Pattern Change of Urban Green Space in Beijing from 2000 to 2010

With the rapid urbanization in Beijing, the vegetated surfaces have been increasingly replaced by impervious built surfaces. A clear recognition on the changes in green spaces pattern is vital for urban administrators and planners to optimize the urban development areas and define the ecological protection red lines. Based on the SPOT images and field investigation data of urban green spaces within the sixth ring road of Beijing in 2000, 2005 and 2010, the study investigated the dynamic changes of urban green spaces in this area, by means of GIS techniques and landscape ecology methods. The results indicated that green spaces in Beijing have decreased by 207 km2 from 2000 to 2010 at the expense of agricultural lands and waters. Although there has been some increases in urban forests and grasslands, it cannot compensate the rapid decrease of urban green spaces. The land cover types conversion occurred mainly in the areas between the fifth and sixth ring roads while the zones encircled by the fourth ring road had a little change. In addition, landscape metrics such as number of patches(NP) and shape index(SHAPE) increased, but the mean nearest-neighbor distance(MNN), aggregation index(AI) and patch cohesion index(COHESION) decreased during the period of 2000-2010. Also green landscapes became considerably isolated and fragmented. The study disclosed the obvious changes of green space size, composition and layout in urban districts of Beijing City from 2000 to 2010, and highlighted that construction of urban green space should consider not only the geometric or aesthetic effect, but also the increase of vertical green volume and optimization of spatial layout to fully develop the ecological service functions of green spaces.

Road Centrality and Landscape Spatial Patterns in Wuhan Metropolitan Area,China

Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the relationships between road centrality and landscape patterns in the Wuhan Metropolitan Area,China.The densities of centrality measures,including closeness,betweenness,and straightness,are calculated by kernel density estimation(KDE).The landscape patterns are characterized by four landscape metrics,including percentage of landscape(PLAND),Shannon′s diversity index(SHDI),mean patch size(MPS),and mean shape index(MSI).Spearman rank correlation analysis is then used to quantify their relationships at both landscape and class levels.The results show that the centrality measures can reflect the hierarchy of road network as they associate with road grade.Further analysis exhibit that as centrality densities increase,the whole landscape becomes more fragmented and regular.At the class level,the forest gradually decreases and becomes fragmented,while the construction land increases and turns to more compact.Therefore,these findings indicate that the ability and potential applications of centrality densities estimated by KDE in quantifying the relationships between roads and landscapes,can provide detailed information and valuable guidance for transportation and land-use planning as well as a new insight into ecological effects of roads.

Identifying Landscape Pattern Metrics for the Hani Terrace in Yunnan, China

This study seeks to isolate a select group of landscape metrics particularly well-suited for describing the Hani Terrace in southwest of China.We examined the response of 47 landscape metrics to a large range of imagery grain sizes.Based on a correlation analysis,the original 47 metrics were placed into 21 groups such that all metrics within a group were strongly correlated with each other with a value of more than 0.9,and were represented by a single descriptor.Using these cross-sectional metrics in the context of principal components analysis,we found that five factors explained almost 93％ of the total variation in the landscape pattern.The highest loadings for these five factors were the Splitting index （SPLIT）,Patch area distribution （AREA_CV）,Shannon＇s diversity index （SHDI）,Euclidean nearest neighbor distance distribution （ENN_AM）,and Total core area （TCA）,respectively.Considering the real landscape,we added the Patch fractal dimension distribution （FRAC_MN） as the sixth landscape pattern metric.As the scale effect of landscape pattern metrics we design to investigate how a suite of commonly use landscape metrics respond to changing grain size.Based on the anlasis,we determined that the best domain of scale to characterise the Hani Terrace pattern metrics is between 40m and 45m.Through the fractal method,we found that the characteristic scale of the Hani Terrace is the same as the scale domain of metrics,among the 40m and 45m.We suggest that the majority of the patterns in the Hani Terrace landscapes,indeed for all those in southwest China,can be described effectively with these six metrics.