Journal of Geographical Sciences >
Quantifying driving forces of urban wetlands change in Beijing City
Received date: 2011-05-10
Revised date: 2011-12-08
Online published: 2012-04-15
Supported by
National Natural Science Foundation of China, No.41171318; No.41001160; The Fundamental Research Funds for the Central Universities, the Beijing Plan Program of Science and Technology, No.D08040600580801; International Program for Cooperation in Science and Technology, No.2009DFA91710
The decision tree and the threshold methods have been adopted to delineate boundaries and features of water bodies from LANDSAT images. After a spatial overlay analysis and using a remote sensing technique and the wetland inventory data in Beijing, the water bodies were visually classified into different types of urban wetlands, and data on the urban wetlands of Beijing in 1986, 1991, 1996, 2000, 2002, 2004 and 2007 were obtained. Thirteen driving factors that affect wetland change were selected, and gray correlation analysis was employed to calculate the correlation between each driving factor and the total area of urban wetlands. Then, six major driving factors were selected based on the correlation coefficient, and the contribution rates of these six driving factors to the area change of various urban wetlands were calculated based on canonical correlation analysis. After that, this research analyzed the relationship and mechanism between the main driving factors and various types of wetlands. Five conclusions can be drawn. (1) The total area of surface water bodies in Beijing increased from 1986 to 1996, and gradually decreased from 1996 to 2007. (2) The areas of the river wetlands, water storage areas and pool and culture areas gradually decreased, and its variation tendency is consistent with that of the total area of wetlands. The area of the mining water areas and wastewater treatment plants slightly increased. (3) The six factors of driving forces are the annual rainfall, the evaporation, the quantity of inflow water, the volume of groundwater available, the urbanization rate and the daily average discharge of wastewater are the main factors affecting changes in the wetland areas, and they correlate well with the total area of wetlands. (4) The hydrologic indicators of water resources such as the quantity of inflow water and the volume of groundwater are the most important and direct driving forces that affect the change of the wetland area. These factors have a combined contribution rate of 43.94%. (5) Climate factors such as rainfall and evaporation are external factors that affect the changes in wetland area, and they have a contribution rate of 36.54%. (6) Human activities such as the urbanization rate and the daily average quantity of wastewater are major artificial driving factors. They have an influence rate of 19.52%.
Key words: urban wetlands change; remote sensing; driving forces; correlation analysis; Beijing
JIANG Weiguo, WANG Wenjie, CHEN Yunhao, LIU Jing, TANG Hong, HOU Peng, YANG Yipeng . Quantifying driving forces of urban wetlands change in Beijing City[J]. Journal of Geographical Sciences, 2012 , 22(2) : 301 -314 . DOI: 10.1007/s11442-012-0928-z
Azous A L, Horner R M, 2000. Wetlands and Urbanization: Implications for the Future. Boca Raton, FL, USA: Lewis Publishers.
Beijing Forestry Survey and Design Institute (BFSDI), 2008. Report of Beijing Wetland Resource Inventory. (in Chinese)
Chen Jin, Li Dongqing, Men Qingzhou et al., 2009. The status and the causes of wetland degradation in the source regions of Yangtze River and Yellow River. Journal of Arid Land Resources and Environment, 23(4): 43-49. (in Chinese)
Cheng Qian, Wu Xiuju, 2006. Landscape pattern change and its driving forces in Xixi National Wetland Park since 1993. Chinese Journal of Applied Ecology, 17(9): 1677-1682. (in Chinese)
Cozar A, Garcia C M, Galvez J A et al., 2005, Remote sensing imagery analysis of the lacustrine system of Ibera wetland (Argentina). Ecological Modeling, 186(1): 29-41.
Deng J L, 1989. Introduction to gray system theory. The Journal of Gray System, 1: 11-24.
Forgette T A, Shuey J A, 1997. A comparison of wetland mapping using SPOT satellite imagery and national wetland inventory data for a watershed in northern Michigan. In: Trettin C C (ed.). Northern Forested Wetlands; Ecology and Management. CRC Lewis Publishers, Boca Raton, Florida, USA, 61-70.
Gluck M, Rempel R, Uhlig P W C, 1996. An evaluation of remote sensing for regional wetland mapping applications. Forest Research Report No.137. Ontario Forest Research Institute, Sault Ste Marie, Ontario, Canada, 33.
Guo Yuedong, He Yan, Deng Wei et al., 2004. Analysis of hydrological vulnerability characteristics and influence factors of Zhalong riparian wetlands. Wetland Science, 2(1): 47-53. (in Chinese)
Huang Hua, He Huiting, Fan Xin et al., 2010. Super-resolution of human face image using canonical correlation analysis. Pattern Recognition, 43(7): 2532-2543.
