Investigating the effects of urban morphological factors on seasonal land surface temperature in a “Furnace city” from a block perspective

Yao, Xiong; Zhu, Zhiping; Zhou, Xingwen; Shen, Yuanping; Shen, Xiabing; Xu, Zhanghua

doi:10.1016/j.scs.2022.104165

Cited by 24 publications

(3 citation statements)

References 55 publications

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“…Results also showed that the relationship between MNDWI and LST was different from that of most studies. Comparing with the studies of Roy and Tetali on land cities ( Tetali et al, 2022 , Yao et al, 2022c ), their studies suggested a negative correlation between MNDWI and LST, while this study was on the contrary. In autumn and winter, MNDWI and LST of island cities were positively correlated.…”

Section: Discussioncontrasting

confidence: 60%

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Zhu

Shen

et al. 2023

Ecological Indicators

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Section: Discussioncontrasting

confidence: 60%

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Zhu

Shen

et al. 2023

Ecological Indicators

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“…The investigation of how topographic factors affect landscape pattern can be simplified as the distribution frequency of various landscape types on different topographic gradients. In order to avoid the dimensional influence caused by the different proportions of different land-use areas under the topographic gradient, we adopt the distribution index model to quantitatively characterize the distribution frequency of each land-use type on different topographic gradients [ 46 , 43 ]. The calculation of distribution index was calculated as:

where P is the distribution index, S ie is the area of the i th land-use type distributed in the topographic interval e ; S i is the total area of the i th land-use type in the study area; S e is the total area of the topographic interval e in the study area; S is the total area of the study area.…”

Section: Methodsmentioning

confidence: 99%

Assessing the effects of topographic gradients on landscape patterns: The study case of Tingjiang river basin, China

Dong

2023

Heliyon

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“…However, the majority of existing literature on LCZ mapping utilizes night-time light data with a coarse spatial resolution, which can potentially impact the final classification accuracy. As a new generation of night-time light-capturing sensors, the Luojia1-01 satellite produces a finer spatial resolution NTL data (130 m) than traditional sensors, i.e., Defense Meteorological Satellite Program-Operational Line-scan System (DMSP/OLS, >1 km) and National Polar-Orbiting Partnership-Visible Infrared Imagery Radiometer Suite (NPP/VIIRS, 541 m) [32]. Assessing the performance of Luojia-1 NTL data on LCZ mapping is necessary.…”

Section: Introductionmentioning

confidence: 99%

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

Wang

Cao

et al. 2023

Remote Sensing

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Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1–9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping.

show abstract

Investigating the effects of urban morphological factors on seasonal land surface temperature in a “Furnace city” from a block perspective

Cited by 24 publications

References 55 publications

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

How does 2D and 3D of urban morphology affect the seasonal land surface temperature in Island City? A block-scale perspective

Assessing the effects of topographic gradients on landscape patterns: The study case of Tingjiang river basin, China

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

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