scholarly journals Analysis of spatiotemporal variations of drought and its correlations with remote sensing-based indices via wavelet analysis and clustering methods

2021 ◽  
Author(s):  
Roghayeh Ghasempour ◽  
Kiyoumars Roushangar ◽  
V. S. Ozgur Kirca ◽  
Mehmet Cüneyd Demirel
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Condition Index ◽  
Stationary Time Series ◽  
Sensor Data ◽  
Wavelet Coherence ◽  
Spatiotemporal Pattern ◽  
Discrete Wavelet ◽  
Clustering Methods

Abstract Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT–EEMD–K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period.

scholarly journals Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

2018 ◽  
Vol 10 (11) ◽  
pp. 1678 ◽  
Author(s):  
Rajagopalan Rengarajan ◽  
John Schott
Keyword(s):  
Time Series ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spectral Response ◽  
Sensor Data ◽  
Landsat 8 ◽  
Real World Data ◽  
Large Variability ◽  
Atmospheric Compensation

Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

scholarly journals Multiscale Analysis of GPS Time Series from Non-decimated Wavelet to Investigate the Effects of Ionospheric Scintillation

2015 ◽  
Vol 16 (2) ◽  
pp. 119 ◽  
Author(s):  
Gabriela De Oliveira Nascimento Brassarote ◽  
Eniuce Menezes de Souza ◽  
João Francisco Galera Monico
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Artificial Satellite ◽  
Stationary Time Series ◽  
Ionospheric Scintillation ◽  
Discrete Wavelet ◽  
The Time Domain ◽  
Gps Time Series ◽  
Redundant Discrete Wavelet Transform

Due to the numerous application possibilities, the theory of wavelets has been applied in several areas of research. The Discrete Wavelet Transform is the most known version. However, the downsampling required for its calculation makes it sensitive to the origin, what is not ideal for some applications,mainly in time series. On the other hand, the Non-Decimated Discrete Wavelet Transform (or Maximum Overlap Discrete Wavelet Transform, Stationary Wavelet Transform, Shift-invariant Discrete Wavelet Transform, Redundant Discrete Wavelet Transform) is shift invariant, because it considers all the elements of the sample, by eliminating the downsampling and, consequently, represents a time series with the same number of coefficients at each scale. In the present paper, the objective is to present the theorical aspects of the a multiscale/multiresolution analysis of non-stationary time series from non-decimated wavelets in terms of its implementation using the same pyramidal algorithm of the decimated wavelet transform. An application with real time series of the effect of the ionospheric scintillation on artificial satellite signals is investigated. With this analysis some information and hidden patterns which can not be detected in the time domain, may therefore be explained in the space-frequency domain.

scholarly journals CLASSIFICATION OF CROPLANDS THROUGH FUSION OF OPTICAL AND SAR TIME SERIES DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 703-704 ◽  
Author(s):  
S. Park ◽  
J. Im
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Accuracy Assessment ◽  
Sensor Data ◽  
Series Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Classification Problems ◽  
Climate Conditions

Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of single data (e.g., such as cloud contamination) through multi-sensor data fusion and the use of time series data. This study investigated two areas with different environment and climate conditions: one in South Korea and the other in US. Cropland classification was conducted by using multi-temporal Landsat 5, Radarsat-1 and digital elevation models (DEM) based on two machine learning approaches (i.e., random forest and support vector machines). Seven classification scenarios were examined and evaluated through accuracy assessment. Results show that SVM produced the best performance (overall accuracy of 93.87%) when using all temporal and spectral data as input variables. Normalized Difference Water Index (NDWI), SAR backscattering, and Normalized Difference Vegetation Index (NDVI) were identified as more contributing variables than the others for cropland classification.

scholarly journals Spatiotemporal Analysis of Droughts over Different Climate Regions using Hybrid Clustering Method

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Roghayeh Ghasempour ◽  
Vahid Nourani
Keyword(s):  
Standardized Precipitation Index ◽  
Spatiotemporal Analysis ◽  
Mitigation Strategies ◽  
Drought Severity ◽  
Spatiotemporal Pattern ◽  
Discrete Wavelet ◽  
Drought Duration ◽  
Time Frequency ◽  
The North ◽  
The Standardized Precipitation Index

