Spatial rangeland variability: using summary statistics and multifractal analysis to classify and monitor rangelands.

2021 ◽  
Author(s):  
Ernesto Sanz ◽  
Andrés Almeida-Ñauñay ◽  
Carlos G. Diaz Ambrona ◽  
Antonio Saa-Requejo ◽  
Margarita Ruiz-Ramos ◽  
...  
Keyword(s):  
Land Surface ◽  
Multifractal Analysis ◽  
Vegetation Index ◽  
Absolute Difference ◽  
Fluctuation Analysis ◽  
Observation Data ◽  
Summary Statistics ◽  
Land Uses ◽  
Agricultural Insurance ◽  
Multifractal Detrended Fluctuation Analysis

<p>Rangelands ecosystem comprises more than a third of the global land surface, sustaining key ecosystem services and livelihoods. Unfortunately, they suffer from severe degradation on a global scale. Tailored-monitoring of rangeland will allow us to improve their management and maintain their social-ecological systems.</p> <p>MODIS data are commonly used to calculate Normalized Differenced Vegetation Index (NDVI) and NDVI anomaly (NDVIa) to monitor rangelands. In this study, we compare summary statistics and multifractal analysis to see if using complexity based tools improves our ability to differentiate land uses and types using remote sensing.</p> <p>We collected time series using satellite data of MODIS (MOD09Q1.006) from 2000 to 2019. An area from southeastern Spain (Murcia province) of 6.25 Km<sup>2</sup> was selected. This area comprised 132 pixels with a spatial resolution of 250 x 250 m<sup>2</sup> and a temporal resolution of 8 days. This area includes irrigated and rainfed crops, shrubs and forested patches.</p> <p>Multifractal detrended fluctuation analysis (MF-DFA) focuses on measuring variations of the moments of the absolute difference of their values at different scales. This allows us to use different multifractal exponent such as generalized Hurst exponent (H(q)), and its range (ΔH) to characterize the area. Here, we have selected H(1), H(2) and ΔH, to reflect variance, persistence and multifractality, respectively. Then, we compare them to the average, standard deviation and kurtosis of our NDVI and NDVIa series.</p> <p>Our results indicate that MF-DFA, allow us to see more clearly the differences among the pixels than the summary statistics. Particularly H(1) and H(2) of NDVI reflects more precisely the vegetation profile and land uses of the selected area. On the other hand, NDVIa allows us to highlight those pixels where several uses occur, or some feature such as roads interact with NDVI. MF-DFA appears as a promising tool to classify and monitor rangelands.</p> <p><strong>Acknowledgements: </strong>The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.</p>

scholarly journals Multifractal Analysis of Hydrologic Data Using Wavelet Methods and Fluctuation Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Tongzhou Zhao ◽  
Liang Wu ◽  
Dehua Li ◽  
Yiming Ding
Keyword(s):  
Time Series ◽  
Water Level ◽  
Multifractal Analysis ◽  
Simulation Models ◽  
Northern China ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Wavelet Leaders ◽  
Detailed Characteristic ◽  
Wavelet Methods

We study the multifractal properties of water level with a high-frequency and massive time series using wavelet methods (estimation of Hurst exponents, multiscale diagram, and wavelet leaders for multifractal analysis (WLMF)) and multifractal detrended fluctuation analysis (MF-DFA). The dataset contains more than two million records from 10 observation sites at a northern China river. The multiscale behaviour is observed in this time series, which indicates the multifractality. This multifractality is detected via multiscale diagram. Then we focus on the multifractal analysis using MF-DFA and WLMF. The two methods give the same conclusion that at most sites the records satisfy the generalized binomial multifractal model, which is robust for different times (morning, afternoon, and evening). The variation in the detailed characteristic parameters of the multifractal model indicates that both human activities and tributaries influence the multifractality. Our work is useful for building simulation models of the water level of local rivers with many observation sites.

A modified Multifractal Detrended Fluctuation Analysis (MFDFA) approach for multifractal analysis of precipitation

2021 ◽  
Vol 565 ◽  
pp. 125611
Author(s):  
Jorge Luis Morales Martínez ◽  
Ignacio Segovia-Domínguez ◽  
Israel Quiros Rodríguez ◽  
Francisco Antonio Horta-Rangel ◽  
Guillermo Sosa-Gómez
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Multifractal Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Improving the analysis of the soil-plant-atmosphere system thought earth observations in large monocrop cereal sequences

2020 ◽  
Author(s):  
David Rivas-Tabares ◽  
Juan J. Martín-Sotoca ◽  
Antonio Saa-Requejo ◽  
Ana María Tarquis
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Vegetation Index ◽  
Crop Yields ◽  
Observation Data ◽  
Precipitation Regime ◽  
Agricultural Insurance ◽  
Residual Time ◽  
Earth Observations ◽  
Atmosphere System

