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author:

Wei, L. (Wei, L..) [1] | Wang, X.-Q. (Wang, X.-Q..) [2] (Scholars:汪小钦) | Chen, Y.-Z. (Chen, Y.-Z..) [3] (Scholars:陈芸芝)

Indexed by:

Scopus PKU CSCD

Abstract:

Fuzhou located in the east of Fujian province is one of the areas holding the most abundant wetland resources in Fujian province, which has a large number of coastal wetlands. The coastal wetland ecosystems provides habitat and nursery areas for many commercially important fish and crustaceans. They also play important roles in shoreline protection, chemical buffering, water quality maintenance, recreation and education, and as reservoirs of genetic materials. During the past decades, the distribution and amount of coastal wetlands have been changed due to human activities, urbanization development, and other economic issues imposed on it, which brought on a series of serious damage to wetland resources. Thus, there is an increasing need to master the distribution and dynamic change of coastal wetlands, which is important to protection, rational development and utilization of the coastal wetlands in Fuzhou. It will play a promational role on wetland resources investigation, long-term monitoring and scientific management for the province and even the national in the future, and has a strong practical value and broad application prospects. In this study, in order to monitor the change of the distribution and amount of the coastal wetlands in Fuzhou, Landsat 7 ETM+ image in 2000 and ALOS image in 2009 were used to classify the coastal wetland resources in Fuzhou. In the classification procedure involved, the images in 2000 and 2009 were classified into 9 land use types, including aquatic farm, mudflat, paddy field, river, shallow water, sandy beach, mudflat, marshy meadow, reservoir pond and non-wetland, using the conventional unsupervised Maximum Likelihood Classification Algorithm. To improve information extraction accuracy from the coastal wetlands, the crude results of unsupervised classification were refined and optimized. Then the distribution and the spatio-temporal changes of the coastal wetlands were analyzed and compared using GIS analysis function. The results showed that natural wetlands in the east coast of Fuzhou had reduced while artificial wetlands have increased significantly in the past 10 years. Aquatic farm had the largest change with its area increasing about 2 699.82 ha, which were mainly transformed from non-wetland types, mudflats and paddy fields. The transformation of paddy fields were mainly changed from non-wetlands, of which the overall area increased by 2 484.21 ha. As for aquatic farm, river, shallow water, sandy beach and mudflat, they all had a certain scale of areas that had been converted to construction lands. The changes of coastal wetlands were primarily caused by the increasingly large areas of aquaculture and the development of agriculture and urban construction.

Keyword:

Change; Coastal wetland; Fuzhou city; Spatial analysis

Community:

  • [ 1 ] [Wei, L.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, Fujian, China
  • [ 2 ] [Wang, X.-Q.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, Fujian, China
  • [ 3 ] [Chen, Y.-Z.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, Fujian, China

Reprint 's Address:

  • 魏兰

    [Wei, L.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, Fujian, China

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Source :

Wetland Science

ISSN: 1672-5948

CN: 22-1349/P

Year: 2011

Issue: 3

Volume: 9

Page: 251-256

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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