On Physical Urban Boundaries, Urban Sprawl, and Compactness Measurement: A Case Study of the Wen-Tai Region, China

Dai, Xiaoling; Jin, Jiafeng; Chen, Qianhu; Fang, Xin

doi:10.3390/land11101637

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…However, based on the relevant studies, cities with higher ULUE levels are generally concentrated in economically developed areas with a reasonable industrial structure and relatively concentrated population, while smaller towns and remote areas tend to have lower ULUE levels. Similarly [64][65][66], UC also exhibits similar patterns in the aforementioned cities [67][68][69].…”

Section: Changes In Urban Compactnessmentioning

confidence: 61%

Coupling Coordination and Spatiotemporal Analysis of Urban Compactness and Land-Use Efficiency in Resource-Based Areas: A Case Study of Shanxi Province, China

Chen,

Chang,

et al. 2023

Land

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Rapid urbanization and industrialization have had a range of effects on urban form and land use, especially in resource-based cities. Previous research has predominantly focused on the analysis of urban compactness or land-use efficiency individually, overlooking their interplay. In this study, we propose a coupled and coordinated research framework for urban compactness (UC) and urban land-use efficiency (ULUE). We analyze the UC and ULUE, as well as the degree and types of coupling and coordination (CCD) between them, in 119 counties and districts rich in coal resources within Shanxi Province. The findings reveal a declining trend in UC in Shanxi Province over time, with coal-resource-based cities exhibiting lower compactness than non-coal-resource-based cities. ULUE has shown stable development during the study period. The CCD between UC and ULUE exhibit spatial and urban typological heterogeneity. Further analysis demonstrates that the CCD types mainly manifest as the lagging development of UC. This research provides a foundation for achieving harmonious development between urban spatial structure and land-use structure and offers new perspectives for the sustainable development of resource-based urban regions.

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Section: Changes In Urban Compactnessmentioning

confidence: 61%

Coupling Coordination and Spatiotemporal Analysis of Urban Compactness and Land-Use Efficiency in Resource-Based Areas: A Case Study of Shanxi Province, China

Chen,

Chang,

et al. 2023

Land

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“…Currently, the primary methods for extracting urban boundaries from remote-sensing images include threshold, mutation detection, and clustering methods. The threshold methods are simple, fast, and have good results at local scales [10][11][12]-for example, the Normalized Difference Built-Up Index (NDBI) [13] and the Index-based Built-Up Index (IBI) [14]. However, determining this threshold is both subjective and empirical.…”

Section: Introductionmentioning

confidence: 99%

A New Technique for Urban and Rural Settlement Boundary Extraction Based on Spectral–Topographic–Radar Polarization Features and Its Application in Xining, China

Li,

Zhou,

Zhou

et al. 2024

Remote Sensing

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Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

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“…The PUB extraction algorithms for remote sensing images are mainly divided into three categories, including the threshold method, the clustering method, and the mutation detection method. The threshold method is the most widely used method in local-scale PUB extraction and has a good effect on local-scale applications [15][16][17]. However, at the national scale, this rule-based threshold selection is difficult to apply on a large scale because it is heavily influenced by artificial parameters.…”

Section: Introductionmentioning

confidence: 99%

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

et al. 2023

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Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

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On Physical Urban Boundaries, Urban Sprawl, and Compactness Measurement: A Case Study of the Wen-Tai Region, China

Cited by 7 publications

References 52 publications

Coupling Coordination and Spatiotemporal Analysis of Urban Compactness and Land-Use Efficiency in Resource-Based Areas: A Case Study of Shanxi Province, China

Coupling Coordination and Spatiotemporal Analysis of Urban Compactness and Land-Use Efficiency in Resource-Based Areas: A Case Study of Shanxi Province, China

A New Technique for Urban and Rural Settlement Boundary Extraction Based on Spectral–Topographic–Radar Polarization Features and Its Application in Xining, China

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

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