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2016 , Vol. 18 >Issue 8: 1069 - 1076

DOI: https://doi.org/10.3724/SP.J.1047.2016.01069

地理空间分析综合应用

三江源生态工程实施前后长江源区宏观生态状况变化分析

  • 刘璐璐 , 1, 2 ,
  • 曹巍 1 ,
  • 邵全琴 , 1, *
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  • 1. 中国科学院地理科学与资源研究所 陆地表层格局与模拟院重点实验室,北京 100101
  • 2. 中国科学院大学,北京 100049
*通讯作者：邵全琴（1962-）,女,江苏常州人,研究员,博士生导师,研究方向为GIS与生态信息。E-mail:

作者简介：刘璐璐（1988-）,女,山东人,博士生,研究方向为GIS与生态信息。E-mail:

收稿日期: 2015-08-24

  要求修回日期: 2015-11-16

  网络出版日期: 2016-08-10

基金资助

国家自然科学基金面上项目（41571504）

国家科技支撑计划项目(2013BAC03B04)

三江源智慧生态畜牧业平台建设项目（2015-SF-A4-1）

中国科学院特色研究所培育建设项目（TSYJS05）

中国科学院科技服务网络计划项目（KFJ-EW-STS-005-04）

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Change of Ecological Condition in the Headwater of the Yangtze River Before and After the Implementation of the Ecological Conservation and Construction Project

  • LIU Lulu , 1, 2 ,
  • CAO Wei 1 ,
  • SHAO Quanqin , 1, *
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  • 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: SHAO Quanqin, E-mail:

Received date: 2015-08-24

  Request revised date: 2015-11-16

  Online published: 2016-08-10

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《地球信息科学学报》编辑部 所有
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摘要

基于NDVI时空序列数据,利用GLOPEM-CEVSA模型,本文估算并分析了长江源区1997-2012年植被覆盖度及植被净初级生产力时空变化特征,并在此基础上评估了生态工程实施前、后长江源区宏观生态状况变化。结果表明：工程实施后,长江源区宏观生态状况显著好转,植被覆盖度及植被净初级生产力明显增加。从多年平均值来看,工程实施后,植被覆盖度好转区域面积占植被区总面积的72.10%,净初级生产力增加区域面积占植被区总面积的73.82%;从变化趋势来看,植被覆盖度好转区域面积净增加13.02%,植被净初级生产力好转区域面积净增加24.62%。工程实施前后相比,各流域宏观生态状况恢复程度具有差异,其中楚玛尔河源头植被覆盖度上升最明显,通天河流域植被净初级生产力上升最明显。长江源区宏观生态状况的好转受益于气候的湿润化及生态工程的共同影响,若要全面有效改善仍需持续努力。

关键词： 长江源区; 植被覆盖度; 植被净初级生产力; 生态保护和建设工程; 生态成效

本文引用格式

刘璐璐 , 曹巍 , 邵全琴 . 三江源生态工程实施前后长江源区宏观生态状况变化分析[J]. 地球信息科学学报, 2016 , 18(8) : 1069 -1076 . DOI: 10.3724/SP.J.1047.2016.01069

Abstract

The positive effects of ecological conservation and construction projects on ecosystem restoration were obvious in the headwater of the Yangtze River. This paper estimated the vegetation coverage (VC) and net primary productivity (NPP) during the period of 1997-2012 in this region by applying the GLOPEM-CEVSA model and time series Normalized Difference Vegetation Index (NDVI) dataset. Then, the changes of ecological conditions before and after the implementation of projects were assessed. The results showed that the ecological conditions in the headwater of the Yangtze River were significantly improved after implementing the projects, with obviously increased VC and NPP. From the annual average levels of VC and NPP, we can see that by comparing the conditions before and after implementing the projects, the area with increased VC and NPP had accounted for 72.10% and 73.82% of the total area respectively. And by looking into the variation trends, we can also see that the area with restored VC and NPP had increased by 13.02% and 24.62% respectively. The vegetation restorations were varied in different watersheds. Compared the conditions before and after implementing the project, the restoration of VC in the headwater of the Chumaer River showed an obvious improvement and NPP had increased significantly in the Tongtian River watershed. The restoration of the ecological condition in the headwater of the Yangtze River was benefited from the combined effects of the humid climate and the ecological projects, while continuous efforts are still needed to improve the ecological environment sustainably and effectively in the future.

