusion, despite the low spatial resolution, the radiance-calibrated DMSP-OLS nighttime images are capable of providing useful demographic and socioeconomic data for cities.

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Lu Dadao, 2007. Urbanization process and spatial sprawl in China.Urban Planning Forum, (4): 47-52. (in Chinese)

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Lv Ping, Zhou Tao, Zhang Zhengfenget al., 2008. Construction and application of land urbanization and corresponding measurement index system.China Land Science, 22(8): 24-28, 42. (in Chinese)The purpose of the paper is to construct the measurement index system of land urbanization by studying its process and features. Methods employed include theoretical and empirical analysis. The results indicate that the measurement index system of land urbanization shall contain the changes of the land use structure, land use benefit level, land use intensity, landscape and investment in land. The results of land urbanization level by the constructed index in towns of typical urban-rural areas in Beijing basically conform to actual conditions. It is concluded that the land urbanization level and corresponding measurement are significant in controlling the process of land urbanization.

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Ma Ting, Zhou Chenghu, Pei Taoet al., 2012. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China’s cities.Remote Sensing of Environment, 124: 99-107.Urbanization process involving increased population size, spatially extended land cover and intensified economic activity plays a substantial role in anthropogenic environment changes. Remotely sensed nighttime lights datasets derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) provide a consistent measure for characterizing trends in urban sprawl over time (Sutton, 2003). The utility of DMSP/OLS imagery for monitoring dynamics in human settlement and economic activity at regional to global scales has been widely verified in previous studies through statistical correlations between nighttime light brightness and demographic and economic variables ( and ). The quantitative relationship between long-term nighttime light signals and urbanization variables, required for extensive application of DMSP/OLS data for estimating and projecting the trajectory of urban development, however, are not well addressed for individual cities at a local scale. We here present analysis results concerning quantitative responses of stable nighttime lights derived from time series of DMSP/OLS imagery to changes in urbanization variables during 1994-2009 for more than 200 prefectural-level cities and municipalities in China. To identify the best-fitting model for nighttime lights-based measurement of urbanization processes with different development patterns, we comparatively use three regression models: linear, power-law and exponential functions to quantify the long-term relationships between nighttime weighted light area and four urbanization variables: population, gross domestic product (GDP), built-up area and electric power consumption. Our results suggest that nighttime light brightness could be an explanatory indicator for estimating urbanization dynamics at the city level. Various quantitative relationships between urban nighttime lights and urbanization variables may indicate diverse responses of DMSP/OLS nighttime light signals to anthropogenic dynamics in urbanization process in terms of demographic and economic variables. At the city level, growth in weighted lit area may take either a linear, concave (exponential) or convex (power law) form responsive to expanding human population and economic activities during urbanization. Therefore, in practice, quantitative models for using DMSP/OLS data to estimate urbanization dynamics should vary with different patterns of urban development, particularly for cities experiencing rapid urban growth at a local scale.

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National Bureau of Statistics, 1995. China City Statistical Yearbook-1993. Beijing: China Statistics Press, 66-79. (in Chinese)

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National Bureau of Statistics, 2000. China City Statistical Yearbook-1999. Beijing: China Statistics Press, 101-108. (in Chinese)

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National Bureau of Statistics, 2008. China City Statistical Yearbook-2007. Beijing: China Statistics Press, 109-116. (in Chinese)

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National Bureau of Statistics, 2014. China City Statistical Yearbook-2013. Beijing: China Statistics Press, 83-89. (in Chinese)

40
Nishii R, Tanaka S, 1999. Accuracy and inaccuracy assessments in land-cover classification.IEEE Transactions on Geoscience and Remote Sensing, 37(1): 491-497.Not Available

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Ou Xiangjun, Zhen Feng, Qin Yongdonget al., 2008. Study on compression level and ideal impetus of regional urbanization: The case of Jiangsu Province.Geographical Research, 27(5): 993-1002. (in Chinese)Through the analysis of related literatures at home and abroad about the measurement of urbanization level and its dynamic changes,we found that China's urbanization has entered a rapid development stage and the motive forces for the development of urbanization have changed enormously.Therefore,the scientific measurement of urbanization development level and quantitative comparative analysis of its ideal powers will be particularly important to promote the level and quantity of regional urbanization.Based on the urbanized connotation,the paper constructs the appraisal index system of the synthesis level of the regional urbanization embarking population urbanization,economic urbanization,life-style urbanization and regional landscape urbanization.With the method of entropy,the generalized analyses to the evolution of the urbanization level of Jiangsu Province from 1991 to 2005 are carried out.The result shows that the urbanization of Jiangsu Province has been enhanced continuously.At the same time,the urbanization mainly represents the fast development of the economic urbanization and the regional landscape urbanization.But the population urbanization is becoming weak to the overall contribution of the regional urbanization and lagging behind other urbanizations.Economic urbanization,life-style urbanization and regional landscape urbanization have improved constantly.Meanwhile,they are turning harmonious gradually.On this basis,the multiple linear regression models are linked to the comparative analysis of the main urbanized impetus of Jiangsu Province from 1991 to 2005.Then the result is that the market forces,the intrinsic forces,the exterior forces and the administrative forces are the main powers of the urbanization development of Jiangsu Province.And the market forces and administrative forces are the ideal powers that promote the urbanization and the economic development of Jiangsu Province.

