Landuse Pattern And Its Drving Forces In Shandong Province

Global change has been the most active field of study these years with the problem of population, resources and environment increasingly growing. It is realized that land-use/cover change (LUCC) is the major force driving environment and climate change, and it also has significant impacts on sustainable development of human society. LUCC has been the main contents of many global change research plan since 1990s, due to its importance in the global change and sustainable development.As a major economic province, contradiction between economic development and land-use has been aggravated in Shandong province. Through the study of land-use change and its driving forces, land-use change can be understood in a timely and accurate manner, and trend, distribution and scale of land-use change can be monitored, so that we can provide basic data for urban construction, and we can serve for investigation, planning, conservation and rational utilization of land resources.Unsupervised and supervised methods for classification were used to classify land-use types, based on annual series of MODIS-NDVI combined with dem and night light data. Canonical correlation and multiple regression were used for exploring land-use pattern, change dynamics and their driving forces.Overall accuracy of land-use classification for 2001 is 84.8%, and the Kappa Coefficient is 0.83. Urban-built land, wood land and farmland respectively take up 2.13%, 23.37%, 52.53% of the whole, which is approximate with statistical data.Combination of night light data into NDVI time series greatly improved accuracy of identification of urban-built land, producer accuracy and user accuracy of which are 87.01%, 95.71% respectively.Seasonal profiles of two rotation dry croplands and two rotation rice are bimodal, while profiles of other land-use types are unimodal.Nighttime light density shows significant difference between different land-use types. Nighttime light density of urban-built land is the highest of all, and that of urban-rural crossbelt is the second highest.Elevation shows significant difference between between different landuse types. Elevation of mixed forest is the highest and Bulrash marsh, salt meadow and water have the lowest elevation.Canonical Correlation Analysis is conducted with eight land use index and fourteen potential spatial determinants involved in county level. Land-use pattern and its causes are clearly revealed and interpreted using this method. Results show that land-use pattern is determined by biophysical factors combined with socio-economic factors. In county level, distribution of farmland, grassland, urban-built land, deciduous forest and coniferous/mixed forest, as well as Land use Intensity, Patch Density and Simpsons Diversity Index all show some patterns affected by corresponding factors. Relationship also exists among certain land use index. Distribution of farmland and grassland, patch density and diversity is affected by population density, climate and distance to coastal line. Negative relationship exists between distribution of farmland and grassland. Fragmentation and diversity of the whole landscape decrease when proportion of farmland increases. Urban-built land is mainly influenced by economic level and convenience of traffic. Distribution of deciduous forest is relevant to distance to Yellow River and region-level city centers. Both distribution of coniferous/mixed forest and land-use intensity are restrained by terrain and climate.Multiple regressions on multiple scales show scale effect of spatial factors. Take land-use intensity for example, distance to coastal line, distance to city centers, annual average temperature and distance to Yellow River consistently influence land-use intensity, while other factors only have influences on certain scales. No factor shows any regular trend with increase or decrease of scales. Regression model fitting results are not so satisfying.Urban expansion, vegetation increase and farm loss are the three major aspects of land-use change in Shandong province. Urban-rural crossbelt, one rotation dry croplands, two rotation dry croplands and decidous forest take up large part of area which converts into urban-built land. One rotation dry croplands, closed shrubland, two rotation dry croplands and urban-rural crossbelt take up large part of area which converts into wood land. A large part of area which comes from farmlands is transformed into decidous forest, urban-built land, urban-rural crossbelt, mixed forest and coniferous forest.City of Linyi, Weifang, Qingdao and Yantai are the four cities which lose more farmlands. City of Linyi, Yantai, Taian, Weifang and Jining are the five cities in which vegetation increases more. City of Qingdao, Weifang, Yantai, Dongying are the four cities in which urban-built land expands more quickly.Land-use dynamic is driven by both biophysical and human factors. Canonical Correlation Analysis in county level shows that urban expansion and farm loss are positively related, which reveals the two happens closely. Urban expansion and farm loss happen more frequently where economic density is higher, distances to canal and yellow river are further, distance to coastal line is nearer, and annual average temperature, hydrological density and rate of change of Per Capita GDP are lower. Rates of change of land-use intensity and increase of vegetation are negatively correlated. Rate of increase of vegetation is restrained by such climatic factors like annual rainfall, average temperature of the coldest month, annual average temperature, and terrain. Rate of increase of vegetation is higher where population density and economic density are higher, which indicates economical development promotes vegetation restoration.Frequency statistic for single factor shows that farm loss, urban expansion and vegetation increase happen more frequently where distances to coastal line and main roads are nearer, and elevation is lower. Yellow River, Jing Hang Canal have no impacts on urban expansion. Rate of urban expansion decreases slightly when distance to city centers increases. Thresholds of distances to Yellow River, Jing Hang Canal and city centers exist when they influence farm loss and vegetation increase. When the value is under the threshold, farm loss and vegetation increase increase with the distance increasing, while they decrease with the distance increasing when the value is over the threshold.Multiple regressions on multiple scales show scale effect of spatial factors. Elevation and soil organic matter consistently influence vegetation increase. Distance to coastal line, annual rainfall and population density consistently influence farm loss. Distance to coastal line and Distance to city centers consistently influence urban expansion. Elevation, population, and distance to coastal line show regular change with increase or decrease of scales when they influence vegetation increase, farm loss and urban expansion respectively. Regression model fitting results are not so satisfying.