Land use and cover change detection and modelling for North Ningxia, China1 Change detection results According to the above processing procedures, the county-level land use and cover change detection was achieved and thematically mapped in figure 2. The change data are illustrated in table 1. It is recognised that 11.74% of the total territory had changed in the period 1987-1999, among which the predominant change is the farmland extension (471km2 in area, 49.44% of the total change). Urban and rural built-up increases are apparent (35.8 and 68.33km2 respectively in surface area, 3.58% and 7.34% of the total change). Water-body has an increase of about 49km2. However, the fastest change here is the Yellow River narrowing. The running course had decreased by 83.83km2 at an reduction rate of 6.10% .
 Figure 2: County-level land use and cover change map of North Ningxia from 1987 to 1999 Panel analysis, a multivariate regression modelling Land use and cover changes can not take place independently but have certain linkages with the human activities and mutations in natural conditions (e.g., climate change). Understanding the dynamics of land use and cover change has increasingly been recognised as one of the key research imperatives in global environmental change research (Lambin et al, 1999; Geist et al, 2001). The monitoring of such changes would be most relevant and useful when it is accompanied by the understanding of the forces driving change processes. This task could be calibrated by a statistical modelling, 'Panel analysis', which links the changes in dependent variables (e.g., land use changes) during a certain interval of time with the changes in independent variables (e.g., human activities) in the corresponding interval of time and across a large number of localities (Lambin, 1994). This analysis postulates a linear relationship between the dependent and independent variables and can be mathematically expressed as follows (Kleinbaum et al, 1976 and 1998; Lambin, 1994): where, Y is the dependent variable, i.e., land use change(s), Xn are the independent variables, i.e., driving forces, or rather, human activities, ฿0 is a constant (or intercept) and ฿n are regression coefficients and E a random error component. Such modelling can discriminate the causes or driving forces governing land cover changes (Lambin,1994; Lambin et al, 2000; Merten, 1997; Serneel et al, 2001; Wu et al, 2001). The county-level land cover change data (table 1) and the corresponding changes in county-level socio-economic and meteorological data from 1988 to 1999 (from the Ningxia Statistical Yearbook, 1989, 2000) were incorporated and inputted into SYSTAT, a software for multivariate analysis. Taking into account the land use changes as dependent and socio-economic and meteorological data as independents, within a confidence level of 0.05, the modelling results are shown in table 2.
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