Analysis of the heterogeneity of urban expansion landscape patterns and driving factors based on a combined Multi-Order Adjacency Index and Geodetector model

Liu, Jing; Xu, Qiqi; Yi, Junhua; Huang, Xin

doi:10.1016/j.ecolind.2022.108655

Cited by 68 publications

(33 citation statements)

References 56 publications

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“…The extent of urban sprawl and its spatial heterogeneity are influenced by a combination of many factors such as economic growth, social development, natural conditions, and policy making. Based on the principles of scientific, comparability, independence, operability, and accessibility of indicators and on the basis of existing results [ 49 , 50 , 51 ], 15 indicators were selected from four levels, namely economic development, social culture, transportation, and government regulation, to quantitatively analyze the drivers of urban sprawl in the UYR, as shown in Table 2 . Using the natural break method in ArcGIS 10.8, the 15 metrics and USI were reclassified into 10 categories and sampled at 10 km × 10 km intervals and imported into the Geodetector.…”

Section: Methodsmentioning

confidence: 99%

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

Zhang

Guan

et al. 2022

IJERPH

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Urban sprawl has become the main pattern of spatial expansion in many large cities in China, and its ecological and environmental effects profoundly impact Chinese urban development. In this paper, nighttime light data and statistical yearbook data are adopted as basic data sources to simulate the evolution trend of the urban sprawl in the upper Yangtze River (UYR), China. First, the urban sprawl index (USI) is employed to assess the level of urban sprawl and to determine the characteristics of urban sprawl under different scales. Second, the spatial autocorrelation model is applied to reveal the spatial pattern change characteristics of urban sprawl from 1992 to 2015. Third, a scenario analysis model of urban sprawl is constructed to simulate the evolution trend of the urban sprawl under different scenarios. Finally, based on the Geodetector, the influence of factors and factor interactions influencing urban sprawl in different time periods is analyzed. The results yield the following main conclusions: (1) The urban sprawl in the UYR first intensifies and then stabilizes over time. The number of cities with high USI in Sichuan province, medium cities, and Chengdu-Chongqing urban agglomeration increases over time, indicating that urban sprawl is intensifying in these areas. (2) The urban sprawl hot spots experience a pattern transformation process of point-like expansion-point-ring expansion-point-axis expansion-axis radiation. (3) Under the scenarios with different scales, the urban land sprawl in large cities is the highest, accounting for more than 47% of the UYR. Urban land sprawl extent in the Chengdu-Chongqing urban agglomeration is the highest, accounting for more than 51% of the UYR. The cities exhibiting the highest sprawl are Chongqing, Lijiang, and Kunming, accounting for 25.84%, 7.37%, and 5.11%, respectively, of the UYR. (4) In the different time scenario simulations, the urban land in large cities exhibits the highest sprawl, accounting for approximately 48.16% of the UYR. The urban land in the Chengdu-Chongqing urban agglomeration demonstrates the highest sprawl, accounting for 50.92% of the UYR. (5) From 1996 to 2002, the driver with the highest influence on urban sprawl was secondary industry share of GDP, with a q-statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a q-statistic of 0.396.

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Section: Methodsmentioning

confidence: 99%

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

Zhang

Guan

et al. 2022

IJERPH

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“…Landscape pattern heterogeneity is a prevalent geographical phenomenon in urban expansion that increases landscape fragmentation, homogenizes urban biodiversity, and creates a serious danger to the stability and ecological security of the urban landscape ecosystem [16][17][18][19]. Analysis of landscape pattern changes and their driving forces could aid in coordinating the relationship between urbanization and landscape ecosystem protection, as well as promote the sustainable development of both the urban social economy and the landscape ecosystem [20]. Especially when ecological conservation has become a global advocacy issue, the sustainability of the landscape is an important prerequisite for the development of the economy [21,22], and it is important to explore the driving force of the landscape pattern under the combined effect of landscape conservation and economic development for planning urban ecosystems, building ecological civilization and realizing ecological economic development [21].…”

Section: Introductionmentioning

confidence: 99%

“…Although the aforementioned statistical analysis methods can investigate the relationship between the characteristics of urbanization landscape patterns and the influencing factors, there is a lack of ability to directly detect the heterogeneity of urban expansion landscape patterns. The geographic detector is a theoretical method and technique for detecting spatial heterogeneity and revealing the factors driving it [20]. It is feasible to calculate the impact and geographical variation of various elements on geospatial phenomena by using the models that have been built [20].…”