Key words： the headwater of the Yangtze River; vegetation coverage; net primary productivity; ecological projects; ecological effect

1 引言

长江源区位于青藏高原腹地,三江源区西部,群山高耸,以冰川、冰缘、高山、高地平原、丘陵地貌为主[1],间有湖泊、沼泽,其冰川面积占整个三江源区的89%以上[2]。自20世纪70年代以来,长江源区生态环境发生严重退化,包括冻土退化、冰川退缩、草场退化、土地沙化、湖泊萎缩等[3]。由于海拔高且气候恶劣,其生态环境更为敏感和脆弱,遭到破坏后,恢复的难度很大。三江源自然保护区生态保护和建设一期工程自2005年开始实施,至2012年已实施8年,以生态保护与建设、农牧民生产生活基础设施建设和科技支撑为主要建设项目,其中生态保护与建设主要包括退牧还草、退耕还林、生态恶化土地治理（封山育林、沙漠化土地防治、湿地保护、黑土滩治理）、鼠害防治、水土保持、减畜等工程。邵全琴等[4]、Tong等[5]通过对气象数据分析,发现自2004 年以来三江源区进入了一个暖湿周期,有利于生态系统的恢复。在气候变化和人类活动的双重驱动作用下,国内外众多研究表明,目前三江源区生态环境已由持续退化状态转变为“初步遏制,局部好转”的态势。邵全琴等[4]、吴丹等[6]的研究表明三江源、长江源土地覆被状况在近30年经历了变差-显著变差-略有好转的变化过程;Liu等[7]研究表明在气候暖湿化以及生态保护工程的影响下,近12年三江源区植被覆盖呈现增加趋势,其中长江源区、黄河源区均呈增加趋势,而澜沧江源区呈下降趋势;张良侠等[8]研究表明,工程实施后三江源区草地产草量提高且载畜压力减轻;李辉霞等[9]基于NDVI数据,利用残差分析法分析指出,生态工程对植被生长的贡献率达到20.68%;姚玉璧等[10]认为近50年长江源区域植被净初级生产力呈显著上升趋势。综上,以往的研究侧重于三江源整区的生态系统及状况变化,或者长江源多年生态状况或宏观结构的变化情况,鲜有针对生态工程实施前后长江源区宏观生态状况的变化及其对比分析。通过对比分析三江源生态工程实施前后长江源区宏观生态状况的变化情况,可为评价三江源生态工程的生态效益及指导该区进一步的生态建设及工程布局提供参考和科学依据。
植被覆盖度是反映地表植被群落生长态势的重要指标,植被净初级生产力则是生态系统生产能力的直接反映[11-12]。分析评估区域植被覆盖度和植被净初级生产力的水平及变化,是评估生态系统支持功能和掌握区域生态系统环境变化的必需工作内容,可为区域生态资源的合理利用、生态系统的可持续发展提供必要的学术参考。本文通过分析三江源生态工程实施前（1997-2004年）、后（2005-2012年）长江源区植被覆盖度及植被净初级生产力2个指标的变化,探讨气候变化和人类活动共同影响下,长江源区宏观生态状况的变化。

2 研究区概况与数据源

2.1 研究区概况

研究区选择以通天河中段称多县境内的直门达水文站为流域出水口的以上流域,包括沱沱河源头、当曲源头、楚玛尔河源头和通天河流域（图1）。研究区面积约为13.95×104 km2,海拔范围在3531~6575 m,多年平均气温和年均降水量分别为-4.8 ℃和397.82 mm。研究区主要生态系统类型有高寒草原、高寒草甸和高寒沼泽湿地3大典型高原生态系统类型。三江源生态建设和保护工程实施范围与三江源国家自然保护区范围相同,其中长江源区主要涉及5个分保护区,即格拉丹东冰川保护区、通天河峡谷灌丛草地保护区、索加-曲麻河高寒草原及湿地保护区、果宗木查湿地保护区和当曲湿地保护区,工程实施面积共6.94×104 km2,占源区总面积的49.75%。
Fig. 1 The scope of the ecological conservation and construction project in the headwater of the Yangtze River

图1 长江源区生态工程实施范围

2.2 数据

本文基于1997-2000年的AVHRR/NDVI和2000-2012年MODIS NDVI进行植被覆盖度的计算。由于2套NDVI来源于不同的传感器,因此数据之间存在一定差异。为了构建1997-2012年的NDVI统一数据集,本文基于AVHRR/NDVI和MODIS NDVI在2000年的同时期数据（每16天1期）,通过空间配准后进行逐栅格的线性拟合,建立2套数据集之间的拟合关系式,得到拟合系数,最终根据拟合系数实现2套数据的归一化处理。
GLOPEM-CEVSA模型所用数据主要包括基于卫星遥感的FPAR数据和气象数据（如气温、降水等）。在上述NDVI数据基础上,采用Liu等[15]开发的冠层辐射传输算法,反演获得用于模型输入的FPAR。气象数据由中国气象局和青海省气象局提供,在13个站点气象数据基础上,利用ANUSPLIN进行空间插值,获得气温、降水、相对湿度、风速、日照时数等空间插值数据作为模型输入。在此基础上,通过GLOPEM-CEVSA模型模拟了1997年以来1 km空间分辨率的三江源区植被净初级生产力数据,然后利用长江源区边界作为掩膜,裁切得到长江源区1997-2012年的植被净初级生产力。

