Exploring the spatial and temporal dynamics of land use - with special reference to China

Land-use and land-cover change have large impacts on our natural environment. Changes in our natural environment directly influence our living conditions through the possibilities that we have to obtain a safe food in a healthy environment, but also in aesthetic ways through our perception of landscapes and diversity. To avoid unfavorable consequences of land-use changes, systematic approaches for land-use intervention are developed for policy makers. Systematic intervention in the dynamics of land-use systems is impossible without a proper understanding of the driving factors in these systems and their behavior.The general objective of this thesis is to present a set of methodologies to explore the spatial and temporal dynamics of land-use systems at the regional level. These methods provide insight into the spatial variability of land use, indicate which (proximate) factors determine the spatial distribution of land use and identify potential, near-future 'hot-spots' of land-use change. The identification of 'hot-spots' of land-use change, i.e., areas that are expected to face high rates of land-use change, is essential to focus follow-up in-depth research and policy measures to address the appropriate issues and geographical locations.Case-studies for the application of the developed methodologies are the countries of China and Ecuador and the Island of Java in Indonesia. Most attention in this thesis is given to the developments in China, whereas the country of Ecuador is used for model development and sensitivity analysis while the case of Java allows for a validation of the model performance. China and the island of Java face large pressures on agricultural land resources due to urban and industrial expansion and limited capacities for agricultural expansion. At the same time, economic growth and urbanization result in an increasing demand for agricultural products. In Ecuador agricultural expansion is still the major land-use change process.The first step in studying land-use change is the quantification of the relations between land use and its driving factors. These relations are derived from a spatial analysis of actual land-use patterns, which reflect the results of historic land-use changes. Correlation and regression analysis are used to identify the most important explanatory variables of the land-use pattern from a large set of biophysical and socio-economic variables that are generally considered to be of potential importance for the distribution of land use. An integrated set of biophysical and socio-economic variables was found to best describe the spatial distribution of the different land-use types. Specific attention is given to the influence of the scale of analysis on the relations between the distribution of land-use and the biophysical and socio-economic explanatory variables. Both the resolution of the data and the extent of the study area have a major impact on the derived relations. Relations obtained at a certain scale of analysis may therefore not be directly applied at other scales or in other areas.The relations between the land-use distribution and the derived sets of biophysical and socio-economic explanatory variables are used to parameterize a land-use change model. This land-use change model, the CLUE modeling framework (the Conversion of Land Use and its Effects), allocates changes in demand for different land-use types at the national level to individual grid cells. Changes in the relative coverage of land-use types in the different grid-cells are calculated in an iterative procedure using the earlier established spatial relations after a change in one of the explanatory variables of the land-use pattern or a change in competitive advantage for a land-use type. Changes in explanatory variables include changes in population density, labor force or urbanization, while changes in competitive advantage originate from changes in demand at the national level. For every year changes are calculated until the allocated changes match the demand at the national level for the different land-use types. For every grid-cell, allocation is constrained by the total land area available in the grid cell, so that competition between the land-use types is explicitly taken into account. A multi-scale approach is followed to account for the scale dependencies of the relations between the land-use pattern and its explanatory variables. This approach provides also a balance between bottom-up effects, resulting from local conditions and top-down effects as a result of changes at national and regional scales. An analysis of the behavior and sensitivity of the model is made for the country of Ecuador. Different scenarios are tested to illustrate the realistic simulation of interconnectivity between regions and bottom-up / top-down interactions in land-use change.The application of the CLUE model for China has a minimum spatial resolution of 32x32 km. The most important land-use conversions in China, caused by urbanization, desertification and afforestation, are simulated for a scenario over the period 1990-2010. This scenario is based upon expected developments in demand for land-use types described in literature. The spatially explicit results allow an analysis of the consequences of a decrease in cultivated area and related production capacity. A preliminary analysis shows that the average production capacity of the lost arable lands is somewhat less than the average production capacity of all agricultural lands together. Regional differences are, however, large.Additionally, a scenario of shifts in the relative importance of different agricultural crops is simulated. The results indicate regions where larger shares of high value crops within the cropping system are to be expected.Large changes are expected in China's livestock sector as a consequence of increasing demands for animal products. Based upon the CLUE modeling framework a livestock module was developed that allows the exploration of changes in the spatial distribution of livestock production systems. The livestock module is directly linked to the simulations of land-use change so that changes in land cover directly influence livestock distributions. Two scenarios are evaluated: a baseline scenario and a scenario that assumes improved management of grasslands to stimulate livestock production in the pastoral regions. For both scenarios most increases in livestock numbers are expected in China's agricultural region. Poor transportation systems and vast distances, causing low prices and high costs of inputs, are the main constraints for more intensive use of the pastoral region.A validation of model performance was made for the island of Java based upon historic data. Simulations from 1979 to 1994 indicated that the model could adequately simulate the pattern of land-use change. The performance on a cell-to-cell basis was reasonable whearas the overall pattern of change was well simulated. Additionally a scenario of land-use change between 1994 and 2010 was simulated assuming a continuation of urbanization on Java. The results identified Java's fertile lowlands as areas where most intensive land-use changes are expected to take place.Apart from changes in the land area of different land-use types also the land-use intensity is subject to changes. Especially when opportunities for agricultural expansion are limited, land intensification is an option to increase agricultural production. A separate analysis was made of the different components of the land-use system that influence the agricultural production capacity of China. Included are changes in agricultural area, multiple cropping index, input use, technical efficiency and technological change. Research methodologies to analyze the spatial and temporal patterns of these land-use system components are all based on semi-empirical analyses of spatial variability in relation to biophysical and socio-economical explanatory variables. The results indicate that different processes and patterns of land-use change are found in various parts of the country. Inefficiencies in the use of agricultural inputs and relatively low input-use intensities, especially in some of the rural, less endowed, western regions, indicate that in these regions of China increases in grain yield are well possible. Awareness of the importance of spatial variability in agricultural development, rather than analyzing developments at the national level only, will help policy makers and scientists to focus future agricultural development programs on the appropriate areas. In-depth studies of the causes of retarded agricultural development, which are still needed as a follow-up to the studies reported here, will help to design proper measures for specific regions.The introduced and applied methodologies provide an essential link between existing research tools needed for adequate land-use negotiations. Projectory studies at the national level that identify land-use change problems or extrapolate trends are used as direct inputs to formulate scenarios for the CLUE simulations. Land-use change trajectories for different scenario conditions are useful inputs for research aiming at the design of alternative land-use configurations or intensities. Policy measures can be derived from scenario conditions resulting in trajectories leading into the direction of desired land-use configurations, to be designed by land-use planners or with the help of optimization models. Furthermore, a confrontation of desired land-use configurations with the simulated land-use trajectories of the CLUE model can lead to a more realistic definition of the objectives and constraints of land-use planning activities.<A HREF="http://www.gis.wau.nl/~landuse1/papers.html"><font size="2">www.gis.wau.nl/~landuse1/papers.html</font>