GIS-based dynamic analysis of land-use change and its ecological effects

Remote Sensing (RS) and Geographic Information System (GIS) are important technologies for the sustainable management of ecological environment. In this paper, Landuse information is obtained by integrating the maximum likelihood classification (MLC) and neural network classification (NNC) method, which can realize the automatic extraction and recognition of land-use in Beijing City from remote sensing data. The characteristics of land-use and its ecological effects are quantified by developing regional ecological value index (EVI) and transfer rate of the ecological value (TREV). The results show that land-use information obtained by the combinative methodology of MLC and NNC is accurate, the land-use change closely associated with the rapid development and the urbanization. The EVI and TREV can make quantitative evaluation for the ecological environment and its evolution direction. From 1996 to 2005, the EVI in study area shows a downward trend. This result would be useful for establishing better future management strategies for ecological environment in urban-rural transition zones.