3 研究方法

3.1 植被覆盖度

植被覆盖度基于植被指数NDVI数据计算得到,公式如式（1）所示。
f = ( NDVI - NDV I soil ) ( NDV I max - NDV I soi l ) （1）
式中：NDVIsoil为纯裸土像元的NDVI值;NDVImax为纯植被像元的NDVI值。
李苗苗[13]认为,当研究区植被覆盖度最大能达到100%、最小能达到0%时,通过对NDVI图像设置一个置信度（95%）,计算置信区间内NDVI最大值NDVImax和最小值NDVImin,即可得到各像元的植被覆盖度。

3.2 植被净初级生产力

植被净初级生产力的计算采用GLOPEM(Global Production Efficiency Models)-CEVSA(Carbon Exchange in the Vegetation-Soil-Atmosphere model)耦合模型。GLOPEM-CEVSA模型是在全球生产效率模型（GLO-PEM）和植被、大气和土壤碳交换模型（CEVSA）的基础上发展而来。GLOPEM-CEVSA模型建立在碳循环过程和生理生态学理论基础上。通过模拟光能利用率,以卫星遥感反演的FPAR模拟植被吸收的光合有效辐射（APAR）,获得植被总初级生产力（GPP）;以植被生物量和气温及不同植被群落的维持性呼吸系数及温度关系模拟植被维持性呼吸（Rm）和生长性呼吸（Rg）,获得植被净第一性生产力（NPP）,如式（2）所示,详见参考文献[14]。
NPP = GPP - Ra （2）
式中：NPP为植被净初级生产力/（gCm-2a-1）;GPP为总初级生产力/（gCm-2a-1）;Ra为植被自养呼吸/（gCm-2a-1）。

4 结果与分析

4.1 植被覆盖状况

从平均植被覆盖度来看,三江源生态保护和建设工程实施后,长江源区平均植被覆盖度状况明显好转。植被覆盖度好转区域总面积占长江源区植被区的72.10%,其中植被覆盖度轻微好转区域面积占46.28%,明显好转区域面积占25.82%;覆盖度变差区域面积仅占11.13%（表1）。从空间分布看,工程实施后植被覆盖度好转区域广泛分布于全区,明显好转区域主要集中于楚玛尔河源头（图2）。
Tab. 1 The area of vegetation coverage change before and after implementing the ecological project in the headwater of the Yangtze River

表1 工程实施前后长江源区植被覆盖度变化面积统计表

植被覆盖度
变化分级/（%）		 工程实施前后植被覆盖度变化
面积/km2 面积比重/（%）
明显变差（<-10） 1698.92 1.57
轻微变差（-10~-2） 10 345.53 9.56
基本稳定（-2~2） 18 142.35 16.77
轻微好转（2~10） 50 071.14 46.28
明显好转（>10） 27 937.63 25.82
Fig. 2 The spatial distribution of vegetation coverage change before and after implementing the ecological project in the headwater of the Yangtze River

图2 工程实施前后长江源区植被覆盖度变化空间分布图

从植被覆盖度变化趋势看,三江源生态保护和建设工程实施后,长江源区植被覆盖度好转趋势更为明显。好转区域面积净增加13.02%,其中轻微好转区域面积净增加13.64%,明显好转区域面积净减少0.62%;基本稳定区域面积净减少9.86%,变差区域面积净减少3.16%（表2）。从空间分布来看,工程实施后,植被覆盖度好转区域由东部和南部逐渐向西部和北部过渡,且好转面积扩大（图3）。
Tab. 2 The area of vegetation coverage variation trend before and after implementing the ecological project in the headwater of the Yangtze River

表2 工程实施前后长江源区植被覆盖度变化趋势面积统计表

植被覆盖度变化趋势
（年变化率）		 生态工程前（1997-2004年） 生态工程后（2005-2012年）
面积/km2 面积比重/（%） 面积/km2 面积比重/（%）
明显变差（<-0.01） 571.12 0.53 2 949.83 2.73
轻微变差（-0.01- -0.001） 25 514.45 23.57 19 719.31 18.22
基本稳定(-0.001-0.001） 23 635.64 21.84 12 961.12 11.98
轻微好转（0.001-0.01） 53 402.43 49.34 68 156.87 62.97
明显好转（> 0.01） 5 115.67 4.73 4 445.42 4.11
Fig. 3 The spatial distribution of vegetation coverage variation trend before (a) and after (b) implementing the ecological project in the headwater of the Yangtze River

