Influence Of Autocorrelation Research Articles

The Relationship Between Vegetation and Terrain Factor Based on Topographic Correction

Vegetation can reflect the ecological characteristics of soil, water and temperature, which is of great significance for protecting the ecological environment. Vegetation growth is affected by topographic features such as elevation, slope and aspect. Due to the autocorrelation of topography and vegetation index, the current research does not fully consider the influence of autocorrelation on the relationship between vegetation index and topographic factors. Therefore, it is necessary to eliminate the autocorrelation of topography and vegetation index through topographic correction, so as to truly reveal the relationship between vegetation and topography. Therefore, this study takes the loess hilly region as the research area, and conducts terrain correction based on Landsat8 OIL data to reveal the relationship between vegetation and different terrain factors ( elevation, slope, aspect ). The main contents and conclusions of this paper are as follows: (1) Analyze the spatial distribution of the corrected vegetation index ( NDVI, EVI ) and topographic factors (elevation, slope, aspect ), explore the law between vegetation index and topographic factors. (2) The response of the corrected vegetation index to topographic features tends to be more consistent. With the increase of elevation, it increases first and then decreases, reaching the maximum at 980~990 m. With the increase of slope, it shows a trend of continuous growth, reaching the maximum at 30 ~ 36 °. With the increase of aspect, there is a trend of growth-reduction-growth, reaching the maximum at 65 °.

Influence of autocorrelation on the topology of the climate network.

Different definitions of links in climate networks may lead to considerably different network topologies. We construct a network from climate records of surface level atmospheric temperature in different geographical sites around the globe using two commonly used definitions of links. Utilizing detrended fluctuation analysis, shuffled surrogates, and separation analysis of maritime and continental records, we find that one of the major influences on the structure of climate networks is due to the autocorrelation in the records, which may introduce spurious links. This may explain why different methods could lead to different climate network topologies.

Estimating the effect of temporally autocorrelated environments on the demography of density‐independent age‐structured populations

Summary In age‐structured populations, environmental autocorrelations influence the long‐run population growth rate as well as the variance in future population size. We used the concept of individual reproductive value to examine how autocorrelated environments affect the dynamics of age‐structured populations, leading to transparent interpretations and estimation of these effects. Environmental autocorrelation is expressed by the covariances between mean individual reproductive values for each age class and size of the same age class with stochastic components depending only on noise matrices from previous years. Thus, if an age class that is large in a given year also tends to perform better than the temporal average of that class in the contribution per individual to future population sizes, then the environmental autocorrelation will be positive. We use a simple model with temporal autocorrelation in recruitment rate to illustrate the theory through analytical results as well as stochastic simulations. We show how the effect of environmental autocorrelation, the term included in the long‐run growth rate, as well as influencing the variance of future population size, can be estimated using a combination of individual‐based demographic data and time series of fluctuations in age composition without estimating autocorrelations and cross‐correlations of large numbers of age‐specific vital rates. The method was applied to data from four mammal species. These analyses revealed that the influence of autocorrelations in the environmental noise on the dynamics of these species was small and in two populations almost negligible. The theoretical explorations as well as the empirical estimates indicate that the temporal scaling of the environmental autocorrelation must be long to substantially affect the long‐term population growth rate. The white noise approximation is therefore often very accurate.

On the influence of autocorrelation and GARCH-effects on goodness-of-fit tests for copulas

Knowing the multivariate stochastic dependence between random variables is of crucial importance for many finance applications. To check the adequacy of copula assumptions by which stochastic dependencies can be described, goodness-of-fit (gof) tests have to be carried out. These tests require (serially) independent and identically distributed (i.i.d.) data as input. Due to autocorrelations and time-varying conditional volatilities, this prerequisite is usually not fulfilled by financial market returns. Within a simulation study, we analyze the influence of these violations of the i.i.d.-prerequisite on the rejection rates of gof tests. We find that in many cases the rejection rates are significantly different for non-i.i.d. data input than for adequately filtered data input. This finding questions the conclusions of early empirical studies applying gof tests for copulas to data without adequately filtering it before. Only in the majority of those constellations that in general yield very low rejection rates, no significant differences have been revealed.