<p>Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. The study of this interaction is feasible through the earth observations of historical data. Remote sensing data and agricultural survey work together identifying and analyzing plots with monocrop cereal sequences. In this research, we investigate the relation of the Normalized Difference Vegetation Index (NDVI) residual time series behavior relative to soil classes from Self-Organizing Maps (SOM) and the precipitation residual time series.</p><p>The midlands of Eresma-Adaja watershed (Dueros’ River basin, Spain) is historically depicted to rainfed cereal agriculture, some evidence of monocropping sequences are worrisome the water availability in the area. Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows analyzing the effect and relationships of this practice by soil type in time. For this, we treat the NDVI and precipitation time residual series as signals. The use of the Generalized Structure Function applied to these residual time series and the Hurst exponent, serve to confirm the soil properties differences from SOM and to reinforce the scaling properties of soil-climate interaction in semiarid regions for cereals in monocrop. As a result, the NDVI and precipitation series present an antipersistence behavior supporting that precipitation regime is influencing as the same manner the NDVI residual time series among complimentary factors.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>Finding for this work was partially provided by Boosting agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain. The data provided by ITACyL and AEMET is greatly appreciated.</p><p> </p>

Revealing climate and vegetation indices interactions through Cross Recurrence Techniques

2021 ◽  
Author(s):  
Andrés Felipe Almeida Ñauñay ◽  
Rosa María Benito Zafrilla ◽  
Miguel Quemada Sáenz-Badillos ◽  
Juan Carlos Losada ◽  
Ana María Tarquis Alfonso
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Observation Data ◽  
Time Step ◽  
Agricultural Insurance ◽  
Complexity Measures ◽  
Temperature And Precipitation ◽  
Multiple Variables ◽  
Over Time

<p>Grasslands are one of the world's major ecosystems groups many of them are now being acknowledged as having a multifunctional role in producing food and rehabilitating croplands, in environmental management and cultural heritage. Multiple studies showed pasture grasslands as a complex agroecological system, depending on multiple variables with a nonlinear dynamic greatly affected by climate fluctuations over time. Remote sensing methods proved to be one of the most effective techniques for monitoring variations over wide areas. In this line, vegetation indices (VIs) demonstrated to be an appropriate indicator of vegetation cover condition. This study aims to perform a method to visualize and quantify the complexity between semiarid grasslands and climate. With this goal, VIs and climate time series are analysed simultaneously with non-linear techniques to reveal the dynamic behaviour of both series over time and their interaction.</p><p>A semi-arid grassland area characterized by a Mediterranean climate with a continental character and low precipitation throughout the year were chosen. VIs time series were constructed from MODIS TERRA (MOD09Q1.006) product. Multispectral images composed by 8-days were acquired from 2002 till 2018 and four pixels with a spatial resolution of 250 x 250 m<sup>2</sup> were chosen in the selected area. Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) were calculated based on these images. Temperature and precipitation series were acquired from a near meteorological station and adapted to 8-day time step.</p><p>Cross-Recurrence plots (CRP) and recurrence quantification analysis (RQA) were performed to analyse the climate and vegetation dynamics simultaneously. To achieve this goal, several measures of complexity were computed, such as Determinism (DET), average diagonal length (LT) and entropy (ENT).</p><p>Our results showed different CRPs depending on the climate variable and the utilized VIs. Temperature and VIs CRPs showed a periodical pattern, implying the temperature seasonality over time. In contrast, precipitation and VIs CRPs showed more chaotical behaviour, due to the occurrence of extreme events and seasonal shifts. These results are quantified by the DET and ENTR values.</p><p>Our results indicate that temperature and precipitation present a dynamical complexity that is revealed in VIs response. Both indices showed different results of complexity measures, implying that MSAVI has a higher complexity than NDVI. This fact is probably due to the addition of a variable soil adjustment factor. Consequently, MSAVI should be considered as a potential alternative to NDVI in semiarid areas.</p><p><strong>Reference</strong></p><p>Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020</p><p><strong>Acknowledgements</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p>

scholarly journals Long-Range Temporal Correlations, Multifractality, and the Causal Relation Between Neural Inputs and Movements

2011 ◽  
Author(s):  
Jianbo Gao ◽  
Yi Zheng ◽  
Jing Hu
Keyword(s):  
Factor Analysis ◽  
Long Range ◽  
Multifractal Analysis ◽  
Causal Relation ◽  
Fano Factor ◽  
Fluctuation Analysis ◽  
Movement Trajectory ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Reaching Task ◽  
Temporal Correlations

Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal correlations. We find that MF-DFA and wavelet multifractal analysis (but not Fano factor analysis) clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.

