Observing the Response of Terrestrial Vegetation to Climate Variability Across a Range of Time Scales by Time Series Analysis of Land Surface Temperature

2016 ◽  
pp. 277-315 ◽  
Author(s):  
Massimo Menenti ◽  
H. R. Ghafarian Malamiri ◽  
Haolu Shang ◽  
Silvia M. Alfieri ◽  
Carmine Maffei ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Time Scales ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Terrestrial Vegetation ◽  
Series Analysis

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.

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 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.

scholarly journals Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

2021 ◽  
Vol 13 (5) ◽  
pp. 1019
Author(s):  
Jianhui Xu ◽  
Yi Zhao ◽  
Caige Sun ◽  
Hanbin Liang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Impervious Surface ◽  
Ecological Quality ◽  
Urban Center ◽  
Urban Centers ◽  
Rapid Urbanization

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals Climate variability and salmonellosis in Singapore – A time series analysis

2018 ◽  
Vol 639 ◽  
pp. 1261-1267 ◽  
Author(s):  
Joel Aik ◽  
Anita E. Heywood ◽  
Anthony T. Newall ◽  
Lee-Ching Ng ◽  
Martyn D. Kirk ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Time Series Analysis ◽  
Series Analysis

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

2019 ◽  
Vol 86 (sp1) ◽  
pp. 239
Author(s):  
Dhanya Joseph ◽  
Vazhamattom Benjamin Liya ◽  
Girindran Rojith ◽  
Pariyappanal Ulahannan Zacharia ◽  
George Grinson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Coastal Zones ◽  
Cmip5 Model ◽  
Series Analysis ◽  
Sea Surface Temperature Anomaly
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