图3 工程前后长江源区植被覆盖度变化率空间分布图

分流域来看,通天河流域植被覆盖度最高,当曲源头其次,楚玛尔河源头植被覆盖度最低。生态工程实施前,除沱沱河源头,各流域植被覆盖度均呈上升趋势,且通天河流域上升最为明显,当曲源头其次;生态工程实施后,各流域植被覆盖度均呈上升趋势,其中,沱沱河、楚玛尔河及当曲源头植被覆盖度上升速率比工程前快,且楚玛尔河源头上升最明显（图4）。
Fig. 4 The interannual variation of vegetation coverage in the headwater of the Yangtze River during 1997-2012

图4 1997-2012年长江源区植被覆盖度年际变化

注：∧ P> 0.05;* P< 0.05

4.2 植被净初级生产力

从年均植被净初级生产力来看,三江源生态保护和建设工程实施后,长江源区植被净初级生产力明显增加。植被净初级生产力增加区域总面积占长江源区植被区的73.82%,其中轻微增加区域面积占46.91%,轻度增加区域面积占18.28%,明显增加区域面积占8.63%;植被净初级生产力变差区域面积仅占11.78%（表3）。从空间分布看,工程实施后植被净初级生产力增加区域广泛分布于全区,明显增加的区域主要集中于通天河流域东部（图5）。
Tab. 3 The area of NPP change before and after implementing the ecological project in the headwaters of the Yangtze River

表3 工程前后长江源区植被净初级生产力变化面积统计表

植被净初级生产力变化/（gCm-2a-1） 工程前后植被净初级生产力变化
面积/km2 占全区面积比重/（%）
明显减少（<-100） 152.04 0.14
轻度减少（-100~-50） 1 023.97 0.95
轻微减少（-50~-5） 11 578.77 10.69
基本稳定（-5~5） 15 595.60 14.40
轻微增加（5~50） 50 788.03 46.91
轻度增加（50~100） 19 794.80 18.28
明显增加（>100） 9 343.22 8.63
Fig. 5 The spatial distribution of NPP change before and after implementing the ecological project in the headwater of the Yangtze River

图5 工程前后长江源区植被净初级生产力变化空间分布图

从植被净初级生产力变化趋势来看,三江源生态保护和建设工程实施后,长江源区植被净初级生产力增加趋势更为明显。植被净初级生产力增加区域面积净增加24.62%,其中轻微增加区域面积净增加15.56%,轻度增加区域面积净增加8.87%,明显增加区域面积净增加0.20%;基本稳定区域面积净增加1.06%,减少区域面积净减少25.68%（表4）。从空间分布来看,生态工程实施后,植被净初级生产力增加区域逐渐向长江源区腹地过渡,且面积扩大（图6）。
Tab. 4 The area of NPP variation trend before and after implementing the ecological project in the headwater of the Yangtze River

表4 1997-2012年三江源地区植被净初级生产力变化率面积统计表

植被净初级生产力变化率分级/（gCm-2a-1） 工程前（1997-2004年） 工程后（2004-2012年）
面积/km2 面积比重/（%） 面积/km2 面积比重/（%）
明显减少（<-50） 129.86 0.12 71.05 0.07
轻度减少（-50~-10） 11 746.68 10.85 5 023.11 4.64
轻微减少（-10~-1） 38 631.19 35.69 17 614.05 16.27
基本稳定（-1~1） 16 843.72 15.56 17 993.2 16.62
轻微增加（1~10） 32 797.12 30.30 49 638.97 45.86
轻度增加（10~50） 8 083.98 7.47 17 680.02 16.34
明显增加（>50） 0.00 0.00 212.15 0.20
Fig. 6 The spatial distribution of NPP variation trend before (a) and after (b) implementing theecological project in the headwater of the Yangtze River

图6 工程前后长江源区植被净初级生产力变化率空间分布图

分流域来看,通天河流域植被净初级生产力最高,当曲源头其次,楚玛尔河源头植被净初级生产力最低;生态工程实施前,各流域植被净初级生产力均呈下降趋势,且通天河流域下降最为明显,楚玛尔河源头其次,当曲源头下降趋势最小;生态工程实施后,各流域植被净初级生产力均呈上升趋势,且通天河流域上升最为明显,其次为当曲源头,楚玛尔河源头上升趋势最低。

