Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Wang, Mengen; Lu, Huimin; Chen, Binjie; Sun, Weiwei; Yang, Gang

doi:10.3390/rs15153732

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…The land surface temperature (LST) was selected from NASA's MOD11A1 dataset, which is available from the International Scientific Data Service Platform (http://datamirror. csdb.cn) with a spatial resolution of 1 km and a temporal resolution of 1 d [24,25].…”

Section: Data Description and Data Sourcesmentioning

confidence: 99%

Response of Alpine Timberline to Permafrost Degradation on Changbai Mountain

Shan,

Xu,

Wang

et al. 2023

Sustainability

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In the permafrost zone, the vegetation growth condition is closely related to the permafrost occurrence state. Changbai Mountain is the highest peak in Northeast China and is also a permafrost distribution area, where the vegetation shows an obvious vertical distribution pattern, and the alpine timberline has a clear boundary. The intersecting zone of alpine timberline is an ecologically fragile area that can be used as an external signal amplifier and is an important site for monitoring climate change. In this study, the surface frost number and alpine timberline in the Changbai Mountain area were analyzed through combining ground and remote-sensing data, using the freezing number model and support vector machine method. The results show that the distribution characteristics of alpine timberline and permafrost at elevation are correlated, there is a response mechanism of alpine timberline to the degradation of permafrost, and the upward migration rate of alpine timberline has increased in the last 20 years. The continuous degradation of permafrost will change the environment of vegetation growth, which, in turn, will affect the global carbon cycle process. Focusing on the state of permafrost will help us to understand climate change in depth, and we can respond to a series of impacts caused by ecological changes in cold regions in advance.

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Section: Data Description and Data Sourcesmentioning

confidence: 99%

Response of Alpine Timberline to Permafrost Degradation on Changbai Mountain

Shan,

Xu,

Wang

et al. 2023

Sustainability

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“…Thermal monitoring of different regions is usually restricted to meteorological data in ground stations [5], [54], [55]. There are few meteorological networks in arid and semi-arid areas, and it is not possible to monitor climatic conditions, especially on a regional and local scale, in these environments [56].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

Aliabad,

Ghaderpour,

Zare

et al. 2024

IEEE Access

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Thermal monitoring of different regions is usually limited to meteorological data in ground stations. Meteorological networks are limited in arid and semi-arid areas, where monitoring climatic conditions is not possible. The aim of this study is to estimate the land surface temperature (LST) hourly for Yazd-Ardakan plain by modeling the diurnal temperature cycle (DTC) using LST imagery of moderate resolution imaging spectroradiometer (MODIS). First, MODIS imagery are reconstructed using the multichannel singular spectrum analysis, and the complete time series without missing values are created. Then, six DTC models are compared. The accuracy of DTC models is examined by ground LST measurements, air temperature, humidity, and wind speed. In addition, the results of examining the root mean square error (RMSE) images obtained from cross-validation based on MODIS LST imagery show that DTC2 has the highest error, where 73% of the area has RMSE greater than 3 • C. In DTC1 and DTC2, 64% and 5.8% of the study region has RMSE less than 2 • C. In general, DTC1, DTC6, DTC5, DTC4, DTC3, and DTC2 models have shown the highest to lowest accuracy in modeling the LST diurnal cycle. In addition, the difference between LST in mountain and plain lands is greater at the time of maximum temperature than at other hours of the day and night. The findings of this research are crucial in studies concerning climate change and land environmental monitoring in arid/semi-arid regions.

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Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China

Wang,

Cao,

Zhang

et al. 2024

PLoS ONE

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The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, the Urban Heat Island (UHI) effect in cities has intensified year by year, jeopardizing the foundation of sustainable urban development. Prudent urban spatial planning holds the potential to robustly ameliorate the persistent deterioration of the UHI phenomenon. This study selects Jinan City as a case study and employs spatial autocorrelation and spatial regression algorithms to explore the spatiotemporal evolution of urban-rural patterns at the township scale. The aim is to identify key factors driving the spatiotemporal differentiation of Land Surface Temperature (LST) from 2013 to 2022. The research reveals a trend of initially rising and subsequently falling LST in various townships, with low-temperature concentration areas in the southern mountainous region and the northern plain area. The "West-Central-East" main urban axis and the southeast Laiwu District exhibit high-temperature zones. Significant influences on LST are attributed to pollution levels, topographical factors, urbanization levels, and urban greenness. The global Moran’s Index for LST exceeds 0.7, indicating a strong positive spatial correlation. Cluster analysis results indicate High-High (HH) clustering in the central Shizhong District and Low-Low (LL) clustering in the northern Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms Geographically Weighted Regression (GWR) and Ordinary Linear Regression (OLR), providing a more accurate reflection of the regression relationships between variables. By investigating the spatiotemporal evolution of LST and its driving factors at the township scale, this study contributes insights for future urban planning and sustainable development.

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Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

Cited by 3 publications

References 60 publications

Response of Alpine Timberline to Permafrost Degradation on Changbai Mountain

Response of Alpine Timberline to Permafrost Degradation on Changbai Mountain

A Comparative Study of Estimating Hourly Images of MODIS Land Surface Temperature Using Diurnal Temperature Cycle Models in Arid Regions

Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China

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