Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.ecolind.2017.08.019 |
Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010 | |
Zhou, Wei1,3; Yang, Han1; Huang, Lu1; Chen, Chun1; Lin, Xiaosong1; Hu, Zhongjun2; Li, Jianlong3 | |
通讯作者 | Zhou, Wei |
来源期刊 | ECOLOGICAL INDICATORS
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ISSN | 1470-160X |
EISSN | 1872-7034 |
出版年 | 2017 |
卷号 | 83页码:303-313 |
英文摘要 | Remote sensing monitoring of grassland degradation will make a clear of the grassland degradation status of China. At the same time, quantitative assessment of the driving factors will benefit to the understanding of degradation mechanism and grassland degradation control. In this study, net primary productivity (NPP) and grass coverage were selected as indicators to analyze grassland degradation dynamics. And we designed a method to assess the driving force of grassland degradation based on NPP. Specifically, the potential NPP and LNPP (NPP loss because of human activities), which is the difference between potential NPP and actual NPP, were used to calculate the contribution of climate and human factors to grassland degradation, respectively. Results showed that grassland degradation area accounted for 22.7% of the total grassland area in China from 1982 to 2010. The contribution of climate change and human activities to grassland degradation was almost equilibrium (47.9% vs 46.4%). Overall, on the grassland restoration, human activities were the dominant driving factors, accounting for 78.1%, whereas the contribution of climate change was only 21.1%. However, there are obviously spatial heterogeneous on driving factors. And the contribution of climate change was larger than human activities. But for the grassland restoration, human activities were the dominant factors. Warm-dry climate was harmful to grass growth but useful restoration measurements were benefit to grassland restoration. Methods in this study can be widely used in other regions of grassland degradation evaluation. The probability distribution functions (pdfs) of habitat suitability were different for the 7 dominant grassland types. Among, the pdfs of Imperata cylindrica (Linn.) Beauv. and Themeda japonica (Willd.) Tanaka was uniform distribution and mainly distributed in the southeastern of China. The pdf of Phragmites australis (Cay.) Trin. ex Stela was normal distribution and widely spread all over of China. The pdfs of the Kobre siapygmaea C.B. Clarke and Stipa purpurea Griseb were "leptokurtic shape" and concentrated in the Tibetan Plateau. |
英文关键词 | Grassland degradation Driving mechanism Human activities Net primary productivity Probability distribution functions |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000417551800029 |
WOS关键词 | NET PRIMARY PRODUCTION ; TIBETAN PLATEAU ; LAND-USE ; INNER-MONGOLIA ; USE EFFICIENCY ; SOUTH-AFRICA ; SOIL CARBON ; MANAGEMENT ; CLIMATE ; DESERTIFICATION |
WOS类目 | Biodiversity Conservation ; Environmental Sciences |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
来源机构 | 南京大学 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/198458 |
作者单位 | 1.Chongqing Jiaotong Univ, Coll Architecture & Urban Planning, Chongqing 400074, Peoples R China; 2.Shangrao Normal Univ, Coll Hist Geog & Tourism, Shangrao 334001, Jiangxi, Peoples R China; 3.Nanjing Univ, Sch Life Sci, Nanjing 210093, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Zhou, Wei,Yang, Han,Huang, Lu,et al. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010[J]. 南京大学,2017,83:303-313. |
APA | Zhou, Wei.,Yang, Han.,Huang, Lu.,Chen, Chun.,Lin, Xiaosong.,...&Li, Jianlong.(2017).Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010.ECOLOGICAL INDICATORS,83,303-313. |
MLA | Zhou, Wei,et al."Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010".ECOLOGICAL INDICATORS 83(2017):303-313. |
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