Qiandao Lake (Xin’an Jiang reservoir) plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010–2013 were obtained for analysis. Cluster analysis (CA) was applied to classify the 12 sampling sites into three groups (Groups A, B and C) and the 12 monitoring months into two clusters (April-July, and the remaining months). Discriminant analysis (DA) identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA) identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

This study presents a practical methodology to monitor the spatiotemporal characteristics of urban expansion in response to rapid urbanization at the provincial scale by integrating remote sensing, urban built-up area boundaries, spatial metrics and spatial regression. Sixty-seven cities were investigated to examine the differences of urbanization intensity, urbanization patterns and urban land use efficiency in conjunction with the identification of socio-economic indicators and planning strategies. Planning proposals to allocate the urbanization intensity among different-sized cities by considering sustainable urban development were also explored. The results showed that the urban area of Zhejiang Province expanded from 31,380 ha in 1980 to 415,184 ha in 2010, indicating that the area of the urban region expanded to more than 13-times the initial urban area. The urban built-up area boundaries became more complex and irregular in shape as the urban area expanded throughout the entire study period. Rapid urban population growth and economic development were identified as significant in stimulating the urban expansion process. However, different-sized cities exhibited marked differences in urban development. Small cities experienced the rapidest urbanization before 2000. Large cities, which are estimated to have the highest urban land use efficiency, had the most dramatic sprawl in urban area at the beginning of the 21st century. Promoting the development of large cities to mega-cities is recommended in Zhejiang Province to ensure sustainable urban development with consideration of land resource preservation.

To investigate the effect of arable land protection policies in China, a practical framework that integrates geographic information systems (GIS), soil quality assessment and landscape metrics analysis was employed to track and analyze arable land transformations and landscape changes in response to rampant urbanization within the Ningbo region (China) from 2005 to 2013. The results showed that arable land loss and degradation have continued, despite the development of a comprehensive legal framework for arable land protection. The implementation of arable land protection policies is judged to be effective, but not entirely successful, because it guarantees the overall amount of arable land but does not consider soil quality and spatial distribution. In addition, there are distinct variations in arable land change dynamics between two temporal intervals. From 2005–2009, the transformation of arable land was diversified, with intensified conversion among arable land, built-up land, water and orchards. Moreover, many new arable land parcels were adjacent to built-up land, and are in danger of being occupied again through urban sprawl. By 2009–2013, most of the arable land was occupied by urban expansion, whereas a majority of newly increased arable land was reclaimed from coastal tideland. Although the newly increased arable land was contiguous and far from the urban area, it is of poor quality and has limited use. The permanent loss of high-quality arable land due to intensified urban sprawl may threaten sustainable development and food security on a larger scale.

This research employed impervious surface analysis to examine process of urban expansion around the Hangzhou Bay, China, by integrating Landsat data obtained over two different periods between 1987 and 2009(i.e., 1987, 2001 and 2009). The amount of impervious surface was quantified by Hyper Spectral Mixture Analysis (HSMA) for each year. We developed this technique to cope with mixed pixel problem in coastal region of this Bay. Results showed that south of the Hangzhou Bay witnessed rapid impervious surface expansion(non-industrial and industrial) in the period 2001to 2009 in comparison to the north part. We found that most urban lands were connected together through developing impervious surface. As a result, it generated a large area of impervious surface along coastal of the Hangzhou Bay. This situation may induce environmental problems in this Bay.