II III 4.2. Methods of geostatistical analysis 4.2.1. Autocorrelation 4.2.2. Spatial autocorrelation 4.2.3. Kriging with an external drift 4.3. Analysis of the relationship between vegetation change and its explanatory factors 4.3.1. Correlation coefficient 4.3.2. Multiple correlation coefficient 4.3.3. Partial correlation coefficient 4.4. Modelling relationship between vegetation patterns and explanatory factors 4.4.1. Simple linear regression model 4.4.2. Multiple linear regression model

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... . Problem of non-stationarity by analysing spatial relationship 4.4.4. Moving window regression 4.4.5. Geographically weighted regression 4.5. Assessment of modelling accuracy 4.5.1. Root Mean Square Error (RMSE) 4.5.2. Standard error 4.5.3. Spatial autocorrelation for accuracy assessment 4.6. Evaluation of land cover change and its driving forces 4.6.1. Background for discrimination between climate-induced and humaninduced vegetation change 4.6.2. Identification of climate and anthropogenic signals in the vegetation time-series 4.6.3. Analysis of regression residuals for identification of areas experiencing anthropogenic impact 5. Analysis of climatic conditions 5.1. Network of climate stations in the study region 5.2. Modelling spatial patterns in climate parameters 5.3. Statistical analysis of climate data. 5.3.1. The inter-annual variability of precipitation and temperature. 5.3.2. Trends in climatic parameters 5.4. Discussion and conclusion IV 6. Within-season dynamics of vegetation activity and their relationship to climate factors 6.1. Spatial distribution of Normalized Difference Vegetation Index (NDVI) and climatic factors in the study area 6.2. Average characteristics of NDVI 6.3. Temporal behaviour of climatic factors and vegetation within the growing season 6.4. Within-season relationship between NDVI and precipitation 6.4.1. Stratification of NDVI-precipitation relationship by land cover type 6.4.2. Stratification of NDVI-precipitation relationship by vegetation communities 6.5. Within-season relationship between NDVI and temperature 6.6. Spatial patterns in NDVI-climate relationship 6.7. Inter-annual variations in within-season NDVI-climate relationship 6.8. Discussion and conclusion 7. Inter-annual change in vegetation activity and its relation to climate 7.1. Patterns in monthly time-series 1982-2001 7.2. Inter-annual relationship between NDVI and climatic parameters 7.2.1. Analysis of spatially averaged NDVI versus precipitation 7.2.2. Relationship between spatially averaged NDVI and temperature. 7.2.3. Spatial patterns in inter-annual NDVI-climate relationship 7.3. Quantifying temporal variability in vegetation conditions 7.3.1. Standard deviation of NDVI 7.3.2. Variance of NDVI values over the study period 7.3.3. Dependence of cv NDVI on the relief 7.4. Discussion and conclusion 8. Spatial response of vegetation cover to climatic factors 8.1. Growing season relationship between NDVI and precipitation 8.1.1. NDVI-rainfall correlation coefficients 8.1.2. NDVI-rainfall relationships by vegetation type 8.1.3. Influence of growing season rainfall on NDVI-rainfall correlation 8.1.4. Spatial patterns in NDVI anomalies and their relationship to rainfall 8.2. Within-season relations between NDVI and rainfall V 8.2.1. Spatial patterns in intra-annual dynamic of NDVI and climate parameters 8.2.2. Within-season NDVI-rainfall correlation coefficients 8.2.3. Influence of vegetation type on within-season relations between NDVI and rainfall 8.2.4. Influence of precipitation amount on NDVI-rainfall relations 8.3. Growing season relationship between temperature and NDVI 8.3.1. NDVI-temperature correlation coefficients 8.3.2. NDVI-temperature correlation coefficients by vegetation type 8.3.3. Influence of annual rainfall on NDVI-temperature correlation 8.4. Within-season relationship between NDVI and temperature 8.4.1. General patterns in the NDVI-temperature correlation 8.4.2. Influence of cover types on within-season relationship between NDVI and temperature 8.5 Discussion and conclusion 9. Application of the geographically weighted regression to modelling relationship between vegetation patterns and climate factors 9.1. Problem of non-stationarity in modelling spatial relationship and approaches to overcome it 9.2. Reducing uncertainty in modelling NDVI-precipitation relationship: a comparison between OLS and GWR regression techniques 9.2.1. Global OLS regression model and its deficiencies 9.2.2. Stratification of NDVI-precipitation relationship by land cover type 9.2.3. Local variability in relationship between vegetation and precipitation 9.2.4. Analysis of regression residuals 9.3. Analysis of temporal variations in NDVI-precipitation relationship using GWR 9.3.1. Variations in the relationship strength 9.3.2. Trends in NDVI-rainfall relationship and their linkages to land use/land cover change 9.4. Discussion and conclusion 10. Detection of climate-induced and human-induced vegetation change 10.1. Trends in spatially averaged NDVI 10.1.1. Trends in growing season NDVI VI Table 10.5. Simple, partial and multiple correlation coefficients between NDVI and explanatory variables for period 1985-2001 XII