Section: Introductionmentioning

confidence: 99%

“…The geographic detector is a theoretical method and technique for detecting spatial heterogeneity and revealing the factors driving it [20]. It is feasible to calculate the impact and geographical variation of various elements on geospatial phenomena by using the models that have been built [20]. The geographic detector provides many advantages and has been applied widely in landscape pattern driving force exploration [36].…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City

et al. 2022

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Rapid urban expansion has significantly altered the regional landscape pattern, posing a serious threat to the sustainable development of natural and social ecosystems. By using landscape patterns indices and an area transfer matrix, this study analyzed the spatial-temporal changes of landscape patterns in the karst mountainous cities of southwest China from 2000 to 2020, by taking the central urban area of Guiyang City (CUAG) as the study area. This study explored the spatial and temporal driving factors of landscape pattern changes by using stepwise multiple linear regression and geographic detector methods. The results show: (1) CUAG’s landscape types altered changed drastically, with the area of forestland and construction land rapid increment and cultivated land decrement significantly. (2) The patches of construction land and forestland tended to be aggregated, the degree of fragmentation was reduced, and the shape was complex; cultivated land fragmentation was intensified. The connectivity of the landscape was improved, while the level of landscape diversity declined, the trend of landscape homogenization was obvious. (3) Socioeconomic and geographical endowment drivers have determined landscape pattern changes. The findings of this study may be used to interpret other similar landscapes worldwide and may imply the protection of urban ecosystem and sustainable development.

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“…In recent years, thanks to the rapid development of remote sensing and mapping, geographic information systems and other elds, which provide people with multi-source, and easily accessible spatial-temporal data and e cient analysis platforms, landscape ecology has seen signi cant developments and is pushing the frontier boundaries of ecology (Chen et al, 2021;Librán-Embid et al, 2020;Luo et al, 2021;Metzger et al, 2021;Yutian et al, 2022). As the core method of landscape ecology, landscape indices have matured in their theoretical basis and institutional framework, and have been applied to study and explore landscape fragmentation, spatial heterogeneity, the evolution of spatial and temporal patterns, and process-driven mechanisms in different regions, ecosystems, and scales around the world Liu et al, 2022;Zhang et al, 2022;).…”

Section: Introductionmentioning

confidence: 99%

Construction of 3D landscape index based on oblique photogrammetry and its application in islands

Guo

Wang

Liu

et al. 2022

Preprint

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The landscape index is quantitative indicator to reflect the composition and spatial configuration characteristics of landscape ecological structures. Following the "P-C-L" frameworks, eight 3D landscape indexes were constructed to quantitatively describe the spatial landscape features and two sensitivity indexes were built to identify the differences between 2D and 3D. Based on two kinds of oblique photogrammetry data of Sichang Island and Tianheng Island, the results show that: 1) The shape index (TPSI) indicates the spatial shape of the patch scale. The TPSI of vegetation was generally higher than that of buildings, with a reasonable correlation (R2 = 0.698). The classification index (TCI) shows remarkable performance in patch-type identification. When the value of TCI approaches values of 100 or 33, the patch-type is probably building or vegetation, respectively, with a classification accuracy rate of 95% after verification. 2) The sensitivity indexes, GSC and ESC, provide an evaluation criterion for the attribute transformation from 2D to 3D. The dimensional change significantly affected the buildings and arbor, with a GSC of 6.697 and 2.306, respectively. The changes in low-rise ground features were not significant. On class and landscape scales, the dynamic ranges of all six 3D indexes increased compared to 2D indexes. The highest was TLSI (3D Landscape Shape Index), and the lowest was TSHEI (3D Shannon Evenness Index), with a growth rate of 349.65% and 0.3%, respectively. 3) The 3D landscape indexes can better feature the biomass and the intensity of human development and construction activities on all scales. Its combination with modern remote sensing and mapping technology can provide a more sound ecological assessment method for spatial planning of different ecosystems.

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Analysis of the heterogeneity of urban expansion landscape patterns and driving factors based on a combined Multi-Order Adjacency Index and Geodetector model

Cited by 68 publications

References 56 publications

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City

Construction of 3D landscape index based on oblique photogrammetry and its application in islands

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