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Ou Xiangjun, Zhen Feng, Ye Leiet al., 2012. Temporal and spatial analysis of regional differences of urbanization quality in Jiangsu Province.Human Geography, (5): 76-82. (in Chinese)According to the meaning of urbanization and the aim of promoting urbanization,from population urbanization,economic urbanization,residential living urbanization,landscape environment urbanization and infrastructure urbanization,we build the evaluative indices system of regional urbanization quality and use entropy method to estimate the urbanization quality of the counties and cities of Jiangsu Province from 2001 to 2009.And then taking these as variables,we also use coefficient of variation to analyze the process and pattern of urbanization quality of the counties and cities of Jiangsu Province.The results are as follows: firstly,since the beginning of the new century,the differences of regional urbanization quality in Jiangsu Province have shown the trend of expansion;secondly,the urbanization quality of cities and counties in Jiangsu Province improves gradually,but their spatial polarization becomes increasingly obvious at the same time.Prefecture-level city downtown areas give priority to downward convergence,central and northern Jiangsu counties give priority to upward convergence;Lastly we should accelerate counties economic development of the northern and central areas in Jiangsu Province(especially in coastal areas),constantly improve their infrastructure construction and people's living standards,establish the regional cooperation organization which is promoted by the government in order to break through the region as a unified regional economic system.With those measures have been finished can we accelerate the upgrading of the urbanization quality development in Jiangsu Province,narrow the disparities in regional development and promote the strategic transformation of urban development initiatives.

43
Pan Aimin, Liu Youjin, 2014. The degree of imbalance between population urbanization and land urbanization of Xiangjiang River Basin.Economic Geography, 34(5): 63-68. (in Chinese)

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Shi Kaifang, Huang Chang, Yu Bailanget al., 2014. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas.Remote Sensing Letters, 5(4): 358-366.ABSTRACT The first global night-time light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day-night band carried by the Suomi National Polar-orbiting Partnership (NPP) satellite were released recently. So far, few studies have been conducted to assess the ability of NPP-VIIRS night-time light composite data to extract built-up urban areas. This letter aims to evaluate the potential of this new-generation night-time light data for extracting urban areas and compares the results with Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data through a case study of 12 cities in China. The built-up urban areas of 12 cities are extracted from NPP-VIIRS and DMSP-OLS data by using statistical data from government as reference. The urban areas classified from Landsat 8 data are used as ground truth to evaluate the spatial accuracy. The results show the built-up urban areas extracted from NPP-VIIRS data have higher spatial accuracies than those from DMSP-OLS data for all the 12 cities. These improvements are due to the relatively high spatial resolution and wide radiometric detection range of NPP-VIIRS data. This study reveals that NPP-VIIRS night-time light composite data would provide a powerful tool for urban built-up area extraction at national or regional scale.

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Su Yongxian, Chen Xiuzhi, Wang Chongyanget al., 2015. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: A case study in the Pearl River Delta, southern China.GIScience and Remote Sensing, 52(2): 218-238.

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Tuttle B, 2007. Active forest fire monitoring in Uttaranchal State, India using multi temporal DMSP-OLS and MODIS data.International Journal of Remote Sensing, 28(10): 2123-2132.This paper gives an account of day-night active forest fire monitoring conducted over the sub-tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence Meteorological Satellite Program - Operational Line Scan system (DMSP-OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May-June 2005 and daily datasets of DMSP-OLS (night-time) and selected cloud-free MODIS (daytime) datasets were used in mapping active fire locations. DMSP-OLS collects data in visible (0.5 to 0.9 m) and thermal (10.5 to 12.5 m) bands and detects dim sources of lighting on the earth's surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP-OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night-time rather than daytime. Validation of satellite-derived fires with ground data showed a high degree of spatial correlation.

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Yang Yang, He Chunyang, Zhang Qiaofenget al., 2013. Timely and accurate national-scale mapping of urban land in China using Defense Meteorological Satellite Program’s Operational Linescan System nighttime stable light data.Journal of Applied Remote Sensing, 7: 073535.

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Yao Shimou, Lu Dadao, Chen Zhenguanget al., 2012. Thinking about urbanization of conform to the grim conditions of China.Economic Geography, 32(5): 1-6. (in Chinese)