Abstract Drought spatiotemporal variations assessment is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. In this study, the spatiotemporal pattern of short to long-term droughts was assessed for an area with different climates. 31 stations located in Iran were considered and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were calculated during the 1951-2016 period. A hybrid methodology namely Maximal Overlap Discrete Wavelet Transform (MODWT) was applied to obtain the SPIs time-frequency properties and multiscale zoning was done via K-means clustering approach. The energy amounts of decomposed subseries via the MODWT were used as inputs for K-means approach. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the north parts where the lowest values of drought duration were obtained. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and mean SPI, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that with increasing the degree of SPI, the similarity of the stations of each cluster increased too and the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.

scholarly journals Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017)

2018 ◽  
pp. 81-89
Keyword(s):  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Condition Index ◽  
Agricultural Drought ◽  
Sensor Data ◽  
Severe Drought ◽  
Dry Land ◽  
Growth State ◽  
Agricultural Areas

Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017) Gisell Carbajal1, Bram Willems1,2 y Waldo Lavado3 1 Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Ap. Postal 14-0149, Lima, Perú 2 Centro de Competencias del Agua, Jr. Bolognesi 150 A, 303, San Miguel, Lima, Perú 3 Servicio Nacional de Meteorología e Hidrología del Perú, Jr. Cahuide 785 Jesús María, Lima 11 – Perú Recibido el 19 de noviembre del 2018. Revisado el 9 de diciembre del 2018. Aceptado el 10 de diciembre del 2018 DOI: https://doi.org/10.33017/RevECIPeru2018.0013/ Resumen En el presente trabajo se analiza la evolución espacial y temporal del Índice de Condición de la Vegetación (ICV), con el propósito de identificar patrones relevantes a la ocurrencia de eventos de sequía agrícola en Piura. El ICV provee información acerca del estado de crecimiento de la vegetación durante situaciones extremas, y se deriva del producto: valores del Índice de Vegetación de Diferencia Normalizada (NDVI) - datos del sensor MODIS (Espectrorradiómetro de Imagen de Resolución Moderada) a una resolución espacial de 1 km en el periodo 2000-2017 a bordo del satélite Terra (MOD13A3, versión 6) obtenida en su paso diurno entre las 10:30 horas y las 12:00 horas (hora local). Los patrones espaciales del ICV revelan que, para el caso de las áreas agrícolas de secano, en el 2004, el 21 % presentaron condiciones de sequía extrema y severa, mientras que en el 2007 fue el 19,5 %, el 2011 el 15,5 % y el 2014 llegó al 21 %. Por otro lado, para el caso de las áreas agrícolas por regadío, en el 2004 se vieron afectadas el 44,2 %, el 2005 fue el 55,4 %, el 2007 fue el 38,8 %, el 2011 fue el 17,1 % y el 2014 fue el 37,1 %. Descriptores: Sequía, patrones espaciales, áreas agrícolas, secano, regadío, ICV Abstract The present work, the spatial and temporal evolution of the Vegetation Condition Index (VCI) is analyzed, with the purpose of identifying patterns relevant to the occurrence of agricultural drought events in Piura. The VCI provides information about the growth state of the vegetation during extreme situations, and it is derived from the product: Normalized Difference Vegetation Index (NDVI) values - MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor data at a spatial resolution of 1 km in the period 2000-2017 on board the Terra satellite (MOD13A3, version 6) Obtained in its passage between 10:30 am and 12:00 pm (local time). The spatial patterns of the VCI reveal that, in the case of rainfed agricultural areas, in 2004, 21 % presented extreme and severe drought conditions, while in 2007 it was 19.5 %, in 2011 the 15.5 % and 2014 reached 21 %. On the other hand, in the case of irrigated agricultural areas, 44.2 % were affected in 2004, 55.4 % in 2005, 38.8 % in 2007, 17.1 % in 2011 and 37.1 % in 2014. Keywords: Drought, spatial patterns, agricultural areas, dry land, irrigated land, ICV

Nearest Neighbor-Time Series Analysis Classification of Faults in Rotating Machinery

