Analysis of the relationship between urban landscape patterns and thermal environment: a case study of Zhengzhou City, China

Li, Bin; Shi, Xuemin; Wang, Haiying; Qin, Mingzhou

doi:10.1007/s10661-020-08505-w

Cited by 40 publications

(10 citation statements)

References 54 publications

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“…Second, this manuscript also used MGWR to analyze the mechanism of high temperature on irritability, which is more conducive to the regulation of surface indicators in local high temperature areas and effectively reduces the risk of emotional health. Although they are not the same as the indicators selected for the thermal environment correlation research in recent years, they have obtained similar research results (Chen and Deng, 2021;Li et al, 2021). Yet, this paper only revealed the differential effect of a single land use type on temperature and irritability, but lacked the analysis of the joint effect of multiple land use types, and the effects of non-land-use type-related indicators such as regional climate, socio-demographics, local plant characteristics, albedo, and other environmental conditions were also ignored.…”

Section: Discussionmentioning

confidence: 76%

“…In 2019, Yu et al (2020) broke the limits of the MGWR model so that it can be widely used in empirical research. Shen et al (2020) discussed how the MGWR model influences second-hand house price, and proved that it is helpful to studies on spatial variation; Chen and Deng (2021) and Li et al (2021) used MGWR to analyze how urban landscape and form influence thermal environment.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

Huang

Ren

2022

Front. Ecol. Evol.

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The emotional health of urban residents is increasingly threatened by high temperatures due to global heating. However, how high temperature affects residents’ emotional health remains unknown. Therefore, this study investigated the spatiotemporal pattern of temperature’s impact on residents’ irritability using data from summer high-temperature measurement and emotional health survey in Beijing, combined with remote sensing images and statistical yearbooks. In detail, this study formulated a multiscale geographically weighted regression (MGWR) model, to study the differentiated and spatial influence of high-temperature factors on emotion. Results show: From 09:00 to 20:00, irritability level rose first then gradually dropped, with a pattern of “aggregation-fragmentation-aggregation.” Irritability is very sensitive to intercept and building density (BD). Other variables all have spatial heterogeneity [except for fraction vegetation coverage (FVC) or road network density (RND) as they are global variables], including normalized difference vegetation index (NDVI), water surface rate (WSR), floor area ratio (FAR), and Modified Normalized Difference Water Index (MNDWI) (sorted from the smallest to the largest in scale). Irritability is negatively correlated with NDVI, WSR, and RND, while positively correlated with intercept, MNDWI, FVC, FAR, and BD. Influence on irritability: WSR < NDVI < BD < MNDWI < RND < intercept < FVC < FAR.

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Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

Huang

Ren

2022

Front. Ecol. Evol.

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“…Therefore, the landscape patterns affecting the positive and negative effects of LST vary across geographic regions [ 74 ]. For example, on the one hand, the transpiration of vegetation and the shading of tree canopies have a cooling effect, and this will limit the cooling effect in areas with high fragmentation and poor connectivity of the green landscape [ 75 ]; on the other hand, in areas with a high fragmentation of patches, the shadows derived from vegetation on surrounding features have the probability of expanding and causing local temperature reduction [ 76 ]. The influence of landscape patterns on LST is unstable, and multiple scenarios need to be considered when exploring the correlation between landscape elements and LST.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Zhang

Kasimu

Liang

et al. 2022

IJERPH

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An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

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“…Past research has convincingly illustrated that urban areas experience higher temperatures compared to their neighboring rural counterparts [69]. Li's research on Zhengzhou unveiled a robust correlation between temperature and the landscape pattern of high-rise buildings [70]. The conclusion that temperature has a strong influence on landscape pattern indices is therefore reliable.…”

Section: Analysis Of Influencing Factorsmentioning

confidence: 99%

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

Ren,

Zhao,

Zhang

et al. 2023

Water

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The Yellow River Basin holds significance as a vital ecological shield and economic hub within China. Adapting land utilization practices and optimizing landscape patterns are of paramount significance in preserving the ecological equilibrium of the Yellow River Basin while fostering high-quality economic development. In this study, we selected the Yellow River Basin in Henan Province as our research area. We use a land use transition matrix and FRAGSTATS 4.2 software to analyze changes in land use and landscape patterns within the study area from 1990 to 2020. Furthermore, Geographical Detector is employed to explore the impact of different natural and social economic factors that have influenced the progress of the landscape surface pattern in the study area. Finally, to identify the zonal aggregation effects of primary components in connection with landscaping feature indices at the city dimension, we use bivariate local spatial autocorrelation. The results are as follows: (1) In terms of land use change characteristics, the area of cultivated land, grassland, shrubs, and bare land shows a decreasing tendency, the area of construction land and forest land shows an increasing tendency, and the water area fluctuates and changes. Most of the cultivated land is shifted to construction land, followed by forest land, construction land, and cultivated land mainly transferred from grassland. (2) At the level of type in terms of shifting landscape patterns, cultivated land, forest land, water, and construction land have a more complex landscape shape, reduced fragmentation, and better natural connectivity. At the overall level, the overall landscape pattern indices are relatively stable, with more patch types and a more balanced distribution. (3) The findings regarding influencing factors reveal that the primary industry output value, population, secondary industry output value, and temperature are the principal driving forces behind the progress of the landscape surface pattern. The main drivers have changed over time in different regions. As indicated by the findings from bivariate local spatial autocorrelation analysis, at the city scale, the leading cause of landscape fragmentation in Luoyang is the primary industry output value, while in Xinxiang, landscape fragmentation is primarily driven by the secondary industry output value and temperature. In this study, we introduce the bivariate local spatial autocorrelation method to analyze the clustering effects of key influencing factors and landscape patterns at the city scale. This is crucial for the harmonized growth of land use planning and the urban economy in the Yellow River Basin.

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Analysis of the relationship between urban landscape patterns and thermal environment: a case study of Zhengzhou City, China

Cited by 40 publications

References 54 publications

Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

Analysis of the spatiotemporal mechanism of high temperature on residents’ irritability in Beijing based on multiscale geographically weighted regression model

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

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