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Yao Shimou, Lu Dadao, Wang Conget al., 2011. Urbanization in China needs comprehensive scientific thinking: Exploration of the urbanization mode adapted to the special situation of China.Geographical Research, 30(11): 1947-1955. (in Chinese)Urbanization is an important comprehensive problem of social and economic development in contemporary China,involving fundamental issue of how to realize the goal of new and modern harmonious social development according to coordinated development of national economy,and sustainable development problem of rational utilization and long-term protection of resources and environment. After more than three decades of reform and opening up,socio-economic development has maintained a new situation of the rapid advance. Brilliant achievements in comprehensive national power,industrialization,urbanization and urban-rural integration have been obtained in China. Urbanization is not only the concept of historical development,but also the historical process of the objective laws of industrialization and social development. With the unprecedented advance of urbanization in the motherland,urbanization comprehensively promotes the tremendous economic and social development,and largely improves living standards and housing conditions of urban and rural residents. However,in recent 10 years(1996-2009) ,urbanization development has derogated from the principle of gradual and orderly progress beyond the normal track of urbanization development,which is called "the rapid urbanization" in the process. Recently,many scholars believes that urbanization in China shows "Great Leap Forward" and "Rash Advance". The main manifestation is the empty high speed of urbanization development,the excessive consumption of water and land resources,large-scale land occupation and land damage in the process of traffic construction in rural areas,medium-sized and large cities,and serious damage and pollution to natural eco-environment. Based on the above-mentioned situations,we suppose that urbanization in China should follow the requirements of scientific development view and think in a scientific perspective with the idea of seeking truth from facts and considering the special situation of China after our research and comprehensive analysis over the past years.

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Zhou Chenghu, Cheng Weiming, Qian Jinkai, 2009. Digital Geomorphological Interpretation and Mapping from Remote Sensing. Beijing: Science Press, 29-30. (in Chinese)

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Zhu Fengkai, Zhang Fengrong, Li Canet al., 2014. Coordination and regional difference of urban land expansion and demographic urbanization in China during 1993-2008.Progress in Geography, 33(5): 647-656. (in Chinese)Much global attention has been paid to China's stable and rapid economic growth and urbanization in recent 30 years since the economic reform and opening-up from the end of the 1970s. At present, China is experiencing rapid development and important transition of urbanization. However, under the background of the urbanrural dual system for population registration and social welfare, China's urbanization process is different from the western countries. It is regarded as a local government-oriented urbanization. Local governments' excessive dependence on land finance has led to a continuous rapid expansion of urban built-up areas. Meanwhile, the household registration system and the high living cost in cities hindered farmers to become real urban residents. Urban land expansion and demographic urbanization became increasingly uncoordinated. Understanding the coordination and regional differences of these two processes will be of great significance to China's new urbanization initiative, which promotes healthy, scientific and sustainable urban transition. In this paper, we focus on the widely existing phenomenon that demographic urbanization of rural population lags behind urban land expansion. A coordination model, Theil index and GIS were used to re-examine the process of urban land expansion and population absorption since the early 1990s, investigate the coordination of human-land relationship in the process of urbanization, and analyze the change of non-agricultural population density in the process of urbanization and its regional differences. We hope the conclusion provides certain references for regulating the development of regional urban land and population. The spatio-temporal evolution of the coordination of urban land expansion and demographic urbanization was analyzed based on the data from China City Statistical Yearbooks. By using the Theil index, the change and regional differences of non-agricultural population density of the builtup areas was also discussed. The results indicate that: (1) Dominated by the strong incentives of land finance, the speed of urban expansion of all provinces in China was generally very fast. The disharmony of urban land expansion and population urbanization extended from the eastern part to the rest of the country, which can be considered as a result of similar urban planning methods and urban development patterns; (2) The non-agricultural population density of the built-up areas generally declined due to this disharmony. Differences between and within regions have shown a tendency of convergence over time. The difference within region was the dominant cause of the interprovincial differences. The huge expansion of the urban built-up areas in direct-controlled municipalities and some other economically developed regions reduced the regional difference of the non-agricultural population density after 2004; (3) The development paradigm that local governments confiscates land without fully integrating the landless farmers into the urban welfare system and providing them with sustainable livelihood choices in urban areas must be abandoned. The new urbanization initiative should make urban housing, social security and medical care available for migrant workers. Local governments should treat them as regular urban residents. Reasonable allocation of construction land, that matches the non-agricultural population, is an effective way to achieve healthy and coordinated urbanization in the future. It is imperative for the government to develop strategies toward a coordinated urbanization in China.

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Zhuo Li, Chen Jin, Shi Peijunet al., 2005. Modeling population density of China in 1998 based on DMSP/OLS nighttime light image.Journal of Geographical Sciences, 60(2): 266-276. (in Chinese)Spatial distribution of population density is crucial for analyzing the relationship among economic growth, environment protection and resource utilization. In this study, population density of China in 1998 at 1-km resolution grids was simulated by integrating DMSP/OLS non-radiance calibrated nighttime light image, SPOT/VEGETATION 10-day maximum NDVI data, population census data and vector data of county boundary. Not only the population density in light patches but also that out of them was estimated in four types of areas. For each area, in light patches, the model for population density estimation was developed based on the significant correlation between light intensity and population, and in "dark area", the models for population density estimation were developed based on Coulomb's law and field superposing theory. Compared with the existed methods for spatializing population density, our method is simpler and more cost saving. The result of the study is consistent with those of other researches on the whole, but the spatial difference is more distinct and the information is richer. The maximum population density simulated is 41096 persons/km2 and average population density at inhabitable area of China is 189 persons/km2. It indicates that the 1-km resolution non-radiance calibrated DMSP/OLS nighttime lights image has the potential to provide population density estimation at 1-km grids.

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