Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A/B and spatial difference analysis

Duan, Pan; ZHANG, Fei; LIU, Changjiang

doi:10.11834/jrs.20210174

Cited by 7 publications

(3 citation statements)

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“…It can effectively eliminate the influence of dry land, bare rock, and bare soil on IS extraction so that water-impervious and waterpermeable surfaces can be more effectively separated and extracted [44]. Because part of the study area contains water, when ENDISI was used to extract water-impervious surfaces in the study area, the Modified Normalized Difference Water Index (MNDWI) was used to mask water [45]. ENDISI and MDNWI can be calculated by Equations ( 1) and (2):…”

Section: Urban Is Extraction Methodsmentioning

confidence: 99%

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

Feng,

Ma,

Jia

et al. 2024

IJGI

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In recent years, China’s economy has experienced rapid development, and its cities have undergone rapid expansion; however, the development of cities in the northwest region has been relatively slow due to various geographical and economic constraints. Studying the urban expansion in these regions is of significant importance for regional planning and development. This study selected the provincial capitals of five underdeveloped provinces in northwestern China as the research sample and used Landsat TM/OLI remote-sensing imagery as the primary data, supplemented by Digital Elevation Model (DEM), meteorological, and socio-economic data, the study extracted urban impervious surfaces using the ENDISI and MNDWI indices. It analyzed the spatial and temporal characteristics of urban impervious surfaces from 1990 to 2020 using indicators such as urban expansion intensity, compactness and fractal dimension, centroid migration, and standard deviation ellipse. Furthermore, the study quantified the influencing factors using Geodetectors. The findings reveal the following: (1) From 1990 to 2020, impervious surfaces in the five cities continued to expand, with Xi’an experiencing the largest expansion area at 549.94 km2 and Xining the smallest at only 132.83 km2, with an expansion intensity of merely 2.99%. However, significant disparities existed in expansion intensity and area across different periods. (2) Overall, the compactness of the cities decreased by 47.6% while the overall fractal dimension increased by 2.85%, indicating a trend towards more dispersed and complex urban forms. (3) Expansion directions varied among the cities, with Xi’an and Urumqi expanding towards the northwest, Lanzhou towards the north, Yinchuan primarily towards the east, and Xining mainly towards the west. (4) Economic, demographic, and investment factors were identified as the primary influencers of urban expansion, exhibiting changes over different periods. Analyzing the similarities and differences in city development can offer valuable insights into urban construction and sustainable development in underdeveloped areas.

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Section: Urban Is Extraction Methodsmentioning

confidence: 99%

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

Feng,

Ma,

Jia

et al. 2024

IJGI

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“…With the continuous progress of satellite technology and the increasing demand for production, high-resolution land use data emerge in endlessly. Massive real-time land use data are widely used in urban land use/cover classification [1,2] , forest vegetation monitoring [3,4] , urban impervious surface detection [5] and urban pollution detection [6] . Land use data is the basis of land use simulation, which can provide necessary information support for land use simulation.…”

Section: Introductionmentioning

confidence: 99%

Land Use Simulation of Shanghai Based on Multi-source Data Integration

Bai,

Li,

2024

Atlantis Highlights in Engineering

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With the continuous improvement of big data mining technology, massive and multi-source land use spatio-temporal big data began to emerge. Big data thinking plays an increasingly important role in ecological monitoring, smart city construction, public safety and support for major decisions. Taking Shanghai as an example, this paper integrates ESA CCI, GlobeLand 30 and MODIS data by voting method, and inputs MCE-CA-Markov model to obtain more accurate prediction results. This study provides a new simulation thinking, which can provide ideas for the prediction of Shanghai and other similar cities.

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“…As an important indicator of urban development process, impervious surfaces have attracted the attention of many scholars at home and abroad, and many studies have shown that a change of the total impervious surface area can effectively reflect the development and expansion of cities, and the spatial distribution characteristics of impervious surfaces are consistent with the built-up areas of cities, which can better reflect the level of urbanization [ [12] , [13] , [14] , [15] , [16] ]. For example, Yang et al [ 17 ] in 2005 found that the spatial and temporal trends of their imperviousness index were consistent with the urban land use/cover changing trends obtained through image interpretation, and proposed that the urban imperviousness index could quickly and objectively assess urban spatial change characteristics.…”

Section: Introductionmentioning

confidence: 99%

Analysis of changes in the spatiotemporal characteristics of impervious surfaces and their influencing factors in the Central Plains Urban Agglomeration of China from 2000 to 2018

Zhao,

Zhang,

Wang

et al. 2023

Heliyon

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Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A/B and spatial difference analysis

Cited by 7 publications

References 0 publications

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

Land Use Simulation of Shanghai Based on Multi-source Data Integration

Analysis of changes in the spatiotemporal characteristics of impervious surfaces and their influencing factors in the Central Plains Urban Agglomeration of China from 2000 to 2018

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