5 结论与讨论

基于1997-2012年植被覆盖度及植被净初级生产力时空变化特征的分析,本文对长江源区宏观生态状况在三江源生态保护和建设工程实施前、后的变化情况进行了评估。研究结果表明,工程实施后长江源区宏观生态状况整体好转,具体结论如下：
工程实施后,长江源区植被覆盖度明显好转,植被覆盖度好转区域面积占长江源区植被区的72.10%。从2个时段植被覆盖度变化趋势来看,工程实施后植被覆盖度好转区域面积净增加13.02%;从各流域来看,工程实施后,各流域植被覆盖度均呈上升趋势,其中,沱沱河、楚玛尔河及当曲源头植被覆盖度上升速率比工程前快,且楚玛尔河源头上升最明显。
工程实施后,长江源区植被净初级生产力明显增加,植被净初级生产力增加区域面积占长江源区植被区的73.82%。从2个时段植被净初级生产力变化趋势来看,工程实施后植被净初级生产力好转区域面积净增加24.62%;从各流域来看,工程实施前,各流域植被净初级生产力均呈下降趋势,工程实施后,各流域植被净初级生产力均呈上升趋势,且通天河流域上升最为明显。
结果表明,生态工程实施后,长江源区植被覆盖度及植被净初级生产力都有所增加,该研究结果与Liu等[7]得出的近10年来,除澜沧江外,三江源区植被覆盖度呈增加趋势的结论相一致;姚玉璧等[10]研究表明近50年来长江源区的植被净初级生产力呈显著上升趋势,虽本研究的结果表明在生态工程实施前,长江源区植被净初级生产力呈下降趋势,但从整个1997-2012整个时间段来看,植被净初级生产力呈现波动上升趋势（图7）,因此并不矛盾。
Fig. 7 The interannual variation of NPP in the headwater of the Yangtze River during 1997-2012

图7 1997-2012年长江源区平均植被净初级生产力年际变化趋势

注：∧ P> 0.05

生态系统宏观生态状况除受到生态工程的影响外,还受到气候等自然因素的影响。影响长江源区植被生长变化的主要气候因子是降水量、最大蒸散量和温度[10,16-17]。湿润指数指年降水量与潜在蒸发散之比,能较好地反映气候的干湿状况。本文利用湿润指数分析长江源区工程实施前后气候干湿变化情况。由图8可看出,长江源区工程实施后气候变得更为湿润,不仅能促进植被的生长,而且有利于扩大湿地的面积,促进区域水分条件的好转[5]。由图9可看出,植被净初级生产力的年际变化率随湿润系数年际变化率的上升呈逐渐上升趋势,相关系数为0.35。为进一步研究生态工程及气候变化对植被净初级生产力变化的影响贡献率,在GLOPEM-CEVSA模型中,利用模型变量控制法,通过输入多年平均气候要素,估算平均气候状况下工程前、后的植被净初级生产力,在不考虑其他非决定性因素的情况下,平均气候状况下生态工程前、后植被净初级生产力的变化主要反映了生态工程的影响。因此,通过与真实气候状况下生态工程前、后植被净初级生产力的变化进行对比,可得出生态工程对植被净初级生产力变化的贡献率达到12.8%,气候变化对其贡献率为87.2%。
Fig. 8 The average moisture coefficient in the headwater of the Yangtze River

图8 长江源区平均湿润系数空间分布

Fig. 9 The scatter distribution of NPP change rate and moisture coefficient change rate in the headwaterof the Yangtze River

图9 长江源区NPP年际变化率与湿润指数年际变化率的散点分布图

虽然在气候湿润化及生态工程的共同驱动下,长江源区生态系统宏观生态状况得到一定的改善,但由于长江源区地处高寒地区,生态系统十分脆弱,生态系统一旦遭到破坏,退化的态势很难通过短期的恢复治理工程得以迅速和全面的扭转。目前植被退化态势好转仅表现在长势上,群落结构并未发生好转[18];过牧超载现象依然很严重,这是造成草地退化的一项关键因素[19];降雨的增加在促进植被生长的同时,也造成了降雨侵蚀力的明显提 高[20];温度的升高使得冰川融化并退缩,尽管短期来看,可使得沼泽、湖泊等土地覆被类型扩张,改善当地水分条件,但长远来看,这必将会影响当地生态系统平衡。因此,遏制长江源区生态系统的退化,全面有效地改善长江源区生态系统宏观生态状况的任务极其艰巨,需要持续努力。
致谢：中国科学院地理科学与资源研究所刘纪远研究员、樊江文研究员对论文的研究思路进行了指导,王军邦副研究员提供了植被净初级生产力数据,黄麟副研究员、李愈哲助理研究员对文章语句及表达进行了修改及建议,在此表示衷心的感谢！

The authors have declared that no competing interests exist.