1983 ◽  
Vol 105 (2) ◽  
pp. 178-184 ◽  
Author(s):  
W. Gersch ◽  
T. Brotherton ◽  
S. Braun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nearest Neighbor ◽  
Mean Value ◽  
Rotating Machinery ◽  
Stationary Time Series ◽  
Sensor Data ◽  
Series Analysis ◽  
Probability Of Misclassification

A unified nearest neighbor-time series analysis approach to the problem of the classification of faults in rotating machinery is developed. The procedure has an optimum minimum probability of misclassification property for normally distributed time series and near optimum misclassification properties otherwise. Examples of the classification of acceleration, pressure, and torque sensor data from stationary, locally stationary, and covariance stationary time series with mean value time functions are considered. Estimates of the probability of misclassification are computed for each situation. The underlying assumptions and properties of the nearest neighbor time series classification procedure and signature analysis procedures are compared.

scholarly journals THE ESTIMATE OF THE SPATIAL-TEMPORAL FEATURES OF VEGETATION COVER OF KAZAKHSTAN BASED ON TIME SERIES SATELLITE INDECES IN 2000-2015

2016 ◽  
Vol XLI-B8 ◽  
pp. 1067-1071
Author(s):  
I. Vitkovskaya ◽  
M. Batyrbayeva ◽  
L. Spivak
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Condition Index ◽  
Negative Trend ◽  
Positive Trend ◽  
High Productivity ◽  
Temporal Features

The article presents the evaluation of spatial-temporal characteristics of Kazakhstan arid and semi-arid areas' vegetation on the basis of time series of differential and integral vegetation indices. It is observed the negative trend of integral indices for the period of 2000-2015. This fact characterizes the increase of stress influence of weather conditions on vegetation in Kazakhstan territory during last decade. Simultaneously there is a positive trend of areas of zones with low values of IVCI index. Zoning of the territory of Kazakhstan was carried out according to the long-term values of the normalized integral vegetation index, which is characteristic of the accumulated amount of green season biomass. Negative trend is marked for areas of high productivity zones, long-term changes in the areas of low productivity zones have tend to increase. However long-term values of the area of the middle zone are insignificantly changed. Location boundaries of this zone in the latitudinal direction connects with a weather conditions of the year: all wet years, the average area is located between 46°- 49°N, and the all dry years - between 47°30'- 54°N. The map of frequency of droughts was formed by low values of the integral vegetation condition index which calculated from satellite data.

scholarly journals Large-scale vegetation responses to terrestrial moisture storage changes

2016 ◽  
Author(s):  
Robert L. Andrew ◽  
Huade Guan ◽  
Okke Batelaan
Keyword(s):  
Land Use ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Water Storage ◽  
Temporal Variations ◽  
Discrete Wavelet ◽  
Large Spatial Scale ◽  
Normalised Difference Vegetation Index ◽  
Vegetation Activity

Abstract. The Normalised Difference Vegetation Index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of NDVI anomalies in a stepwise regression. Results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, NDVI appears to be more sensitive to inter-annual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher frequencies of water storage changes. Different types of vegetation, defined by areas of land use type show distinct differences in how they respond to the changes in water storage which is generally consistent with our physical understanding. This unique method provides useful insight into how NDVI is affected by changes in water storage at different temporal scales across land use types.

scholarly journals Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

2017 ◽  
Vol 4 (1) ◽  
pp. 160741 ◽  
Author(s):  
Liang Huang ◽  
Xuan Ni ◽  
William L. Ditto ◽  
Mark Spano ◽  
Paul R. Carney ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
High Frequency ◽  
Stationary Time Series ◽  
Sensor Data ◽  
Series Data ◽  
Scaling Analysis ◽  
High Frequency Oscillations

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on–off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

scholarly journals TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 453-459 ◽  
Author(s):  
R. Das ◽  
P. K. Das ◽  
S. Bandyopadhyay ◽  
U. Raj
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Empirical Distribution ◽  
Monsoon Season ◽  
Standardized Precipitation Index ◽  
Indian Region ◽  
Agricultural Drought ◽  
Drought Vulnerability ◽  
Drought Conditions

<p><strong>Abstract.</strong> The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982&amp;ndash;2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5&amp;thinsp;km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8&amp;thinsp;km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.</p>

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