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China's domestic urban planning only worked on researches of urban space control, the scope definition of urban development is not clear enough. The purpose of this study is to present a new urban growth boundary (UGB) delimitation method which combined land suitability evaluation (LSE) and cellular automata (CA). This method gave credence to LSE's advantage in sustainable land use, and CA's advantage in objective dynamic simulation. The ecological limitation areas were defined by LSE, which were regarded as the restricted areas of urban growth; meanwhile, it was taken as an important model input to guide intensive land allocation in urban growth model (CA model). The future urban growth scenarios were predicted by CA model and the corresponding UGB lines were delineated by ArcGIS 10.1. The results indicated that this method had good performance in Ningbo's urban growth simulation. When compared to the planned UGB in urban master planning, the simulated UGBs under port development and regulated scenarios showed more intensive and suitable spatial layout of land. Besides, the simulated UGB under regulated scenario had the most reasonable space structure and the largest ecological protection effect among the UGBs. Hence, the simulated UGBs were superior to the planned UGB. The study recommends that this UGB delimitation method can promote sustainability of land development and ecological environment in Chinese cities. [ABSTRACT FROM AUTHOR]

Multiple policy projects have changed land use and land cover (LULC) in China’s rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements. [ABSTRACT FROM AUTHOR]