Investigating the dynamics of wetland landscape pattern in Beijing from 1984 to 2008

Abstract

The landscape pattern of Beijing wetlands has undergone a significant change as a result of natural and artificial elements. Supported by remote sensing and GIS technology, using multi-temporal TM images from 1984 to 2008 in Beijing, this paper analyzed the dynamic characteristics of wetlands landscape pattern through selected typical indices including patch area, patch average area, fractal dimension index, diversity, dominance, contagion indices and the spatial centroids of each wetlands type were calculated. Finally, the paper explored the evolution mode and driving factors of wetland landscape pattern. The results were obtained as follows: the total wetland area increased during the period 1984-1996, then decline from 1996 to 2004. The wetland area in 1994 accounted for only 47.37% of that in 2004. The proportion of artificial wetland area was larger than that of natural wetland. The proportion of reservoir wetland was 33.50% to 53.73% and had the maximum average area. pond and paddy field wetland type with the least average area accounted for 16.46% to 45.09% of the total wetland area. The driving forces of the natural river wetland were mainly natural elements; its fractal dimension index was greater than the others. The Shannon diversity index of wetland landscape increased from 1.11 in 1992 to 1.34 in 2004, indicating that the difference between proportions of each wetland type decreased and areas of each wetland type were evenly distributed. The contagion index went down from 65.59 to 58.41, indicating that the connectivity degraded. Miyun Reservoir had the largest area and its area change had a great impact on the location of the centroid. Wetland resources degenerated gradually from the joint effects of natural and artificial factors. During the period 2006-2008, the precipitation increased and the drought condition was relieved. The government implemented series of positive policies to save water resources, and the wetland area increased.

Variation in the structure of subtidal landscapes in the NW Mediterranean Sea

“…Greater values of contagion occur in the presence of few large, contiguous patches, whereas many small and scattered patches generate lower values. In addition, we calculated mean patch size for each type of habitat and the global mean patch size among all habitats, in order to examine patterns of variability in habitat fragmentation (Zhang et al 2011) In order to account for potentially confounding effects due to substratum heterogeneity, we measured topographic complexity according to the chain method (Luckhurst & Luckhurst 1978). One end of a 10 m long stainless-steel chain was randomly placed along the transect and allowed to sink to the bottom, while released in a straight line.…”

Assessment of wetland fragmentation in the middle reaches of the Heihe River by the type change tracker model

“…reaches of the Heihe River With the influence of climate change and human activities, the wetland area decreased continuously (Niu et al, 2012;Verones et al, 2013), and it became fragmentation (Gibbes, 2009;Wang et al, 2011;Zhang et al, 2011). It was found that mean patch density, perimeter area ratio and patch shape fragmentation indices increased in different extent, and largest patch index, aggregation index and mean patch size decreased in the previous researches using landscape pattern indices (Li and Zhao, 2010;Jiang et al, 2013).…”

“…The area change and rate of different wetland types can be determined by the dynamic degree model, but it is incapable to analyze the spatially fragmentation process. The centroid model used to detect the direction of wetland degradation (Yue et al, 2003;Zhang et al, 2011) also could not reveal the fragmentation in the wetland interior. The CA model could be used to simulate and forecast the changing process of wetland landscape pattern in the pixel level, but it is insufficient for land type in variable state and transformation among wetland types (Zhang et al, 2009).…”

“…Researchers built time serial wetland data for detecting the degradation of regional wetlands in an effective manner (Nielsen et al, 2008). The dynamic degree model and centroid model were established to analyze the wetland change (Yue et al, 2003;Wen et al, 2011;Zhang et al, 2011), but these methods applied the vector algorithm and failed to combine raster data effectively. Most of the landscape pattern indices also lack spatial information and fragmentation process (Wang et al, 2011;Jiang et al, 2013).…”

Landscape Pattern Evolution Processes of Wetlands and Their Driving Factors in the Xiong’an New Area of China

“…The study of the dynamic change characteristics and evolution trends of wetland landscape patterns is conducive to the protection, restoration, planning, and management of wetland landscapes, and it is an important basis for the construction of regional ecological security patterns. To date, several studies have focused on employing remote sensing (RS) and geographic information system (GIS) technologies to calculate quantitative indicators and analyze the characteristics of landscape patterns at different scales [ 10 , 11 , 12 , 13 , 14 , 15 ]. Wetland landscape pattern evolution research methods have primarily included qualitative description, landscape ecological chart overlays, and landscape pattern quantitative analysis [ 16 ].…”