Spatiotemporal patterns in a general networked activator–substrate model

To reveal the spatiotemporal patterns of the asymmetry in the Tianshan mountains’ climatic warming, in this study, we analyzed climate and MODIS snow cover data (2001–2019). The change trends of asymmetrical warming, snow depth (SD), snow coverage percentage (SCP), snow cover days (SCD) and snow water equivalent (SWE) in the Tianshan mountains were quantitatively determined, and the influence of asymmetrical warming on the snow cover activity of the Tianshan mountains were discussed. The results showed that the nighttime warming rate (0.10 °C per decade) was greater than the daytime, and that the asymmetrical warming trend may accelerate in the future. The SCP of Tianshan mountain has reduced by 0.9%. This means that for each 0.1 °C increase in temperature, the area of snow cover will reduce by 5.9 km2. About 60% of the region’s daytime warming was positively related to SD and SWE, and about 48% of the region’s nighttime warming was negatively related to SD and SWE. Temperature increases were concentrated mainly in the Pamir Plateau southwest of Tianshan at high altitudes and in the Turpan and Hami basins in the east. In the future, the western and eastern mountainous areas of the Tianshan will continue to show a warming trend, while the central mountainous areas of the Tianshan mountains will mainly show a cooling trend.

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Re-calculation of responsibility distribution and spatiotemporal patterns of global production carbon emissions from the perspective of global value chain

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.145065 ◽

2021 ◽

Vol 773 ◽

pp. 145065

Author(s):

Zhi Zheng

Keyword(s):

Carbon Emissions ◽

Value Chain ◽

Spatiotemporal Patterns ◽

Global Value Chain

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Spatiotemporal patterns of cutaneous leishmaniasis in the district upper and lower Dir, Khyber Pakhtunkhwa, Pakistan: A GIS-based spatial approaches

Acta Tropica ◽

10.1016/j.actatropica.2021.105861 ◽

2021 ◽

Vol 217 ◽

pp. 105861

Author(s):

Ismail Zeb ◽

Naveeda Akhter Qureshi ◽

Nargis Shaheen ◽

Mazhar Iqbal Zafar ◽

Abid Ali ◽

...

Keyword(s):

Cutaneous Leishmaniasis ◽

Spatiotemporal Patterns ◽

Khyber Pakhtunkhwa

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Declining dry deposition of NO2 and SO2 with diverse spatiotemporal patterns in China from 2013 to 2018

Atmospheric Environment ◽

10.1016/j.atmosenv.2021.118655 ◽

2021 ◽

pp. 118655

Author(s):

Kaiyue Zhou ◽

Yu Zhao ◽

Lin Zhang ◽

Mengxiao Xi

Keyword(s):

Dry Deposition ◽

Spatiotemporal Patterns

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Spatiotemporal Patterns Formed by a Discrete Nutrient-Phytoplankton Model with Time Delay

Complexity ◽

10.1155/2020/8541432 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Feifan Zhang ◽

Wenjiao Zhou ◽

Lei Yao ◽

Xuanwen Wu ◽

Huayong Zhang

Keyword(s):

Steady State ◽

Time Delay ◽

Numerical Simulations ◽

Functional Response ◽

Bifurcation Analysis ◽

Discrete Model ◽

Continuous Model ◽

Spatiotemporal Patterns ◽

Homogeneous Steady State ◽

Simulation Results

In this research, a continuous nutrient-phytoplankton model with time delay and Michaelis–Menten functional response is discretized to a spatiotemporal discrete model. Around the homogeneous steady state of the discrete model, Neimark–Sacker bifurcation and Turing bifurcation analysis are investigated. Based on the bifurcation analysis, numerical simulations are carried out on the formation of spatiotemporal patterns. Simulation results show that the diffusion of phytoplankton and nutrients can induce the formation of Turing-like patterns, while time delay can also induce the formation of cloud-like pattern by Neimark–Sacker bifurcation. Compared with the results generated by the continuous model, more types of patterns are obtained and are compared with real observed patterns.

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Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

Remote Sensing ◽

10.3390/rs13050974 ◽

2021 ◽

Vol 13 (5) ◽

pp. 974

Author(s):

Lorena Alves Santos ◽

Karine Ferreira ◽

Michelle Picoli ◽

Gilberto Camara ◽

Raul Zurita-Milla ◽

...

Keyword(s):

Machine Learning ◽

Land Use ◽

Time Series ◽

Satellite Image ◽

Spatiotemporal Patterns ◽

Supervised Machine Learning ◽

Learning Methods ◽

Self Organizing Maps ◽

Machine Learning Methods ◽

Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

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