Analysis of spatial differentiation and air quality impact of green space landscape in Xi'an, China

Ren, Wenjing; Ren, Wenjing; Ma, Xuan

doi:10.1007/s13762-023-04844-6

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…It is notable that a complex nonlinear relationship between vegetated area metrics and UIE-VG was observed. This is because large areas of aggregated vegetation are resistant to anthropogenic disturbances and have been well managed in urban areas but also lower local temperatures and atmospheric CO 2 concentrations 22,39,40 . Furthermore, based on the results of regulation amplitudes (Δω i ), built-up land landscape patterns significantly enhanced UIE-VG, with a range from 3.5% to 81.3%.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies indicate that the expansion of built-up areas is related to the intensification of urban heat islands, urban dry islands, and urban pollution islands [18][19][20] , which in turn influence vegetation growth 21 . Conversely, increasing green land and waterbodies can mitigate these effects 22 . Furthermore, a complex nonlinear relationship exists between microclimate factors and landscape patterns, and this relationship can vary in urbanization 23 .…”

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confidence: 99%

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Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Li,

Zhao,

Chen

et al. 2024

npj Urban Sustain

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Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Li,

Zhao,

Chen

et al. 2024

npj Urban Sustain

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“…Its privileged subtropical monsoon climate, abundant sunlight, and rainfall offer a conducive environment for agricultural development. As one of the earliest developed regions in Taiwan, Chiayi is also Taiwan's most important timber distribution and processing center, earning the reputation of a "Timber Metropolis" [67].…”

Section: Spatiotemporal Variation Characteristics Of Nighttime Light ...mentioning

confidence: 99%

Spatiotemporal Characteristics of Urbanization in the Taiwan Strait Based on Nighttime Light Data from 1992 to 2020

Yun

et al. 2023

Remote Sensing

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Urbanization is a crucial indicator which reflects the socio-economic development of a country or region. The regions across the Taiwan Strait (TSR) have garnered attention worldwide as being representative of typical urbanization development along the southeastern coast. Currently, research in the TSR predominantly focuses on individual regions, with limited academic achievements comparing urbanization paths across the strait. In particular, the domain of comparative analysis of the spatiotemporal characteristics of urbanization dynamics in TSR by using long time series of nighttime light data remains largely underexplored. Therefore, this study focused on comparing the urbanization paths in the TSR and analyzing the spatiotemporal characteristics of urbanization by using the long-term nighttime light data from 1992 to 2020. Additionally, some methods such as Theil–Sen median trend analysis, Mann–Kendall significance test, Hurst exponent, spatial statistics, and time series were used to quantitatively analyze the spatial distribution patterns and temporal trends of nighttime lights in the TSR since 1992. The results were as follows: (1) From 1992 to 2020, the spatial distribution of nighttime light data in TSR exhibited significant spatial heterogeneity, with high-value areas mainly located in southeastern Fujian and northwestern Taiwan, while low-value areas were concentrated in Fujian’s inland regions; (2) During this period, nighttime lighting data increased from 729,863 in 1992 to 2,729,052 in 2020, and the percentage of its high-value (40–063) increased from 2.59% in 1992 to 12.22% in 2020; (3) Comparison of nighttime light data across representative cities from Taiwan (Taipei, Hsinchu) and Fujian (Xiamen, Fuzhou) uncovered distinct growth patterns—while Taiwanese cities had a high initial brightness value (the lowest value in the last 30 years was 518,379.4), their growth was relatively slow (average growth rate of 17%); Fujian cities, on the other hand, started from lower initial brightness value (the lowest value in the last 30 years was 35,123.1), but displayed substantial growth vigor (average growth rate of 222%); (4) During the study period, the nighttime light data of the vast majority of cities in the TSR demonstrated a significant increasing trend, particularly in coastal areas and urban centers; (5) Predictions of future trend variation suggest that the significantly increasing trend of cities surrounding Taiwan’s primary metropolitan areas will intensify, whereas metropolitan regions such as Keelung may witness a decline in future trend variations. However, only a mere 0.03% of the nighttime light data show a significant decreasing trend. Additionally, there are distinct differences in the urbanization development stages of the TSR. Fujian is currently undergoing rapid urbanization, while Taiwan’s urbanization has entered a stable stage. The study reveals that factors such as geographical location, natural resources, transportation infrastructure, population size, and industrial structure collectively influence the urbanization characteristics of the TSR. This research bears substantial significance for deepening the comprehension of the patterns and processes of urbanization development in the TSR and provides valuable insights for urban construction and development across the strait.

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Quantitative Characterization of Highway Landscape Space Visual Perception Based on Deep Learning

Qin,

Liu,

Yang

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Analysis of spatial differentiation and air quality impact of green space landscape in Xi'an, China

Cited by 5 publications

References 51 publications

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Spatiotemporal Characteristics of Urbanization in the Taiwan Strait Based on Nighttime Light Data from 1992 to 2020

Quantitative Characterization of Highway Landscape Space Visual Perception Based on Deep Learning

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