Constructing MODIS LAI time-series background library based on temporal and spatial analysis of MODIS LAI products

2010 ◽  
Author(s):  
Huifang Zhang ◽  
Runhe Shi ◽  
Chaoshun Liu ◽  
Wei Gao
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Temporal And Spatial ◽  
Modis Lai

scholarly journals Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Lianren Wu ◽  
Jinjie Li ◽  
Jiayin Qi
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Power Law ◽  
Clustering Algorithm ◽  
Temporal Data ◽  
Literature Reference ◽  
Spectral Centroid ◽  
Popularity Dynamics ◽  
Temporal And Spatial ◽  
Spatio Temporal

AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MATLAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion.

scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

Risk areas for Human Visceral Leishmaniosis 2007–2018: A temporal and spatial analysis in Ceará, Brazil

2021 ◽  
Author(s):  
Kellyn Kessiene de Sousa Cavalcante ◽  
Jarier de Oliveira Moreno ◽  
Reagan Nzundu Boigny ◽  
Francisco Roger Aguiar Cavalcante ◽  
Caroline Mary Gurgel Dias Florêncio ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Risk Areas ◽  
Temporal And Spatial ◽  
Visceral Leishmaniosis

The open access usage advantage: a temporal and spatial analysis

2021 ◽  
Author(s):  
Guangyao Zhang ◽  
Yuqi Wang ◽  
Weixi Xie ◽  
Han Du ◽  
Chunlin Jiang ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Open Access ◽  
Temporal And Spatial

An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series

2008 ◽  
Vol 5 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Feng Gao ◽  
Jeffrey T. Morisette ◽  
Robert E. Wolfe ◽  
Greg Ederer ◽  
Jeff Pedelty ◽  
...  
Keyword(s):  
Time Series ◽  
Modis Lai

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

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