Spatial–Temporal Variability of Land Surface Temperature Spatial Pattern: Multifractal Detrended Fluctuation Analysis

Abstract

In order to investigate the spatial-temporal variability of land surface temperature (LST) spatial distribution in the context of rapid urbanization, we introduced the multifractal detrended fluctuation analysis (MFDFA) to the LST patterns in Xiamen city and Xiamen Island, China, during 1994-2015. Results reveal the almost same long-range dependence of the LST spatial pattern both in Xiamen city and Xiamen Island. LST has a long memory for a certain spatial range of LST values, such that a large increment in LST value is likely to be followed by a large increment in LST values of a certain spatial range. On the other hand, the LST spatial pattern possesses a multifractal nature, which shows an increasing trend over time as urbanization increased both in the whole study area and in the Xiamen Island. The irregularity of fractal structure exhibits a similar change from 1994 to 2015 in Xiamen city, as revealed by the multifractal spectrum with lefthook shapes. However, the multifractal spectrums exhibit different shapes for different study years in Xiamen Island, capturing the evolution from right-hook shape to left-hook shape and finally to a symmetrical shape. The difference in land surface change in small spatial scale leads to the variation in multifractal parameters. Meantime, differentiated from the previous study, we found that this long-range dependence is probably influenced by the natural factors, such as the local climate, atmospheric circulation, and any other factors.

Characterizing the Spatio-Temporal Variations of Urban Growth with Multifractal Spectra

“…The spatio-temporal evolution of urban systems is a crucial aspect of urban planning, and multifractal methodology offers an effective tool for studying this evolution. However, due to limited time series data, most studies have focused on comparing changes in multifractal spectra to understand the general laws of urban development [ 19 , 30 , 31 , 32 , 33 , 34 , 35 ], or fitted the time series of D q using logistic functions to reveal the spatial replacement dynamics of urban development. Based on logistic functions, it is possible to determine the type of urban evolution, predict the time of maximum speed, and divide urban development stages macroscopically [ 36 ].…”

“…Nevertheless, the evolution of cities in the real world, as reflected in multifractal parameters, may exhibit local fluctuations. While many studies focus on seeking macroscopic patterns of urban evolution through spectrum comparison [ 19 , 30 , 31 , 32 , 33 , 34 , 35 ] or trend fitting [ 36 ], few studies delve into characterizing spatio-temporal variations of urban evolution at a microlevel.…”

Progress of remote sensing inversion research on surface temperature of lakes and reservoirs

“…However, obtaining high-quality remotely sensed data is still a challenge, especially regarding the monitoring of large-scale lakes. In addition, it is also a challenge to properly handle disturbances such as noise and cloud cover in remotely sensed data [6][7][8] ， In this section, a detailed summary of remote sensing data sources for lake surface temperature inversion is presented.…”

Characterizing the Spatio-Temporal Variations of Urban Growth with Multifractal Spectrums

“…Nevertheless, the evolution of cities in the real world, as reflected in multifractal parameters, may exhibit local fluctuations. While many studies focus on seeking macroscopic patterns of urban evolution through spectrum comparison [7,[18][19][20][21][22][23] or trend fitting [24], few studies delve into characterizing spatio-temporal variations of urban evolution at a micro level. Hence, by combining the distinct growth characteristics of multifractal parameters under positive and negative moment orders, we can expect to explore more detailed aspects of the spatio-temporal evolution process of urban morphology.…”