scholarly journals PAttern REtrieval or deNegation Testing Scheme (PARENTS) v.1.0 – Identifying the degree of presence of given patterns in spatial time series

2019 ◽  
Author(s):  
Benjamin Müller ◽  
Matthias Bernhardt ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Surface Characteristics ◽  
Observation Time ◽  
Soil Parameters ◽  
Simple Test ◽  
Testing Scheme ◽  
Spatially Distributed ◽  
Pattern Retrieval ◽  
Test Algorithm

Abstract. The large number of spatially distributed earth observation products, i.e. time series of surface emissions and reflectances at different wavelengths with increasing spatial resolution, contribute to the derivation of surface characteristics, e.g. vegetation or soil parameters in the environmental sciences. These derivatives usually build upon complex algorithms consisting of atmospheric corrections and process descriptions. The testing scheme presented here seeks a different approach to identifying these surface characteristics that control the generation of such observation time series. Spatially distributed patterns of these characteristics of different persistence usually dominate parts of a time series because of their very specific reaction to and interaction with environmental influences. We test these characteristics' patterns for their existence in a rotated vector space of elementary patterns derived from a principal component analysis of an observational time series. With the result of this test we can then make valid assumptions, e.g. with regard to the importance of the surface properties for the emittance or reflectance, or their spatial uncertainties. We demonstrate the functionality of this rather simple test algorithm for a synthetic and fully traceable example, and an application in a medium hydrological catchment for a time series of thermal satellite data. Possible future applications for this scheme are the prioritization and improvement of model input, data assimilation, or the evaluation and validation of model output.

scholarly journals Key soil parameters affecting the survival of Panax notoginseng under continuous cropping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wumei Xu ◽  
Fengyun Wu ◽  
Haoji Wang ◽  
Linyan Zhao ◽  
Xue Liu ◽  
...  
Keyword(s):  
Soil Ph ◽  
Pearson Correlation ◽  
Principal Component ◽  
Panax Notoginseng ◽  
Soil Parameters ◽  
Continuous Cropping ◽  
Poor Growth ◽  
Available N ◽  
The Poor ◽  
Replant Problem

AbstractNegative plant-soil feedbacks lead to the poor growth of Panax notoginseng (Sanqi), a well-known herb in Asia and has been used worldwide, under continuous cropping. However, the key soil parameters causing the replant problem are still unclear. Here we conducted a field experiment after 5-year continuous cropping. Sanqi seedlings were cultivated in 7 plots (1.5 m × 2 m), which were randomly assigned along a survival gradient. In total, 13 important soil parameters were measured to understand their relationship with Sanqi’s survival. Pearson correlation analysis showed that 6 soil parameters, including phosphatase, urease, cellulase, bacteria/fungi ratio, available N, and pH, were all correlated with Sanqi’s survival rate (P < 0.05). Principal component analysis (PCA) indicated that they explained 61% of the variances based on the first component, with soil pH being closely correlated with other parameters affecting Sanqi’s survival. The optimum pH for Sanqi growth is about 6.5, but the mean soil pH in the study area is 5.27 (4.86–5.68), therefore it is possible to ameliorate the poor growth of Sanqi by increasing soil pH. This study may also help to reduce the replant problem of other crops under continuous cropping since it is widespread in agricultural production.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

scholarly journals Systematic Water Fraction Estimation for a Global and Daily Surface Water Time-Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2675
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Spatial Resolution ◽  
Pure Water ◽  
Feature Space ◽  
Observation Time ◽  
Temporal Information ◽  
Landsat 8 ◽  
Water Fraction ◽  
Classification Probability

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

Correlating geologic and seismic data with unconventional resource production curves using machine learning

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. O39-O47 ◽  
Author(s):  
Ryan Smith ◽  
Tapan Mukerji ◽  
Tony Lupo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Oil And Gas ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Full Time ◽  
Data Types ◽  
Production Time ◽  
Well Production ◽  
Unconventional Resource