Huang Ying, Zhou Yunxuan, Wu Wen et al., 2009. Shanghai urban wetland extraction and classification with remote sensed imageries based on a decision tree model. Journal of Jilin University (Earth Science Edition), 39(6): 1156-1162. (in Chinese)
Jiang Weiguo, Li Jing, Wang Wenjie et al., 2005. An analysis of changes and driving forces of wetland using RS and GIS in Liaohe River Delta. Remote Sensing for Land & Resources, 65(7): 62-65. (in Chinese)
Jiang Weiguo, Liu Jing, Liu Li et al., 2009. Study on the classification of urban wetlands based on RS and GIS. The International Society for Optical Engineering, v 7498, 2009, MIPPR 2009 - Remote Sensing and GIS Data Processing and Other Applications.
Johnston R M, Barson M M, 1993. Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification. Australian Journal of Marine and Freshwater Resources, 44(2): 235-252.
Kuang Wenhui, Liu Jiyuan, Shao Quanqin et al., 2009. Spatio-temporal patterns and driving forces of urban expansion in Beijing Central City since 1932. Journal of Geo-information Science, 11(4): 428-435. (in Chinese)
Li Lingling, Gong Huili, Zhao Wen, 2008. Change detection and driving factor analysis of Beijing wetlands from 1996 to 2006. Journal of Capital Normal University (Natural Science Edition), 29(3): 95-101. (in Chinese)
Long Hualou, Li Xiubin, 2001. Land use pattern in transect of the Yangtse River and its influential factors. Acta Geographica Sinica, 56(4): 417-425. (in Chinese)
Luo Xianxiang, Deng Wei, He Yan et al., 2002. Driving forces of runoff changes for marshy rivers in Sanjiang Plain. Acta Geographica Sinica, 57(5): 603-610. (in Chinese)
Magnus Borga, 2001. Canonical correlation a tutorial, available from
Min Xu, Pakorn Watanachaturaporn, Pramod K Varshney et al., 2005. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3): 322-336.
Niu Zhenguo, Gong Peng, Chen Xiao et al., 2009. Chinese mapping of wetlands and associated preliminary analysis of geographical features based on remote sensing in China. Science in China (Series D), 39(2): 188-203.
Rao B R M, Dwivedi R S, Kushwaha S P S, 1999. Monitoring the spatial extent of coastal wetlands using ERS-1 SAR data. International Journal of Remote Sensing, 20(13): 2509-2517.
Shanmugam P, Ahn Y, Sanjeevi S, 2006. A comparison of the classification of wetland characteristics by linear spectral mixture modeling and traditional hard classifiers on multi-spectral remotely sensed imagery in southern India. Ecological Modeling, 194: 379-394.
Smith M G, Spencer T, Murray A L et al., 1998. Assessing seasonal vegetation change in coastal wetlands with airborne remote sensing: an outline methodology. Mangroves Salt Marshes, 2: 15-28.
Sun Caizhi, Zeng Qingyu, Liu Yuyu, 2010. Spatial and temporal change and its driving forces of the Raoyanghe wetlands based on RS and GIS. Research of Soil and Water Conservation, 17(2): 150-159. (in Chinese)
Tang Huiquan, 2009. Analysis on the gray correlation between wetland area change and land use type in Sanjiang Plain. Territory & Natural Resources Study, (3): 50-51. (in Chinese)
Tracy Boyer, Stephen Polasky, 2004. Valuing urban wetlands: A review of non-market valuation studies. Wetlands, 24(4): 744-755.
Wang Xuelei, Ning Longmei, Yu Jing et al., 2008. Changes of urban wetland landscape pattern and impacts of urbanization on wetland in Wuhan city. Chinese Geographical Science, 18(1): 47-53.
Wang Zhaoli, Chen Xiaohong, Zeng Lvchun et al., 2006. Application of gray system theory to analyze the driving force system of land use change in Shenzhen City. China Population, Resources and Environment, 16(6): 124-128. (in Chinese)
Wilen B O, Bates M K, 1995. The US-fish-and-wildlife services national wetland inventory project. Vegetation, 118(1/2): 153-169.
Zhou Huiping, Jiang Hong, Zhou Guomo et al., 2010. Monitoring the change of urban wetland using high spatial resolution remote sensing data. International Journal of Remote Sensing, 31(7): 1717-1731.
/
〈 | 〉 |