A Time-Series Analysis of Land Surface Temperature in Macao, China

2011 ◽  
Vol 58-60 ◽  
pp. 1119-1123
Author(s):  
Jin Qu Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
City Development ◽  
Series Analysis ◽  
Temperature Separation ◽  
Urban Heat ◽  
The Pearl River

Macao city located in the Pearl River delta, China, was chosen to study the effect of urban heat island and its time-series analysis of land surface temperature (LST) in spatial expansion. The LST was analyzed by a temperature separation method based on statistical results. In the case of urban area, it was composed by three parts: downtown and old built-up areas with high-density buildings and dwellings, new built-up areas and developing site. The trend of city development was studied that the developing site would become to be new built-up areas and the formerly new built-up areas would become to be the downtown and old built-up areas. These three parts stand for different stages of a city.

scholarly journals Time Series Analysis of Land Surface Temperatures in 20 Earthquake Cases Worldwide

2018 ◽  
Vol 11 (1) ◽  
pp. 61 ◽  
Author(s):  
Efthymia Pavlidou ◽  
Mark van der Meijde ◽  
Harald van der Werff ◽  
Christoph Hecker
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Emissions ◽  
Temporal Fluctuations ◽  
Series Analysis ◽  
Land Surface Temperatures ◽  
Spatio Temporal

Earthquakes are reported to be preceded by anomalous increases in satellite-recorded thermal emissions, but published results are often contradicting and/or limited to short periods and areas around the earthquake. We apply a methodology that allows to detect subtle, localized spatio-temporal fluctuations in hyper-temporal, geostationary-based land surface temperature (LST) data. We study 10 areas worldwide, covering 20 large (Mw > 5.5) and shallow (<35 km) land-based earthquakes. We compare years and locations with and without earthquake, and we statistically evaluate our findings with respect to distance from epicentra and temporal coincidence with earthquakes. We detect anomalies throughout the duration of all datasets, at various distances from the earthquake, and in years with and without earthquake alike. We find no distinct repeated patterns in the case of earthquakes that happen in the same region in different years. We conclude that earthquakes do not have a significant effect on detected LST anomalies.

scholarly journals TIME SERIES ANALYSIS OF URBANISATION IMPACT ON THE TEMPERATURE VARIATIONS OFF MUMBAI COAST

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 31-37
Author(s):  
S. Bhattacharjee ◽  
K. Lekshmi ◽  
R. Bharti
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Coastal Waters ◽  
Land Surface ◽  
Continuous Process ◽  
Coastal System ◽  
Series Analysis ◽  
Climatic Environment ◽  
The Impact

Abstract. Urbanisation is an ever-evolving, complicated continuous process distinct from its surroundings, having the tendency to create a micro-scale system with characteristic local environmental conditions. Large-scale urbanization near the coasts has a definite impact on the coastal processes due to dynamic interactions of the coastal waters with the urban atmospheric, hydrological and anthropogenic residues. This study focuses on understanding the contribution of immediate atmospheric variations due to urbanization on surface temperature of coastal waters along the Mumbai coast. Different meteorological and air quality parameters such as Air Temperature (AT), Land Surface Temperature (LST), Precipitation (P), Relative Humidity (RH), Wind Speed (WS) and Aerosol Optical Depth (AOD) collectively were used as determinants of local urban climatic environment; to analyse and understand the impact of urbanization on Sea Surface Temperature (SST) representing coastal system. ERA5 Reanalysis meteorological data and MODIS satellite data products were used to extract information of the said parameters for a period of 20 years and time-series analysis was performed for each using Mann-Kendall method to establish their trend. Harmonic regression using Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were used to model the existing and forecast the future trend of SST which showed an increasing trend with comparatively better representation by NNAR (RMSE 0.4 – 0.7 K). Further, a polynomial multiple regression model was built to correlate the influence of all urban climatic parameters with SST, which clearly indicated positive forcing of local climate variation on the coastal waters with an R2 value of 0.93.

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