Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Yu, Bailang; Shu, Song; Liu, Hongxing; Song, Wei; Wu, Jianping; Wang, Lei; Chen, Zuoqi

doi:10.1080/13658816.2014.922186

Cited by 181 publications

(72 citation statements)

References 44 publications

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“…This process is mainly concentrated on developing countries [4]. In this process, cities become much more linked with the surrounding towns or villages by the movement of people, goods, or information [5,6]. Hence, they are known as metropolitans in the literature [7], which functions as a single unit by representing intensive interactions and mixtures in socioeconomic and demographic aspects [4].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

2017

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Abstract:Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work.

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Section: Introductionmentioning

confidence: 99%

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

2017

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“…The automatic classification method using high-resolution imagery and topographic data is a promising approach for general landscape classification [43][44][45], mountain hazard monitoring [46][47][48], and geomorphology mapping [49,50]; it is also advisable in agricultural terrace extraction [17]. In the Loess Plateau, terraces have diverse vegetation cover and are located in an undulating and broken landform environment.…”

Section: Rationality Of the Proposed Methodsmentioning

confidence: 99%

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Zhao

Fang

Ding

et al. 2017

IJGI

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Abstract:Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of "vegetation recovery". This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs) and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA). A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN), accumulative curvature slope (AC), and slope of slope (SOS) were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM) analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART) analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and spectrum characteristics, demonstrating huge potential in hilly-gully loess region with similarly complex terrain and diverse vegetation covers.

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“…For example, Lo [7] visually identified 10 most notable urban spatial clusters in China, proving the potential of NTL data to detect urban spatial clusters besides extracting urban built-up areas. Yu et al [8] proposed a new method based on minimum spanning tree and object-oriented method, applying it to the extraction of urban agglomerations in China. Yi et al [25] used NTL data to study the urbanization in northeastern China from 1992 to 2010, analyzing the urbanization process of 34 prefecture-level cities by a unit circle model.…”

Section: Introductionmentioning

confidence: 99%

“…Urban aggregation has gradually become the key area for implementing development strategies in regional urbanization [6]. Studies on urban agglomeration have also been a heated topic among scholars in both natural and social sciences [7,8]. In the early period of urbanization, like China in the 1980s, the pace of development of cities inside an urban agglomeration was relatively slow and in a balanced pattern.…”

Section: Introductionmentioning

confidence: 99%

Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data

Peng

Liu

et al. 2017

Remote Sensing

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Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the limitation of statistical units, while the application of remote sensing data, such as nighttime light (NTL) data, provides an effective solution. In this study, based on the DMSP/OLS NTL data, the urbanization type of all towns in the Beijing-Tianjin-Hebei urban agglomeration was analyzed from the aspects of development level and speed. Meanwhile, spatial cluster analysis of development level by local Moran's I was used to explore spatial inequality, and the trend was discussed by comparing the development characteristics on both sides of the transition line of different development levels (inequality boundary). The results showed that the development level of the whole region increased dramatically as the mean DN value increased by 65.99%, and 83.72% of the towns showed a positive development during 2000-2012. The spatial distribution of urbanization types showed that Beijing and Tianjin were at a high urbanization level with rapid speed of development, with the southern region having a medium development level and the northwestern region lagging behind. The spatial cluster analysis also revealed a gradually intensifying trend of inequality as the number of towns with balanced development reduced by 319 during 2000-2012, while the towns in the high-high areas increased by 99 and those in the low-low areas increased by 229. Moreover, the development speed inside the inequality boundary was obviously higher than that outside, indicating an increasingly serious situation for spatial inequality of urban development in the whole region.

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Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Cited by 181 publications

References 44 publications

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data

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