scholarly journals A novel approach to quantify metrics of upwelling intensity, frequency, and duration

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254026
Author(s):  
Amieroh Abrahams ◽  
Robert W. Schlegel ◽  
Albertus J. Smit
Keyword(s):  
High Resolution ◽  
Time Series Data ◽  
Coastal Upwelling ◽  
Series Data ◽  
High Resolution Data ◽  
Novel Approach ◽  
Benguela Upwelling ◽  
Novel Method ◽  
Upwelling Intensity ◽  
Rigorous Method

The importance of coastal upwelling systems is widely recognized. However, several aspects of the current and future behaviors of these systems remain uncertain. Fluctuations in temperature because of anthropogenic climate change are hypothesized to affect upwelling-favorable winds and coastal upwelling is expected to intensify across all Eastern Boundary Upwelling Systems. To better understand how upwelling may change in the future, it is necessary to develop a more rigorous method of quantifying this phenomenon. In this paper, we use SST data and wind data in a novel method of detecting upwelling signals and quantifying metrics of upwelling intensity, duration, and frequency at four sites within the Benguela Upwelling System. We found that indicators of upwelling are uniformly detected across five SST products for each of the four sites and that the duration of those signals is longer in SST products with higher spatial resolutions. Moreover, the high-resolution SST products are significantly more likely to display upwelling signals at 25 km away from the coast when signals were also detected at the coast. Our findings promote the viability of using SST and wind time series data to detect upwelling signals within coastal upwelling systems. We highlight the importance of high-resolution data products to improve the reliability of such estimates. This study represents an important step towards the development of an objective method for describing the behavior of coastal upwelling systems.

scholarly journals Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

2020 ◽  
Vol 9 (4) ◽  
pp. 225 ◽  
Author(s):  
Luís Pádua ◽  
Nathalie Guimarães ◽  
Telmo Adão ◽  
António Sousa ◽  
Emanuel Peres ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Time Series Data ◽  
Cost Effective ◽  
Series Data ◽  
High Resolution Data ◽  
Fire Monitoring ◽  
Burned Areas ◽  
Technological Developments ◽  
Sentinel 2

Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

Trend analysis of time-series data: A novel method for untargeted metabolite discovery

2010 ◽  
Vol 663 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Sonja Peters ◽  
Hans-Gerd Janssen ◽  
Gabriel Vivó-Truyols
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Novel Method ◽  
Analysis Of Time Series

scholarly journals Probing the size of a magnetosphere of a young solar-like star

2008 ◽  
Vol 4 (S259) ◽  
pp. 413-414
Author(s):  
Catrina M. Hamilton ◽  
C. M. Johns-Krull ◽  
R. Mundt ◽  
W. Herbst ◽  
J. N. Winn
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Binary System ◽  
Line Profile ◽  
Main Sequence ◽  
Time Series Data ◽  
Young Stars ◽  
Series Data ◽  
Accretion Flows ◽  
Magnetospheric Accretion

AbstractWe have obtained high resolution spectra of the pre-main sequence binary system KH 15D (V582 Mon) while the star is fully visible, fully occulted, and during several ingress and egress events over the course of five contiguous observing seasons. The Hα line profile is a standard probe of the magnetospheric accretion flows on young stars such as KH 15D. We use these time series data to map out the size of the magnetosphere and find that it changes size from one observing season to the next.

scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

scholarly journals Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 99
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Scale ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Economic Recession ◽  
Series Data ◽  
Economic Activities ◽  
Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

scholarly journals Contextualizing time-series data: quantification of short-term regional variability in the San Pedro Channel using high-resolution in situ glider data

2018 ◽  
Vol 15 (20) ◽  
pp. 6151-6165 ◽  
Author(s):  
Elizabeth N. Teel ◽  
Xiao Liu ◽  
Bridget N. Seegers ◽  
Matthew A. Ragan ◽  
William Z. Haskell ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Column ◽  
Time Series Data ◽  
Series Data ◽  
Regional Variability ◽  
Modes Of Variability ◽  
Subsurface Chlorophyll Maximum ◽  
San Pedro

Abstract. Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.

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