2009),"Fuzzy model-based assessment and monitoring of desertification using MODIS satellite imagery", Engineering Computations, Vol. 26 Iss 7 pp. 745 -760 Permanent link to this document: http://dx.

PurposeThe purpose of this paper is to better understand landscape dynamics in arid and semi‐arid environments. Land degradation has recently become an important issue for land management in western China. The oasis ecosystem is especially sensitive to environmental disturbances, such as abnormal/extreme precipitation events, variations in the water supply from the upper watersheds, fluctuations in temperature, etc. Satellite remote sensing of terrestrial ecosystems can provide us with the temporal dynamics and spatial distributions of green cover over large areas of landscape. Seasonal green cover data are especially important in assessing landscape health (e.g. desertification, rate of urban sprawl, natural disturbances) in arid and semi‐arid regions. In this study, green cover data are derived from vegetation indices retrieved from moderate resolution imaging spectroradiometer (MODIS) sensors onboard the satellite Terra.Design/methodology/approachSatellite images recorded during the period from April 2000 to December 2005 are analyzed and the spatial distribution and temporal changes of the Ejin Oasis quantified.FindingsThis study shows that it is possible to derive important parameters linked to landscape sensitivity from MODIS and the derived imagery, such as normalized difference vegetation index (NDVI) time‐series data. Such a MODIS‐based time‐series monitoring system is particularly useful in arid and semi‐arid environments. The results of landscape sensitivity analysis prove the effectiveness of the method in assessing landscape sensitivity from the years 2001‐2005.Practical implicationsThe novel strategy used in this investigation is based on the T‐S fuzzy model, which is in turn based on fuzzy theory and fuzzy operations.Originality/valueSimulation results based on fuzzy models will help to improve the monitoring techniques used to evaluate land degradation and to estimate the newest tendency in landscape green cover dynamics in the Ejin Oasis.

PurposeThis study seeks to develop a systematic means of identifying regression models using a complex regression model with a statistical method.Design/methodology/approachAs a widely adopted statistical scheme for analyzing multifactor data, regression analysis provides a conceptually simple algorithm for examining functional relationships among variables. This investigation assesses the proposed relationship using a sample of data in regression analysis and then estimates the fit using statistics. Furthermore, several algorithms and added variable plots are presented to obtain an appropriate regression model and the relationship between response variables y, p and explanatory variables x0,x1,x2, … ,xp.FindingsThe proposed statistical scheme is demonstrated by the analysis of experimental data on internal waves, in which the results can well illustrate what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.Practical implicationsIn previous studies, field observations of internal waves were carried out. Owing to the limit of stationary measurement in situ, sufficient experiment data were not easily obtained. On the other hand, data collected by laboratory experiments express more information on wave mechanism, such as energy dissipation, mixing efficiency, and stratification thickness in a stratified layer fluid system. In present study, the authors supply valuable experiment data and analytic method, which will be a great contribution to the geophysical fluid dynamics. The results illustrate well what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.Originality/valueMore recently, it has been proposed that internal wave mixing may contribute significantly to internal mixing in the ocean and hence has an important influence on world climatic changes. Based on the statistical algorithm and regression model, the reduction in internal wave energy can be predicted on a sloping bottom due to frictional effect. Since, interaction between internal waves and uniform slopes has occurred in an estuary, a lake or in the ocean, the results available in this paper would benefit future study on internal wave hydrodynamics.

PurposeThis study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.Design/methodology/approachThe proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.FindingsA suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ2 tests are greater than 0.05. However, the Pearson χ2 value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.Practical implicationsA comparison of the four predictive powers: R2, max‐rescaled R2, the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.Originality/valueThe goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.