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data. We are then able to predict the well-production time series, with 65%–76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.

scholarly journals Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

2021 ◽  
Vol 13 (20) ◽  
pp. 4123
Author(s):  
Hanqi Wang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Fengyu Xu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Evaluation Indexes ◽  
Learning Network ◽  
Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

scholarly journals Exploratory study of factors associated with human brucellosis in mainland China based on time-series-cross-section data from 2005 to 2016

2018 ◽  
Author(s):  
Yun Lin ◽  
Tao Zhang ◽  
Xingyu Zhang
Keyword(s):  
Time Series ◽  
Rural Areas ◽  
Associated Factors ◽  
Mainland China ◽  
Principal Component ◽  
Dynamic Panel Data ◽  
Control Measures ◽  
Medical Expenditure ◽  
Human Brucellosis ◽  
Control Effect

AbstractObjectiveMany studies focused on reasons behind the increasing incidence and the spread of human brucellosis in mainland China, yet most of them lacked comprehensive consideration with quantitative evidence. Hence, this study aimed to further investigate the epidemic mechanism and associated factors of human brucellosis in China so as to provide suggestions on more effective countermeasures.MethodsData of human brucellosis incidence and some associated factors in economy, animal husbandry, transportation and health were collected at provincial level from 2005-2016. Time series plot and cluster analysis were first used to visualize incidence levels and categorize provinces based on their incidence level and epidemic trend of human brucellosis. Furthermore, according to the characteristics of data, the dynamic panel data model in combination with supervised principal component analysis was proposed to explore the effects of associated factors on human brucellosis.Results① The incidence rate of human brucellosis has increased threefold (from 1.41 in 2005 to 4.22 in 2016) in mainland China. Incidence rates in the north have always been higher than those in the south, but the latter also experienced an upward trend especially in the recent five years. ② The 31 provinces of mainland China were categorized into three clusters, and each cluster had its own characteristics of incidence level and epidemic trend. ③ Public health expenditure and rural medical expenditure proportion were potential protective factors of human brucellosis, with attribute risks of −0.74 and −1.04 respectively. Other factors (such as amount of sheep, total length of highways, etc.) exhibited relatively trivial effects.ConclusionsThe epidemic status of human brucellosis has changed in both spatial and temporal dimensions in recent years. Apart from those traditional control measures, more attention should be paid to the improvement of medical healthcare especially in rural areas in order to strengthen the control effect.

scholarly journals Space-time variability of hydrological drought and wetness in Iran using NCEP/NCAR and GPCC datasets

2010 ◽  
Vol 14 (10) ◽  
pp. 1919-1930 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Reanalysis Data ◽  
Space Time ◽  
Hydrological Drought ◽  
Time Variability ◽  
Climate Conditions ◽  
Good Agreement

Abstract. Space-time variability of hydrological drought and wetness over Iran is investigated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the Global Precipitation Climatology Centre (GPCC) dataset for the common period 1948–2007. The aim is to complement previous studies on the detection of long-term trends in drought/wetness time series and on the applicability of reanalysis data for drought monitoring in Iran. Climate conditions of the area are assessed through the Standardized Precipitation Index (SPI) on 24-month time scale, while Principal Component Analysis (PCA) and Varimax rotation are used for investigating drought/wetness variability, and drought regionalization, respectively. Singular Spectrum Analysis (SSA) is applied to the time series of interest to extract the leading nonlinear components and compare them with linear fittings. Differences in drought and wetness area coverage resulting from the two datasets are discussed also in relation to the change occurred in recent years. NCEP/NCAR and GPCC are in good agreement in identifying four sub-regions as principal spatial modes of drought variability. However, the climate variability in each area is not univocally represented by the two datasets: a good agreement is found for south-eastern and north-western regions, while noticeable discrepancies occur for central and Caspian sea regions. A comparison with NCEP Reanalysis II for the period 1979–2007, seems to exclude that the discrepancies are merely due to the introduction of satellite data into the reanalysis assimilation scheme.

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