Literature DB >> 20953697

Characterizing wetland change at landscape scale in Jiangsu Province, China.

Chi Xu1, Sheng Sheng, Wen Zhou, Lijuan Cui, Maosong Liu.   

Abstract

Human activities produced great impacts on wetlands worldwide. Taking Jiangsu Province, China, as a representative wetland region subject to extensive human activities, the aim of this study is to understand the conversion trajectory and spatial differentiation in wetland change from a multi-scale perspective. Based on multi-temporal Landsat images, it was found that the natural wetlands decreased by 11.2% from 1990 to 2006 in Jiangsu Province. Transition matrices showed that the conversion of natural wetlands to human-made wetlands (mostly aquaculture ponds) was the major form of natural wetland reduction, accounting for over 60% of the reduction. Percentage reduction and area reduc tion of natural wetlands were respectively quantified within different wetland cover zones using a moving window analysis. Average percentage reduction showed a decreasing tendency with increasing wetland cover. The high-cover and mid-cover zone presented the largest area reduction at the scales of 1-2 km and 4-8 km, respectively. Local hotspots of natural wetland reduction were mapped using the equal-interval and quantile classification schemes. The hotspots were mostly concentrated in the Lixiahe marshes and the coastal wetland areas. For the area reduction hotspots, the quantile classification presented larger area and more patches than the equal-interval classification; while an opposite result was shown for the percentage reduction hotspots. With respect to the discontinuous distribution of the natural wetlands, area reduction could be more appropriate to represent reduction hotspots than percentage reduction in the study area. These findings could have useful implications to wetland conservation.

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Year:  2010        PMID: 20953697     DOI: 10.1007/s10661-010-1735-6

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


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