Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China

Abstract

China's resource-based cities have made tremendous contributions to national and local economic growth and urban development over the last seven decades. Recently, such cities have been in transition from resource-centered development towards human-oriented urbanization to meet the requirements of long-term sustainability for the natural environment and human society. A good understanding of urban expansion and evolution as a consequence of urbanization has important implications for future urban and regional planning. Using a series of remote sensing (RS) images and geographical information system (GIS)-based spatial analyses, this research explores how a typical resource-based mining city, Datong, has expanded and evolved over the last two decades (2000–2018), with a reflection on the role of urban planning and development policies in driving the spatial transformation of Datong. The RS images were provided and processed by the Google Earth Engine (GEE) platform. Spatial cluster analysis approaches were employed to examine the spatial patterns of urban expansion. The results indicate that the area of urban construction land increased by 132.6% during the study period, mainly along with the Chengqu District, the Mining Area, and in the southeast of the Nanjiao District, where most new towns are located. Reflection on the factors that influence urban expansion shows that terrain, urban planning policies, and social economy are driving Datong’s urban development.

Construction of an Industrial Structure Analysis and Evaluation Model for Oil and Gas Resource-Based Cities Based on Deep Learning Model and Cluster Analysis

“…On the other hand, the tertiary industry should be vigorously developed. e tertiary sector is a complex and mixed industry group, and it is important to scientifically determine the priorities and development of the tertiary sector, and to continue to exploit its advantages in order to build Daqing into a distribution and service centre in western Heilongjiang [10,11].…”

Overview of the Special Issue on Applications of Remote Sensing Imagery for Urban Areas

“…This Special Issue (SI) aims to invite recent advances in the applications of RS imagery for urban areas, and 17 papers in total were selected and published. Among them, 12 papers emphasize the novel urban application algorithms based on RS imageries, such as urban attribute mapping, building extraction, classification, change detection, and so on [1][2][3][4][5][6][7][8][9][10][11][12], and 5 papers directly employed RS imageries to analyze the environmental variations and urban expansion in typical cities, such as urban heat island, air pollution, lightning, and so on [13][14][15][16][17].…”

“…For the remaining five papers analyzing the urban expansion and urban environmental changes [13][14][15][16][17], Liu et al [13] used time-series Landsat imagery to map and quantify the spatiotemporal dynamics of urban expansion from 1990 to 2020 in Xiaonan District in Hubei Province, China. The built-up area and urban land are extracted in the RS images using different classification methods.…”

“…A slight decrease of CG lightning is found under most high dust AOD conditions. Xue et al [16] used a series of RS images to explore how a typical resource-based mining city, Datong, has expanded and evolved over the last two decades (2000-2018), with a reflection on the role of urban planning and development policies in driving the city spatial transformation. The results indicate that the area of urban construction land has increased by 132.6% during the study period.…”

“…Novel models for RS classification, target and change detection are also included, which can significantly support further urban applications [5][6][7][8][9]. Different analyses on urban expansion, urban heat islands, air pollution, and lightning can advance our understanding of the interactions between urban development and the regional environment [13][14][15][16][17]. Almost all of these researches use the satellite RS imageries from optical, thermal, or SAR sensors.…”