The influence of autocorrelation on the ability to detect trend in hydrological series

AbstractThis study investigated using Monte Carlo simulation the interaction between a linear trend and a lag‐one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial correlation alters the variance of the estimate of the Mann–Kendall (MK) statistic; and the presence of a trend alters the estimate of the magnitude of serial correlation. Furthermore, it was shown that removal of a positive serial correlation component from time series by pre‐whitening resulted in a reduction in the magnitude of the existing trend; and the removal of a trend component from a time series as a first step prior to pre‐whitening eliminates the influence of the trend on the serial correlation and does not seriously affect the estimate of the true AR(1). These results indicate that the commonly used pre‐whitening procedure for eliminating the effect of serial correlation on the MK test leads to potentially inaccurate assessments of the significance of a trend; and certain procedures will be more appropriate for eliminating the impact of serial correlation on the MK test. In essence, it was advocated that a trend first be removed in a series prior to ascertaining the magnitude of serial correlation. This alternative approach and the previously existing approaches were employed to assess the significance of a trend in serially correlated annual mean and annual minimum streamflow data of some pristine river basins in Ontario, Canada. Results indicate that, with the previously existing procedures, researchers and practitioners may have incorrectly identified the possibility of significant trends. Copyright © Environment Canada. Published by John Wiley & Sons, Ltd.

The effect of autocorrelation on the results of visually analyzing data from single-subject designs.

Single-subject research designs are used to conduct clinical research and outcome evaluation in occupational therapy. Confusion exists regarding the best method to analyze and interpret single-subject data. One hundred graphs displaying the results of published single-subject research were examined to determine the influence of autocorrelation on the visual inferences made by the original investigators. The graphs were selected from 20 articles published over 10 years in seven rehabilitation journals. The data were extrapolated and lag 1 autocorrelation coefficients computed for both the baseline and treatment phases. Data analysis focused on two issues: (a) whether a relationship existed between the amount of autocorrelation present in a graph and the conclusion on the basis of visual analysis and (b) whether the amount of autocorrelation varied across different phases of the single-subject graphs. When a significant degree of autocorrelation was present, researchers using visual analysis were more likely to conclude that there was no clinically significant change in performance. Autocorrelation values were significantly higher in the treatment phases of the single-subject designs. Additional research is needed to establish a set of decision rules to assist clinicians in using visual analysis to evaluate the results of single-subject research.

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The influence of autocorrelation on OC-lines of compliance criteria for concrete strength

For the calculation of Operating Chacteristic (OC) lines of compliance criteria, it is necessary to introduce a model that reflects the stochastic nature of the relevant variable as realistically as possible. As far as concrete is concerned, it was found that a significant autocorrelation exists between consecutive strength values. As shown in the paper, this aspect has important consequences for the resulting probabilities of acceptance as its effect may be represented as a reduction of the effective number of observations. In order to stimulate the consideration of autocorrelation by people concerned with compliance control of concrete, both exact and approximate methods that take into account autocorrelation are presented and the practical application is illustrated.

The influence of autocorrelation in signature extraction—an example from a geobotanical investigation of Cotter Basin, Montana

The presence of positive serial correlation (autocorrelation) in remotely sensed data results in an underestimate of the variance-covariance matrix when calculated using contiguous pixels. This underestimate produces an inflation in F statistics. For a set of Thematic Mapper Simulator data (TMS), used to test the ability to discriminate a known geobotanical anomaly from its background, the inflation in F statistics related to serial correlation is between 7 and 70 times. This means that significance tests of means of the spectral bands initially appear to suggest that the anomalous site is very different in spectral reflectance and emittance from its background sites. However, this difference often disappears and is always dramatically reduced when compared to frequency distributions of test statistics produced by the comparison of simulated training sets possessing equal means, but which are composed of autocorrelated observations.