ARMA time series modelling of remote sensing imagery: A new approach for climate change studies

2002 ◽  
Vol 23 (24) ◽  
pp. 5225-5248 ◽  
Author(s):  
J. M. Piwowar ◽  
E. F. Ledrew
2014 ◽  
Vol 24 ◽  
pp. 17-26 ◽  
Author(s):  
Lifan Chen ◽  
Zhenyu Jin ◽  
Ryo Michishita ◽  
Jun Cai ◽  
Tianxiang Yue ◽  
...  

2021 ◽  
Vol 13 (19) ◽  
pp. 3845
Author(s):  
Guangbo Ren ◽  
Jianbu Wang ◽  
Yunfei Lu ◽  
Peiqiang Wu ◽  
Xiaoqing Lu ◽  
...  

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.


Hydrobiologia ◽  
2020 ◽  
Vol 848 (1) ◽  
pp. 77-94 ◽  
Author(s):  
Martin T. Dokulil ◽  
Kuimei Qian

AbstractThe review intends to give an overview on developments, success, results of photosynthetic research and on primary productivity of algae both freshwater and marine with emphasis on more recent discoveries. Methods and techniques are briefly outlined focusing on latest improvements. Light harvesting and carbon acquisition are evaluated as a basis of regional and global primary productivity and algal growth. Thereafter, long-time series, remote sensing and river production are exemplified and linked to the potential effects of climate change. Lastly, the synthesis seeks to put the life achievements of Colin S. Reynolds into context of the subject review.


2013 ◽  
Vol 51 (1) ◽  
pp. 140-150 ◽  
Author(s):  
Luciana Alvim S. Romani ◽  
Ana Maria H. de Avila ◽  
Daniel Y. T. Chino ◽  
Jurandir Zullo ◽  
Richard Chbeir ◽  
...  

PLoS Medicine ◽  
2018 ◽  
Vol 15 (7) ◽  
pp. e1002629 ◽  
Author(s):  
Yuming Guo ◽  
Antonio Gasparrini ◽  
Shanshan Li ◽  
Francesco Sera ◽  
Ana Maria Vicedo-Cabrera ◽  
...  

2019 ◽  
Vol 39 (22) ◽  
Author(s):  
靖传宝 JING Chuanbao ◽  
周伟奇 ZHOU Weiqi ◽  
钱雨果 QIAN Yuguo

2008 ◽  
Vol 9 (6) ◽  
pp. 1377-1389 ◽  
Author(s):  
Thomas A. McMahon ◽  
Anthony S. Kiem ◽  
Murray C. Peel ◽  
Phillip W. Jordan ◽  
Geoffrey G. S. Pegram

Abstract This paper introduces a new approach to stochastically generating rainfall sequences that can take into account natural climate phenomena, such as the El Niño–Southern Oscillation and the interdecadal Pacific oscillation. The approach is also amenable to modeling projected affects of anthropogenic climate change. The method uses a relatively new technique, empirical mode decomposition (EMD), to decompose a historical rainfall series into several independent time series that have different average periods and amplitudes. These time series are then recombined to form an intradecadal time series and an interdecadal time series. After separate stochastic generation of these two series, because they are independent, they can be recombined by summation to form a replicate equivalent to the historical data. The approach was applied to generate 6-monthly rainfall totals for six rainfall stations located near Canberra, Australia. The cross correlations were preserved by carrying out the stochastic analysis using the Matalas multisite model. The results were compared with those obtained using a traditional autoregressive lag-one [AR(1)], and it was found that the new EMD stochastic model performed satisfactorily. The new approach is able to realistically reproduce multiyear–multidecadal dry and wet epochs that are characteristic of Australia’s climate and are not satisfactorily modeled using traditional stochastic rainfall generation methods. The method has two advantages over the traditional AR(1) approach, namely, that it can simulate nonstationarity characteristics in the historical time series, and it is easy to alter the decomposed time series components to examine the impact of anthropogenic climate change.


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