Assessment of urban cooling effect based on downscaled land surface temperature: A case study for Fukuoka, Japan

Peng, Wangchongyu; Yuan, Xin; Gao, Weijun; Wang, Rui; Chen, Wei

doi:10.1016/j.uclim.2021.100790

Cited by 26 publications

(7 citation statements)

References 38 publications

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“…The MIR band, which proves the positive linear relationship between soil moisture, was selected as a proxy to indicate the change in soil moisture [ 13 ]. Peng et al [ 38 ] used the SZA as a parameter to downscale LST. The SZA refers to the angle between the incident light direction and the zenith direction.…”

Section: Methodsmentioning

confidence: 99%

“…The characteristics of RF regression and Kriging regression are combined to construct this method. Specifically, RF regression is first used to improve the quality of the original data and obtain the spatial LST feature; then, a stable nonlinear link is built and connected to the LST with several spatiotemporal parameters that are directly related to the LST, including terrain [ 33 ], population density (PD) [ 34 ], land use and land cover change (LUCC) [ 35 ], soil texture (in terms of the clay, silt, and sand contents, ST) [ 36 ], enhanced vegetation index (EVI) [ 37 ], solar zenith angle (SZA) [ 38 ], and middle-infrared surface reflectance (MIR) [ 13 ] data as well as the LST spatial feature of the LST. Finally, Kriging regression is applied to further narrow the residuals.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China

Teng

Chen

et al. 2023

Sensors

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Land surface temperatures (LST) are vital parameters in land surface–atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current reconstruction methods are limited for maintaining spatial details and high accuracies. We developed a new gap-free algorithm termed the spatial feature-considered random forest regression (SFRFR) model; it builds stable nonlinear relationships to connect the LST with related parameters, including terrain elements, land coverage types, spectral indexes, surface reflectance data, and the spatial feature of the LST, to reconstruct the missing LST data. The SFRFR model reconstructed gap-free LST data retrieved from the Landsat 8 satellite on 27 July 2017 in Wuhan. The results show that the SFRFR model exhibits the best performance according to the various evaluation metrics among the SFRFR, random forest regression and spline interpolation, with a coefficient of determination (R2) reaching 0.96, root-mean-square error (RMSE) of 0.55, and mean absolute error (MAE) of 0.55. Then, we reconstructed gap-free LST data gathered in Wuhan from 2016 to 2021 to analyze urban thermal environment changes and found that 2020 presented the coolest temperatures. The SFRFR model still displayed satisfactory results, with an average R2 of 0.91 and an MAE of 0.63. We further discuss and discover the factors affecting the visual performance of SFRFR and identify the research priority to circumvent these disadvantages. Overall, this study provides a simple, practical method for acquiring gap-free LST data to help us better understand the spatiotemporal LST variation process.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China

Teng

Chen

et al. 2023

Sensors

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“…At each split, a subset of the available predictor variables is randomly selected to further enhance the diversity and independence of the trees in the ensemble. This process continues until a predefined stopping criterion is satisfied, such as reaching a specified maximum tree depth or the minimum number of samples required for further splitting [62]. Once trained, the accuracy and performance of the model are evaluated by the coefficient of determination (R 2 ).…”

Section: Random Forest Regression Model For Feature Importance Assess...mentioning

confidence: 99%

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Wu,

Zhang,

et al. 2023

Sustainability

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Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.

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“…Studies on UF temperatures currently focus on extreme conditions, such as heatwaves and cold spells that occur during the peak of summer and winter, respectively [27,[58][59][60]. In addition, research on the temperature reduction effects of UFs using remote sensing techniques is actively being conducted [61][62][63]. Analysis of temperature reduction effects using remote sensing techniques has the advantage of the capability to compare large spatial scales simultaneously.…”

Section: Plos Onementioning

confidence: 99%

Cooling effect of urban forests on the urban heat island in Seoul, South Korea

et al. 2023

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Air pollution and climate change amplify the urban heat island (UHI) effect, which has an adverse effect on human health. Urban forests (UFs) are important to reduce the UHI effect; however, the quantitative effect of UFs on UHI, relative to time and space, has not yet been investigated. In this study, we aimed to quantitatively measure the actual thermal environment in UFs. To this end, temperature and humidity loggers were installed in 17 UFs in Seoul for a year and analyzed according to vegetation characteristics and accessibility. The urban forests and park showed consistent temperature reduction, whereas the lawn showed higher temperature reduction effects during autumn-winter. The traffic island showed lower annual temperature reduction effect than other UFs. From spring to autumn, mixed and broadleaved forests showed better temperature reduction effect than coniferous forests. The temperature in UFs decreased by approximately 1.9°C over ~3 km from the traffic island near the city to the forest. This study revealed the difference in the cooling effect according to the type and location of UF and the vegetation structure. The functional characteristics of plants and the UF that reflects them can help reduce the negative impact of climate warming and UHI on human health.

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Assessment of urban cooling effect based on downscaled land surface temperature: A case study for Fukuoka, Japan

Cited by 26 publications

References 38 publications

Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China

Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China

How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data

Cooling effect of urban forests on the urban heat island in Seoul, South Korea

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