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Tong L G, Xu X L, Fu Y, et al.Wetland changes and their responses to climate change in the "Three-River Headwaters" region of China since the 1990s[J]. Energies, 2014,7:2515-2534.The wetland ecosystem in the 09銇04Three-River Headwaters09 (TRH) region plays an irreplaceable role in water source conservation, run-off adjustment and biodiversity maintenance. In recent years, assessment of wetland resources affected by climate changes has aroused enormous attention, since it can further protect wetland resources and provide a scientific basis for decision makers. In this study, wetland changes and its response to climate changes in the TRH region from the early 1990s to 2012 were analyzed by remote sensing (RS) image interpretation and climate change trend analysis. The results showed that wetlands occupied 6.3% of the total land area in 2012, and swamps, streams & rivers and lakes were the dominant wetland types in the TRH region. Since the early 1990s, wetlands have undergone great changes, and total wetland area increased by 260.57 km 2 (1.17%). Lakes, reservoir & ponds took on continuous increasing trend, but swamps, streams & rivers had a continuous decreasing trend. On the other hand, the wetland area in the Yangtze River basin showed an overall increasing trend, while in the Yellow River and Langcang River basins, it decreased in general. The climate turned from Warm-Dry to Warm-Wet. The average temperature and precipitation increased by 0.91 00°C and 101.99 mm, respectively, from 1990 to 2012, and the average humidity index ( HI ) increased by 0.06 and showing an upward trend and a shifting of the dividing line towards the northwest in both the areas of semi-humid and semi-arid zone. The correlation analysis of wetland changes with meteorological factors from 1990 to 2012 indicated that the regional humidity differences and the interannual variation trend, caused by the change of precipitation and evaporation, was the main driving factor for the dynamic variation of wetland change in the TRH region. In the general, the increase of HI in the THR region since the 1990s, especially in the western TRH region, contributed to wetland increase continuously. The conclusions of this study will provide some scientific references for the management and protection of wetlands in the TRH region, especially for restoration, reconstruction and conservation of degradation wetland.

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DOI

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Liu X F, Zhang J S, Zhu X F, et al.Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000-2011[J]. Journal of Geographical Sciences, 2014,24(2):288-302.The Three-River Headwaters Region (TRHR), which is the source area of the Yangtze River, Yellow River, and Lancang River, is of key importance to the ecological security of China. Because of climate changes and human activities, ecological degradation occurred in this region. Therefore, "The nature reserve of Three-River Source Regions" was established, and "The project of ecological protection and construction for the Three-River Headwaters Nature Reserve" was implemented by the Chinese government. This study, based on MODIS-NDVI and climate data, aims to analyze the spatiotemporal changes in vegetation coverage and its driving factors in the TRHR between 2000 and 2011, from three dimensions. Linear regression, Hurst index analysis, and partial correlation analysis were employed. The results showed the following: (1) In the past 12 years (2000-2011), the NDVI of the study area increased, with a linear tendency being 1.2%/10a, of which the Yangtze and Yellow River source regions presented an increasing trend, while the Lancang River source region showed a decreasing trend. (2) Vegetation coverage presented an obvious spatial difference in the TRHR, and the NDVI frequency was featured by a bimodal structure. (3) The area with improved vegetation coverage was larger than the degraded area, being 64.06% and 35.94%, respectively during the study period, and presented an increasing trend in the north and a decreasing trend in the south. (4) The reverse characteristics of vegetation coverage change are significant. In the future, degradation trends will be mainly found in the Yangtze River Basin and to the north of the Yellow River, while areas with improving trends are mainly distributed in the Lancang River Basin. (5) The response of vegetation coverage to precipitation and potential evapotranspiration has a time lag, while there is no such lag in the case of temperature. (6) The increased vegetation coverage is mainly attributed to the warm-wet climate change and the implementation of the ecological protection project.

DOI

[8]
张良侠,樊江文,邵全琴,等.生态工程前后三江源草地产草量与载畜压力的变化分析[J].草业学报,2014,23(5):116-123.lt;p>三江源生态保护和建设工程对生态系统的恢复具有重要作用,其对草地生态系统的影响也成为关注的热点。本文基于GLO-PEM模型和载畜压力指数,对比分析了三江源地区实施生态工程前后草地产草量和载畜压力的变化。结果表明,工程实施后的2005-2012年8年的草地平均产草量为694 kg/hm<sup>2</sup>比工程实施前1988-2004年17年的平均产草量(533 kg/hm<sup>2</sup>)提高了30.31%,减畜措施实施后的2003-2012年10年的平均载畜压力指数为1.46,比1988-2002年15年平均载畜压力指数(2.49)下降了36.1%。草地产草量的提高和载畜压力的减轻,主要归因于生态保护和建设工程的实施以及气候变化。其中,生态工程对草地生态系统的恢复已初见成效。</p>

