Simulation Of High-resolution Urban Population Density Based On Remote Sensing And GIS

Population is a major issue confronting the contemporary world, and also is the most important factor affected the modernization. Urban is the most densely populated areas. Grasping the spatial distribution of urban population timely and accurately is the key to solve the problems facing the city. At present, there are two principle methods about the study of population spatial distribution. One method uses demographic data to establish relationship between factors such as population and natural environment, and pixelize statistical population data. This method usually applied to large-scale study. Another method use remote sensing image, extracting information related to population spatial distribution to establish the relationship between information and population distribution and simulate the spatial distribution of population.Although study on the spatial distribution of population has been considerable development, but there are still many deficiencies, such as low spatial resolution and temporal resolution. In this paper, Beibei District for example, backed by GIS technology, using high resolution QuickBird imagery, simulated day and night spatial distribution of urban population respectively. Research content and achievements describe as follows:Build urban land use classification system which applies to high-resolution simulation of urban population density. Introduce the principle, interpretation difficulties and methods of information extraction. Probe the building height information extraction theory based on remote sensing images, establish the relationship between building height and the shadow of the study area, and analyze the tolerance of the extraction results.Making use of field survey data and interpretation results to model the night spatial distribution of urban population, and analyze the error of simulated results. Introduce land use attraction weight in the traffic forecasting model to simulate the day spatial distribution of urban population. Transforming research results to grid format, which realizes the high-resolution (spatial resolution and temporal resolution) of the urban population density simulation. Finally, take air pollution diffusion for an example to illustrate the application of these research results in the urban Management.