Monitoring rainfed cereals under different soils and rainfall pattern

2020 ◽  
Author(s):  
Ana Maria Tarquis ◽  
David Rivas-Tabares ◽  
Juan J. Martín-Sotoca ◽  
Antonio Saa-Requejo
Keyword(s):  
Soil Properties ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Observation Data ◽  
Self Organizing Map ◽  
Agricultural Insurance ◽  
Crop Condition

<p>In most Mediterranean climate regions drought events are of great importance and their effects on rainfed crops are evident. Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. However, very few studies are found on the importance of both factors on crop condition.</p><p>Several plots were localized in the midlands of Eresma-Adaja watershed. Combining remote sensing data and agricultural survey work those with monocrop cereal sequences were identify. These plots were clustered based on which soil class were allocated based on a Self-Organizing Map and precipitation regimen elaborated in the area (Rivas-Tabares et al., 2019). Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows us to analyze the effect and relationships of soil type and rainfall with Normalized Difference Vegetation Index (NDVI) in time.</p><p>The NDVI average from both areas are statistically different in the growing season suggesting that soils and weather conditions are motivating the spectral variability of sites. The influence of soil texture and rainfall regimen related to NDVI values and interannual variability during the crop growth are discussed.</p><p><strong>References</strong></p><p>Rivas-Tabares, D., AM Tarquis, B Willaarts, Á De Miguel. 2019. An accurate evaluation of water availability in sub-arid Mediterranean watersheds through SWAT: Cega-Eresma-Adaja. Agricultural Water Management 212, 211-225.</p><p> </p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>Finding for this work was partially provided by Boosting agricultural Insurance based 465 on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain. The data provided by ITACyL and AEMET is greatly appreciated.</p>

scholarly journals Multifractal analysis of solar flare indices and their horizontal visibility graphs

2012 ◽  
Vol 19 (6) ◽  
pp. 657-665 ◽  
Author(s):  
Z. G. Yu ◽  
V. Anh ◽  
R. Eastes ◽  
D.-L. Wang
Keyword(s):  
Time Series ◽  
Solar Cycle ◽  
Detrended Fluctuation Analysis ◽  
Multifractal Analysis ◽  
Fluctuation Analysis ◽  
Horizontal Visibility ◽  
Multifractal Detrended Fluctuation Analysis ◽  
X Ray ◽  
Visibility Graphs ◽  
Detrended Fluctuation

Abstract. The multifractal properties of the daily solar X-ray brightness, Xl and Xs, during the period from 1 January 1986 to 31 December 2007 which includes two solar cycles are examined using the universal multifractal approach and multifractal detrended fluctuation analysis. Then we convert these time series into networks using the horizontal visibility graph technique. Multifractal analysis of the resulting networks is performed using an algorithm proposed by us. The results from the multifractal analysis show that multifractality exists in both raw daily time series of X-ray brightness and their horizontal visibility graphs. It is also found that the empirical K(q) curves of raw time series can be fitted by the universal multifractal model. The numerical results on the raw data show that the Solar Cycle 23 is weaker than the Solar Cycle 22 in multifractality. The values of h(2) from multifractal detrended fluctuation analysis for these time series indicate that they are stationary and persistent, and the correlations in the time series of Solar Cycle 23 are stronger than those for Solar Cycle 22. Furthermore, the multifractal scaling for the networks of the time series can reflect some properties which cannot be picked up by using the same analysis on the original time series. This suggests a potentially useful method to explore geophysical data.

MULTIFRACTAL ANALYSIS OF BRAZILIAN AGRICULTURAL MARKET

Fractals ◽  
2020 ◽  
Vol 28 (05) ◽  
pp. 2050076
Author(s):  
TATIJANA STOSIC ◽  
SALMAN ABARGHOUEI NEJAD ◽  
BORKO STOSIC
Keyword(s):  
Market Efficiency ◽  
Multifractal Analysis ◽  
Price Policy ◽  
Time Dependent ◽  
Fluctuation Analysis ◽  
Agricultural Commodity ◽  
Agricultural Market ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation ◽  
Comprehensive Study

In order to address the overall properties of the Brazilian agricultural commodity market and the intricate effects of political and economic instabilities, in this work, we provide a comprehensive study of the multifractal properties of the Brazilian commodities using multifractal detrended fluctuation analysis (MFDFA). We focus on the daily price of 12 Brazilian agricultural commodities over the last two decades, and four commodities (sugar, soybean, coffee and cattle) are also studied in terms of time-dependent MFDFA to address the effects of particular political and economic instability events. All commodities exhibit multifractal properties, which are then used to evaluate market efficiency. We find that all commodities except coffee show lower market efficiency for prices in Brazilian Reals (BRL) than in US Dollars (USD) reflecting the presence of constrains in domestic agricultural market, such as minimum price policy. From time-dependent MFDFA, we find that after the 2007/2008 food crisis, the market efficiency increases, as indicated by the changes in price dynamics towards lower persistency and weaker multifractality, with the dominance of small fluctuations.

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