DOI

[ Zhang L X, Fan J W, Shao Q Q, et al.Change in grassland yield and grazing pressure in the Three Rivers headwater region before and after the implementation of the eco-restoration project[J]. Acta Prataculturae Sinica, 2014,23(5):116-123. ]

[9]
李辉霞,刘国华,傅伯杰.基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究[J].生态学报,2011,31(19):5495-5504.采用Spot VEGETATION 逐旬NDVI数据、1 ∶ 100万植被类型图和气象站资料,在掌握近10a三江源地区植被变化趋势基础上,分不同植被类型探讨植被生长对气候变化的响应机制,并通过分离气候要素与人类活动对NDVI的贡献,定量评估生态保护与建设工程的实施效果。结果表明,区域尺度上,三江源地区2001-2010年植被生长呈好转趋势,植被增长从东南向西北递减;在10a时间尺度上,气候变化是影响植被生长的决定性因素,但人类活动可在短期内加快植被变化速率,气候要素和人类活动对植被生长的贡献分别为79.32%和20.68%;降水和气温对植被生长的影响程度相当,其中受春季和秋季的降水和气温影响最大,尤其是植被生长季前后一个月(4月份和10月份)的气候条件;与林地和灌丛相比,高寒草地受气候条件的抑制作用更为明显,其中高寒草甸受气候变化的影响最大,NDVI与降水和气温均具有较高相关性,高寒草原受气温的影响比较大,而高山植被受降水的抑制作用更为明显;在气候条件利于植被生长的趋势下,2001-2010年三江源地区的人类活动对生态环境表现出正影响,实测NDVI<sub>max</sub>与模拟NDVI<sub>max</sub>之间的残差为0.0863,表明生态保护与建设行动取得初步成效,其中黄河源区东部和长江源区通天河两侧的生态恢复效益最为明显,而在唐古拉山、昆仑山、布青山、阿尼玛卿山等山脉的周边地区,人类活动对生态环境仍表现为负影响;时间尺度上人类活动对植被的正影响呈现出下降趋势,2001-2010年NDVI<sub>max</sub>残差的回归斜率为-0.0039,表明生态项目实施的短期行为严重,生态建设的效果缺乏长效性。

[ Li H X, Liu G H, Fu B J.Response of vegetation to climate change and human activity based on NDVI in the Three-River Headwaters region[J]. Acta Ecologica Sinica, 2011,31(19):5495-5504. ]

[10]
姚玉璧,杨金虎,王润元,等.50年长江源区域植被净初级生产力及其影响因素变化特征[J].生态环境学报,2010,11:2521-2528.基于长江源区1959-2008年月平均气温、最高气温、最低气温、相对湿度、降水量、风速和日照时数等气候要素资料,应用修订的Thomthwaite Memorial模型计算了50年植被净初级生产力,分析其年际和年代际变化特征及其主要气象因子的影响.结果表明:1959-2008年间,研究区年降水量呈增加趋势,降水量变化曲线线性拟合倾向率每10年为5.685～13.047 mm,春夏季增幅较大;年平均气温呈极显著上升趋势,气温变化曲线线性拟合倾向率每10年在0.240～0.248℃之间,增温率以秋冬季最大;最大蒸散呈增加趋势,年最大蒸散变化曲线线性拟合倾向率每10年在5.073～5.366 mm,春季增幅最大;地表湿润指数也呈增加趋势,年地表湿润指数变化曲线线性拟合倾向率每10年为0.013～0.020,冬季增幅最大,在10年周期时间频率附近,出现了6～8个干湿交替期,20世纪90年代之后为偏湿期,在低频区,1998-2005年有偏干振荡;近50年年NPP变化呈显著上升趋势,NPP变化曲线线性拟合倾向率每10年在97.901～197.01 kg·hm-2之间,2001-2008年NPP较高.影响长江源区.NPP变化的主要气候因子是降水量、最大蒸散量和平均最低气温.

