Wetland changes in the Amur River Basin: Differing trends and proximate causes on the Chinese and Ru

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论文题目:	Wetland changes in the Amur River Basin: Differing trends and proximate causes on the Chinese and Russian sides
英文论文题目:	Wetland changes in the Amur River Basin: Differing trends and proximate causes on the Chinese and Russian sides
第一作者:	毛德华
英文第一作者:	D. H. Mao
联系作者:	王宗明
英文联系作者:	Z. M. Wang
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发表年度:	2021
卷:	280
期:
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摘要:	According to the United Nations Sustainable Development Goals (SDGs), understanding the extent of wetlands, their change trends and the proximate causes is important for the conservation of wetlands and endangered waterfowls. Here we studied the world's ninth largest river basin, the Amur River Basin (ARB), with a land area of 2.08 million km(2). Our objectives were to address the information deficiencies of spatially explicit wetland distributions and their changes and to quantify the proximate causes of these changes in various periods in the ARB. A hybrid approach combining object-based and hierarchical decision-trees classification (HOHC) was applied to Landsat series images to obtain multitemporal land cover datasets from 1980 to 2016. Further quantitative analysis revealed that the ARB held 184,561 km(2) of wetlands in 2016, accounting for 9% of the whole basin area. Among these, 59% of the wetlands were identified on the Russian side, while 40% were on the Chinese side, and 1% were on the Mongolian side. The ARB lost 22% of its wetland (52,246 km2) from 1980 to 2016, with a consistent net loss from 1980 to 2010 but an area gain from 2010 to 2016. Human activities dominated the consistent wetland losses on the Chinese side of the ARB, of which cropland expansion was the primary proximate cause of wetland loss (69%). Conversely, the wetlands on the Russian side had consistent losses from 1980 to 2010 followed by a gain from 2010 to 2016, which could be attributed to climate change. These quantified data will inform decision-making on wetland conservation and benefit scientific studies depending on spatially explicit wetland information.
英文摘要:	According to the United Nations Sustainable Development Goals (SDGs), understanding the extent of wetlands, their change trends and the proximate causes is important for the conservation of wetlands and endangered waterfowls. Here we studied the world's ninth largest river basin, the Amur River Basin (ARB), with a land area of 2.08 million km(2). Our objectives were to address the information deficiencies of spatially explicit wetland distributions and their changes and to quantify the proximate causes of these changes in various periods in the ARB. A hybrid approach combining object-based and hierarchical decision-trees classification (HOHC) was applied to Landsat series images to obtain multitemporal land cover datasets from 1980 to 2016. Further quantitative analysis revealed that the ARB held 184,561 km(2) of wetlands in 2016, accounting for 9% of the whole basin area. Among these, 59% of the wetlands were identified on the Russian side, while 40% were on the Chinese side, and 1% were on the Mongolian side. The ARB lost 22% of its wetland (52,246 km2) from 1980 to 2016, with a consistent net loss from 1980 to 2010 but an area gain from 2010 to 2016. Human activities dominated the consistent wetland losses on the Chinese side of the ARB, of which cropland expansion was the primary proximate cause of wetland loss (69%). Conversely, the wetlands on the Russian side had consistent losses from 1980 to 2010 followed by a gain from 2010 to 2016, which could be attributed to climate change. These quantified data will inform decision-making on wetland conservation and benefit scientific studies depending on spatially explicit wetland information.
刊物名称:	Journal of Environmental Management
英文刊物名称:	Journal of Environmental Management
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参与作者:	D. H. Mao, Y. L. Tian, Z. M. Wang, M. M. Jia, J. Du and C. C. Song
英文参与作者:	D. H. Mao, Y. L. Tian, Z. M. Wang, M. M. Jia, J. Du and C. C. Song

Wetland changes in the Amur River Basin: Differing trends and proximate causes on the Chinese and Ru

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