Analysis of Temporal and Spatial Characteristics of Urban Expansion in Xiaonan District from 1990 to 2020 Using Time Series Landsat Imagery

Abstract

With the rapid development in the global economy and technology, urbanization has accelerated. It is important to characterize the urban expansion and determine its driving force. In this study, we used the Xiaonan District in Hubei Province, China, as an example to map and quantify the spatiotemporal dynamics of urban expansion from the two perspectives of built-up area and urban land in 1990–2020 by using remote sensing images. The location of rivers was found to be a primary limiting factor for spatial patterns and expansion of the built-up area. The transfer of the city center and the main direction of expansion generally corresponded well to the topography, policies, and development strategies. The built-up area expanded faster than the urban population in 1995–2020, which caused a waste in land resources. The results showed that the urban expansion first decreased and then increased during the research period. The increase in the proportion of the secondary industry was the main driving force of the urban expansion. Based on the characteristics of urban expansion in the past three decades, we conclude that the urban land of Xiaonan District will expand quickly in the future and will occupy vast agricultural land. The government must deploy control measures to balance the benefits and costs of urban expansion.

Deep Semantic Segmentation for Rapid Extraction and Spatial-Temporal Expansion Variation Analysis of China’s Urban Built-Up Areas

“…The direction of the country's urban expansion determines the development direction of the entire country to a certain extent and provides a reference value for the country's macromanagement work. In this section, we used the geometric quadrant orientation method (Liu et al, 2021) to compare and analyze the expansion differences of urban built-up areas in different spatial orientations in each period, and different spatial orientations reflected the spatial characteristics of urban expansion, so the spatial form of China's urban built-up areas expansion can be described.…”

“…Third, the method combined nighttime light data and remote sensing images to extract urban built-up areas, which is commonly used in smallscale extraction (Ma, 2018;He et al, 2020;Li et al, 2020), for example, combining the traditional remote sensing image Landsat with nighttime light data. However, the Landsat image has many bands, and extracting urban built-up areas on a large scale requires a large amount of data, resulting in a more complex and time-consuming experimental setup for extraction (Liu et al, 2021;Mithun et al, 2021). Lu et al (2008) found that combining MODIS (moderate-resolution imaging spectroradiometer) and NDVI (Normalized Difference Vegetation Index) data with DMSP/OLS nighttime light data can improve the accuracy of urban built-up area extraction.…”

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.…”

“…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.…”