Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature

The urban heat island (UHI) effect is a global problem that is likely to grow as a result of urban population expansion. Multiple studies conclude that green spaces and waterbodies can reduce urban heat islands. However, previous studies often treat urban green spaces (UGSs) as static or limit the number of green spaces investigated within a city. Cognizant of these shortcomings, Landsat derived vegetation and land surface temperature (LST) metrics for 80 urban green spaces in Puebla, Mexico, over a 34-year (1986–2019) and a 20-year (2000–2019) period were studied. To create a photo library, 73 of these green spaces were visited and the available land cover types were recorded. Green spaces with Indian laurel were found to be much greener and vegetation index values remained relatively stable compared to green spaces with mixed vegetation cover. Similarly, green spaces with large waterbodies were cooler than those without water. These results show that larger green spaces were significantly cooler (p < 0.01) and that size can explain almost 30% of temperature variability. Furthermore, green spaces with higher vegetation index values were significantly cooler (p < 0.01), and the relationship between greenness and temperature strengthened over time.

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Analysis of the Cooling Effect of Urban Green Spaces in Mitigating Micro-climate Change Using Geospatial Techniques in Adama City, Central Ethiopia

10.21203/rs.3.rs-1095929/v1 ◽

2021 ◽

Author(s):

Biratu Bobo Merga ◽

Kenate Worku Tabor ◽

Girma Alemu

Keyword(s):

Climate Change ◽

Urban Planning ◽

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Green Space ◽

Green Spaces ◽

Urban Green ◽

Urban Green Spaces

Abstract Nowadays, addressing urban climate in urban planning through mapping has got world-wide attention. Greening urban environment is one of the best mechanisms to combat the effects of micro-climate change. Therefore, this study aims at analyzing the cooling effects of Urban Green Spaces (UGS) in mitigating micro-climate change in Adama City with special emphasis on land surface temperature variation with respective to vegetation cover for the last two decades i.e. from 2000 to 2020. Three different remotely sensed data of Landsat7 ETM+ (2000 and 2010) as well as Landsat8 OLI/TIRS (2020) were used in the study. The consistent land surface temperature data were retrieved from Landsat7 ETM+ and Landsat8 OLI/TIRS using mono window and split window algorithms, respectively. Regression and correlation analysis among Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were also performed in Statistical Package for Social Science (SPSS V23). The study reveals that the proportion of Urban Green Spaces (UGS) to other land use/land cover particularly, dense vegetation cover were reduced from 29.3Km2(21.20%) in 2000 to 18.17Km2 (13.15%) in 2020. The main land dynamic process, which could considerably contribute to the increase in Land Surface Temperature, was the rapid expansion of built-up areas in the study area. The model produced through multiple linear regression analysis clearly indicates that the two urban parameters (built up and green areas) contributed 75.2% of the Land Surface Temperature (LST) variations in Adama City. The cooling efficiency (CE) and the threshold value of efficiency (TVoE) of green space in Adama City were calculated as 5.5 ± 0.5 ha. This finding implies that when Adama City municipality implements urban planning, allocating a green space area of 5.5 ± 0.5 ha reduces surface temperature by about 2.85 0C which is the most efficient to reduce heat effects. The study suggests that strengthening of plan execution capacity, public participation in urban planning and strengthening the development of urban green spaces as an important strategy to mitigate the effects of micro-climate change.

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Application of Land Surface Temperature Analysis in Urban Green Spaces: Case Studies from South Asia

GI_Forum ◽

10.1553/giscience2021_02_s202 ◽

2021 ◽

Vol 1 ◽

pp. 202-214

Author(s):

Gulam Mohiuddin ◽

Jan–Peter Mund

Keyword(s):

Surface Temperature ◽

South Asia ◽

Case Studies ◽

Land Surface Temperature ◽

Land Surface ◽

Green Spaces ◽

Temperature Analysis ◽

Urban Green ◽

Urban Green Spaces

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Vegetation index-based biomass model and Land Surface Temperature (LST) from urban green spaces in Bandung City derived from multispectral imageries

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/747/1/012060 ◽

2021 ◽

Vol 747 (1) ◽

pp. 012060

Author(s):

A L Suti ◽

I P Ash Shidiq ◽

Rokhmatulloh ◽

A Wibowo

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Green Spaces ◽

Urban Green ◽

Urban Green Spaces

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Dynamic Changes in Community Deprivation of Access to Urban Green Spaces by Multiple Transport Modes

Frontiers in Public Health ◽

10.3389/fpubh.2021.615432 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shunqi Cheng ◽

Zhiqiang Liao ◽

Yu Zhu

Keyword(s):

Growth Rate ◽

Spatial Pattern ◽

Urban Area ◽

Public Transport ◽

Green Spaces ◽

Dynamic Changes ◽

Urban Green ◽

Urban Green Spaces ◽

The Core ◽

Transport Modes

Urban green spaces (UGSs) improve the quality of life of urban inhabitants. With the acceleration of urbanization and changes in traffic networks, it remains unclear whether changes in the distribution of UGSs can satisfy the needs of all inhabitants and offer equal services to inhabitants from different socioeconomic backgrounds. This study addresses this issue by analyzing dynamic changes in UGS accessibility in 2012, 2016, and 2020 for inhabitants of the central urban area of Fuzhou in China at the community level. The study introduces multiple transportation modes for an accessibility estimation based on a framework using the two-step floating catchment area method and examines the dynamic changes in community deprivation of UGS accessibility using Kernel regularized least squares, a machine learning algorithm. The results demonstrate that spatial disparities of UGS accessibility exist among the multi-transport modes and vary with time. Communities with high accessibility to UGSs by walking are scattered around the urban area; for accessibility by cycling, the high accessibility regions expand and surround the regions with low accessibility in the core urban areas, forming a semi-enclosed spatial pattern. However, the core urban spatial orientation of UGS accessibility by public transit demonstrates a reverse trend to the above two modes. The spatial pattern of UGS accessibility also varies over time, and the growth rate of accessibility slightly declined during the study period. Furthermore, the increase in UGS accessibility tended to slow from 2016–2020 compared with 2012–2016, and the trend toward equality was also erratic. The degree of deprivation for communities first weakened and was then aggravated, corresponding to the slowdown in the growth rate of accessibility, leading to the persistence existence of social inequality. Moreover, significant deprivation mainly exists among less educated people or those using the cycling and integrated travel modes. Although public transport is developing, deprived communities, such as communities with large proportion of older people, have experienced a decline in access to UGSs by public transport. Based on these findings, the study proposes a policy framework for the balanced distribution of UGSs as part of urbanization.

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