trend estimation
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2021 ◽  
Vol 13 (24) ◽  
pp. 5018
Author(s):  
Xueying Li ◽  
Wenquan Zhu ◽  
Zhiying Xie ◽  
Pei Zhan ◽  
Xin Huang ◽  
...  

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.


Author(s):  
Seedari Ujwala Rani ◽  
Naveen P. Singh ◽  
Pramod Kumar ◽  
Rabindra Nath Padaria ◽  
Ranjit Kumar Paul

The study was carried out for ten Agro climatic zones in Karnataka state in India. The temperature and rainfall data were used for analysis from 1979-2019 which is about 40 years. Understanding spatiotemporal rainfall pattern, Rainfall Anomaly Index which is drought indicator technique was  used to classify the positive and negative severities in rainfall anomalies. The RAI ranges below 0.2 are considered as dry zone. The analysis resulted that, all zones are falls in category of dry zone with range of 0.2 to 0.4. For past five years, North Eastern Transition Zone was noted maximum times falling in the range of RAI below 0.2 and near to zero. Statistical techniques like linear trend estimation, R square was used for trend estimation across annual, seasonal to identify the variation in the temperature across different zones. The meaningful statistically significant achieves when there is r2≥0.65 and p≤0.05. It was analysed that, hilly Zone experienced decreased trend in both minimum and maximum temperature in all seasons which ultimately reflected in annual temperature to decrease with high R square values.


Antibiotics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1267
Author(s):  
Bernard Hernandez ◽  
Pau Herrero-Viñas ◽  
Timothy M. Rawson ◽  
Luke S. P. Moore ◽  
Alison H. Holmes ◽  
...  

In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians’ adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it provides fundamental knowledge to the wide range of stakeholders to revise and potentially tailor existing guidelines to the specific needs of each hospital.


Author(s):  
Fernanda Valente ◽  
Márcio P. Laurini
Keyword(s):  

Author(s):  
Tingting Liu ◽  
Xiaotong Li ◽  
Chen Bao ◽  
Michael Correll ◽  
Changehe Tu ◽  
...  
Keyword(s):  

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