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Analysis and prediction of vegetation dynamic changes in China: Past, present and future

机译:中国植被动态变化的分析与预测:过去,现在与未来

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摘要

Vegetation is an important link between water, atmosphere and land, and the growth of vegetation is an important indicator of ecosystem change. Therefore, it is essential to study the dynamic changes of vegetation and predict the vegetation dynamics. Based on the Normalized Difference Vegetation Index (NDVI) and statistical analysis (e.g., trend analysis methods and Hurst exponent), this study investigates the historical dynamic changes of vegetation in China, and the multi-regression model was used to construct a predict model from the perspective of water deficit. The future features were predicted under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios from 12 Coupled Model Inter-comparison Project phase 5 (CMIP5) models. The results showed that 1) most areas (80.1%) of China showed increasing trends in the annual NDVI change during 1982-2015, and the areas showing the degradation trends were mainly found in Northeast China, North Xinjiang and the Qinghai-Tibet Plateau; 2) the prediction model constructed by precipitation and reference crop evapotranspiration (ET0) can well predict the vegetation dynamics in China; and 3) the future vegetation in China will be better than that in the past, except for some areas (e.g., the northeastern and southeastern parts of China) in spring, and the dynamic changes of vegetation under RCP8.5 scenario will be greater than that under RCP4.5 scenario. Nevertheless, in spring, vegetation degradation cannot be ignored.
机译:植被是水,大气和土地之间的重要联系,植被的生长是生态系统变化的重要指标。因此,必须研究植被的动态变化并预测植被动态。基于归一化差异植被指数(NDVI)和统计分析(例如趋势分析方法和赫斯特指数),研究了中国植被的历史动态变化,多次回归模型用于构建预测模型水赤字的视角。从12个耦合模型相互比较项目阶段5(CMIP5)模型的两个代表性浓度途径(RCP4.5和RCP8.5)方案中预测了未来的功能。结果表明,1)中国大多数地区(80.1%)在1982 - 2015年期间的年度NDVI变化的趋势越来越趋势,展示了降解趋势的地区主要发现在中国东北,新疆和青藏高原北部和青藏高原; 2)通过沉淀和参考作物蒸散(ET0)构成的预测模型可以很好地预测中国的植被动态; 3)除了春天的一些地区(例如,中国东北部和东南部地区)之外,中国的未来植被将比过去更好,植被下的植被下的动态变化将大于在RCP4.5场景下。然而,在春天,植被退化不能忽视。

著录项

  • 来源
    《Ecological indicators》 |2020年第2期|106642.1-106642.11|共11页
  • 作者单位

    Southern Univ Sci & Technol Sch Environm Sci & Engn State Environm Protect Key Lab Integrated Surface Shenzhen Peoples R China|Southern Univ Sci & Technol Sch Environm Sci & Engn Guangdong Prov Key Lab Soil & Groundwater Pollut Shenzhen Peoples R China;

    Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling Shaanxi Peoples R China|Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling Shaanxi Peoples R China;

    Southern Univ Sci & Technol Sch Environm Sci & Engn State Environm Protect Key Lab Integrated Surface Shenzhen Peoples R China|Southern Univ Sci & Technol Sch Environm Sci & Engn Guangdong Prov Key Lab Soil & Groundwater Pollut Shenzhen Peoples R China;

    Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling Shaanxi Peoples R China|Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling Shaanxi Peoples R China;

    Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin Peoples R China;

    Southern Univ Sci & Technol Sch Environm Sci & Engn State Environm Protect Key Lab Integrated Surface Shenzhen Peoples R China|Southern Univ Sci & Technol Sch Environm Sci & Engn Guangdong Prov Key Lab Soil & Groundwater Pollut Shenzhen Peoples R China;

    Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Vegetation dynamics; Vegetation index; Statistical model; China; Representative concentration pathways;

    机译:植被动态;植被指数;统计模型;中国;代表浓度途径;

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