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This paper introduces Beijing-1 and its applications in researching the eco-environment after the Wenchuan Earthquake on 12 May 2008. The land use/cover types of a study area close to the epicentre of the earthquake, taken before and after the earthquake, were classified using Beijing-1 small satellite multispectral images. Based on the land use/cover change, several eco-environmental problems were studied, including biotope change, vegetation cover change, hydro-ecology change and land degradation. The eco-environmental status change was also evaluated. The conclusion is that the Wenchuan Earthquake affected the land use/cover and eco-environment of the research area acutely, and the eco-environmental status change was observable. Habitats have become fragmented. The vegetation has been greatly destroyed. The dammed lakes which were produced were threatening to cause floods in the downriver region. The land degradation was obvious and there was a large decrease in the Eco-environmental Index (EI). [ABSTRACT FROM AUTHOR]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

Google Earth uses aerial and satellite images from various sources as its background. It is designed to enable every person who owns a computer to easily 'fly' to aerial views of any location on the planet. However, just as with ordinary aerial or satellite images, there inevitably exist shadows in Google Earth images, making some ground objects obscured, even unidentifiable. In this paper, we describe a model we have developed for image shadow removal that uses radiative transfer theory in combination with prior knowledge to compensate for the lost information over shadow areas. We arbitrarily selected six images with severe shadows from Beijing, Shanghai, Hong Kong, Tokyo, New York and San Francisco, respectively, for algorithm testing. Our experimental results show that shadows in images can be considerably suppressed. [ABSTRACT FROM AUTHOR]

Change detection of land cover from satellite images is emphasized in China as a result of rapid land cover changes. This paper discusses a method to calculate land cover changes based on fuzzy settings. In this method, land cover is regarded as fuzzy spatial objects rather than crisp objects. The fuzzy land cover is derived based on a fuzzy classification. The degree of change is then calculated using fuzzy reasoning. In order to minimize any errors in image matching, fuzzy polygons are adopted in the reasoning. By incorporating spectral value differences, the errors in image classification can be reduced considerably. The land cover changes from 1989 to 1998 in a part of Sanya City of China are inferred as an example. The results show that this fuzzy method is able to identify the land cover changes more precisely than the crisp method. Furthermore, the transitional land cover changes can be revealed as a by‐product. [ABSTRACT FROM AUTHOR]