On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

Militino, Ana F.; Moradi, Mehdi; Ugarte, M. D.

doi:10.3390/rs12061008

Cited by 64 publications

(41 citation statements)

References 56 publications

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“…The CS test is also a member of the non-parametric family, just like the Mann-Kendall test. The CS test has limited use but is very powerful for trend analysis in datasets [65]. The CS test is applied to numerous circumstances to understand the trueness of the obtained values, where the substructure of this technique is the binomial distribution.…”

Section: Cox and Stuart (Cs) Testmentioning

confidence: 99%

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

et al. 2021

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This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen’s slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan.

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Section: Cox and Stuart (Cs) Testmentioning

confidence: 99%

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

et al. 2021

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“…The breaks for additive seasonal and trend (BFAST) method has been widely used to detect seasonal, gradual, and abrupt changes in monthly NDVI time series. The breaks detected in the seasonal component represent a land cover change; as such, BFAST has been applied in many different regions to describe abrupt changes to land cover as a result of climate change or anthropogenic activity [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Guo

Wang

et al. 2021

Remote Sensing

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In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

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“…This is a non-parametric sign test for detecting trends in independent, time-ordered data [11]. Although this test is not as powerful as the Mann-Kendall test [39], the computational effort of this test is lower (increases linearly with the sequence size). The main steps to perform the Cox-Stuart test are shown in Algorithm 1.…”

Section: Increasing/decreasing Trendsmentioning

confidence: 99%

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Arellano

Nguyen

Hinton

et al. 2021

Preprint

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With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology and visualisation tool based on data analytics that allows the practitioner to anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine's settings are needed (tool replacement, repositioning the axis, etc). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.

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On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

Cited by 64 publications

References 56 publications

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

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