Characterizing Factors Associated with Built-Up Land Expansion in Urban and Non-Urban Areas from a Morphological Perspective

Zhang, Zhonghao; Xiao, Rui; Yu, Wei; Liu, Yue; Lin, Meng; Wang, Meng

doi:10.3390/su9081411

Cited by 13 publications

(12 citation statements)

References 52 publications

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“…Introducing random factors into the fractal generation process will form a random fractals. Random fractals can better simulate the land-cover spatial structure [45]. In this study, the box-counting method is used to measure the fractal dimension of land-cover spatial structure.…”

Section: Fractal Measurement Methods Of Urban Land-cover Spatial Structurementioning

confidence: 99%

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

2021

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Understanding the urban land-cover spatial patterns is of particular significance for sustainable development planning. Due to the nonlinear characteristics related to the spatial pattern for land cover, it is essential to provide a new analysis method to analyze them across remote sensing imagery. This paper is devoted to exploring the fractals and fractal dimension properties of land-cover spatial patterns in Shenzhen city, China. Land-cover information was extracted using a supervised classification method with ArcGIS technology from cloud-free Landsat TM/ETM+/OLI imagery, covering 1988–2015. The box-counting method and the least squares regression method are combined to estimate fractal dimensions of the land-cover spatial pattern. The information entropy was used to verify our fractal dimension results. The results show the fractal dimension changes for each land cover type from 1988 to 2015: (1) the land-cover spatial form of Shenzhen city has a clear fractal structure, but fractal dimension values vary in different land cover types; (2) the fractal dimension of build-up land increases and reaches a stable value, while grassland and cultivated land decrease; The fractal structure of grassland and bare land showed a bifractals trend increasing year by year; (3) the information entropy dimension growth is approaching its maximum capacity before 2011. We integrated the information entropy index and fractal dimension to analyze the complexity in land-cover spatial evolution from space-filling, space balance, and space complexity. It can be concluded that driven by policies, the land-cover spatial form in Shenzhen experienced a process from a hierarchical spatial structure with a low evolution intensity to a higher evolution intensity with multiscale differential development. The fractal dimension has been becoming better through self-organization, and its land resources are reaching the growth limits.

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Section: Fractal Measurement Methods Of Urban Land-cover Spatial Structurementioning

confidence: 99%

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

2021

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“…Land use in Yogyakarta City is dominated by built-up area (94.19%), open field (1.88%), water body (0.08%), and vegetation (3.86%%) (Figure 2 and Figure 4). Built-up land has a complex relationship with urban development because it affects economic activity, social life and is physically vulnerable [19,20]. This study did not look specifically at the characteristics of urban buildings affecting physical vulnerability.…”

Section: Resultsmentioning

confidence: 99%

Spatial analysis of the urban physical vulnerability using remote sensing and geographic information systems (case study: Yogyakarta City)

Arif

Wardhana

Martiana

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Vulnerability mapping in urban planning is very important to reduce damage caused and loss of life due to disasters. This paper discussed the mapping of urban physical vulnerability using remote sensing, and geographic information systems (GIS) approaches. Physical vulnerability parameters used were building density, distribution of public facilities, slope, and land use. Landsat 8 OLI satellite imagery used remote sensing data to obtain building density information using the Normalised Difference Built Index (NDBI) and land use interpretation approaches. With the use of GIS, the spatial distribution of urban vulnerabilities could be known through the overlay of each indicator. The results of the analysis obtained that the physical vulnerability classes of Yogyakarta City were very high (26%), high (29%), moderate (29%), low (16%), and very low (0%). Some areas categorized as vulnerable to very vulnerable are areas with high building density, namely the Sub-districts of Danurejan, Kraton, Gedongtengen, Jetis, Ngampilan, Pakualaman, Mantrijeron, parts of Gondomanan and Tegalrejo.

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“…Finally, we only examined urban expansion general characteristics of the YRDUA based on four indices. In future work, more geographic indices, e.g., fractal dimensions of urban built-up areas [57] and the urban expansion intensity index [58], should be used to further explore urban expansion characteristics of the YRDUA. Moreover, landscape pattern indices for land cover data derived from Landsat imagery or high spatial resolution images and ecosystem service values can be used to explore changes in landscape patterns and ecosystem service values resulting from land use changes [59].…”

Section: Discussionmentioning

confidence: 99%

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

Zhang

Sun

et al. 2018

IJGI

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Abstract:Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light from the Earth's surface at night without moonlight. It generates effective time series data for mapping the dynamics of urban expansion. As a major urban agglomeration in the world, the Yangtze River Delta Urban Agglomeration (YRDUA) is an important intersection zone of both the "Belt and Road Initiative" and the "Yangtze River Economic Belt" in China. Therefore, this paper analyses urban expansion characteristics of the YRDUA for 1993-2012 from urban extents extracted from the DMSP/OLS for 1993, 1997, 2002, 2007, and 2012. First, calibration procedures are applied to DMSP/OLS data, including intercalibration, intra-annual composition, and inter-annual series correction procedures. Spatial extents are then extracted from the corrected DMSP/OLS data, and a threshold is determined via the spatial comparison method. Finally, three models are used to explore urban expansion characteristics of the YRDUA from expansion rates, expansion spatial patterns, and expansion evaluations. The results show that the urban expansion of the YRDUA occurred at an increasing rate from 1993-2007 and then declined after 2007 with the onset of the global financial crisis. The Suxichang and Ningbo metropolitan circles were seriously affected by the financial crisis, while the Hefei metropolitan circle was not. The urban expansion of the YRDUA moved from the northeast to the southwest over the 20-year period. Urban expansion involved internal infilling over the first 15 years and then evolved into external sprawl and suburbanization after 2007.

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Characterizing Factors Associated with Built-Up Land Expansion in Urban and Non-Urban Areas from a Morphological Perspective

Cited by 13 publications

References 52 publications

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

Spatial analysis of the urban physical vulnerability using remote sensing and geographic information systems (case study: Yogyakarta City)

Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012

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