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陕甘宁地区植被恢复对气候变化和人类活动的响应(英文) 总被引:3,自引:2,他引:3
The "Grain for Green Project" initiated by the governments since 1999 were the dominant contributors to the vegetation restoration in the agro-pastoral transitional zone of northern China. Climate change and human activities are responsible for the improvement and degradation to a certain degree. In order to monitor the vegetation variations and clarify the causes of rehabilitation in the Shaanxi-Gansu-Ningxia Region, this paper, based on the MODIS-NDVI and climate data during the period of 2000-2009, analyzes the main charac-teristics, spatial-temporal distribution and reasons of vegetation restoration, using methods of linear regression, the Hurst Exponent, standard deviation and other methods. Results are shown as follows. (1) From 2000 to 2009, the NDVI of the study area was improved progres-sively, with a linear tendency being 0.032/10a, faster than the growth of the Three-North Shelter Forest Program (0.007/10a) from 1982 to 2006. (2) The vegetation restoration is characterized by two fast-growing periods, with an "S-shaped" increasing curve. (3) The largest proportion of the contribution to vegetation restoration was observed in the slightly improved area, followed by the moderate and the significantly improved area; the degraded area is distributed sporadically over southern part of Ningxia Hui Autonomous Region as well as eastern Dingbian of Shaanxi province, Huanxian and Zhengyuan of Gansu province. (4) Climate change and human activities are two driving forces in vegetation restoration; more-over anthropogenic factors such as "Grain for Green Project" were the main causes leading to an increasing trend of NDVI on local scale. However, its influencing mechanism remains to be further investigated. (5) The Hurst Exponent of NDVI time series shows that the vegetation restoration was sustainable. It is expected that improvement in vegetation cover will expand to the most parts of the region. 相似文献
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基于T-S模糊神经网络模型的榆林市土壤风蚀危险度评价 总被引:1,自引:0,他引:1
选择位于风沙过渡区的榆林市为研究区域,以GIS技术和T-S模糊神经网络为依托,从土壤风蚀影响因子及风蚀动力学机制出发构建区域土壤风蚀危险度模型。基于此模型,对榆林市土壤风蚀危险度空间分异特征进行了分析,结果表明:T-S模糊神经网络模型可有效地揭示出区域土壤风蚀危险度与环境之间的映射关系,为土壤风蚀预测提供依据;风力、植被、气温、降水、地形等环境要素控制着土壤风蚀危险度空间分异格局;榆林市土壤风蚀危险度空间分异格局表现为:危险度从西北向东南逐渐降低。 相似文献
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