[ Yao Y B, Yang J H, Wang R Y, et al.Change feature of net primary productivity of natural vegetation and its impact factors over the source region of the Yangtze River in recent 50 years[J]. Ecology and Environmental Sciences, 2010,11:2521-2528. ]

[11]
甘春英,王兮之,李保生,等.连江流域近18 年来植被覆盖度变化分析[J].地理科学,2011,31(8):1019-1024.以TM影像为数据源,运用基于NDVI的像元二分模型,计算和分析连江流域1988和2006年植被演变特点及空间分布特征,并将两期影像的植被覆盖度图与连江流域分岩溶区地质图进行叠加,进而分析地质构造对植被覆盖度的影响。结果表明:①受气候及与人文因素的影响,1988～2006年连江流域植被覆盖度有所增加。表现为较高和高植被覆盖区面积增加,低、较低和中度植被覆盖区面积减少。②受地质构造影响,非岩溶区的植被质量优于岩溶区。③在空间分布上,近18 a来连江流域植被覆盖度的变化较显著。

[ Gan C Y, Wang X Z, Li B S, et al.Changes of vegetation coverage during recent 18 years in Lianjiang River watershed[J]. Scientia Geographica Sinica, 2011,31(8):1019-1024. ]

[12]
穆少杰,李建龙,杨红飞,等.内蒙古草地生态系统近10年NPP时空变化及其与气候的关系[J].草业学报,2013,22(3):6-15.lt;p>植被净初级生产力（ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ，ＮＰＰ）及其对气候变化的响应研究是全球变化的核心内容之一。通过改进的光能利用率模型（ＣＡＳＡ 模型），利用ＭＯＤＩＳＮＤＶＩ数据、土地覆盖分类数据、气象数据等，逐像元模拟２００１－２０１０年内蒙古草地生态系统ＮＰＰ的时空变化，分析其对气候因子变化的响应关系。结果表明，１）２００１－２０１０年内蒙古草地多年平均ＮＰＰ为２８１．３ｇＣ／（ｍ<sup>２</sup>&middot;ａ），空间分布呈由西南向东北递增的趋势，草甸草原、典型草原和荒漠草原平均ＮＰＰ分别为４３１．８，２８８．７和１２３．５ｇＣ／（ｍ<sup>２</sup>&middot;ａ）；２）２００１－２０１０年间内蒙古草地ＮＰＰ 总体上呈上升趋势。ＮＰＰ上升趋势最明显的草地主要分布在毛乌素沙地、浑善达克沙地、科尔沁沙地、呼伦贝尔盟和大兴安岭南麓地区，而下降趋势最明显的草地主要分布在阴山山脉和锡林郭勒盟中部的典型草原区；３）总体而言，降水量是内蒙古草地净初级生产力的主要影响因素。草甸草原ＮＰＰ与降水量、温度的关系均很密切，而且与温度的相关性更强；典型草原和荒漠草原ＮＰＰ则主要受降水量控制，其中荒漠草原ＮＰＰ与降水量的关系更密切。</p>

DOI

[ Mu S J, Li J L, Yang H F, et al.Spatio-temporal variation analysis of grassland net primary productivity and its relationship with climate over the past 10 years in Inner Mongolia[J]. Acta Prataculturae Sinica, 2013,22(3):6-15. ]

[13]
李苗苗. 植被覆盖度的遥感估算方法研究[D].北京:中国科学院研究生院,2003.

[ Li M M.The method of vegetation fraction estimation by remote sensing[D]. Beijing: Graduate University of the Chinese Academy of Sciences, 2003. ]

[14]
王军邦,刘纪远,邵全琴,等.基于遥感-过程耦合模型的1988-2004年青海三江源区净初级生产力模拟[J].植物生态学报,2009,33(2):254-269.三江源区不仅是地处青藏高原的全球气候变化的敏感区, 也是我国甚至亚洲最重要河流的上游关键源区。作为提供物质基础的植被净初级生产力(Net primary production, <EM>NPP</EM>), 是评价生态系统状况的重要指标。该文应用已在碳通量观测塔验证, 扩展到区域水平的遥感-过程耦合模型GLOPEM-CEVSA, 以空间插值的气象数据和1 km分辨率的AVHRR遥感反演的FPAR数据为模型主要输入, 模拟并分析了1988～2004年该区<EM>NPP</EM>时空格局及其控制机制。结果表明, 该区植被平均<EM>NPP</EM>为143.17 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP> 呈自东南向西北逐渐降低的空间格局, 其中, 以森林<EM>NPP</EM>最高(267.90 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP>), 其次为农田(222.94 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP>)、草地(160.90 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP>)和湿地(161.36 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP>), 荒漠最低(36.13 gC·m<SUP>–2</SUP>·a<SUP>–1</SUP>)。其年际变化趋势在空间上呈现出明显的差异, 西部地区<EM>NPP</EM>表现为增加趋势, 每10 a增加7.8~28.8 gC·m<SUP>–2</SUP>; 而中、东部表现为降低趋势, 每10 a降低13.1~42.8 gC·m<SUP>–2</SUP>。根据显著性检验, <EM>NPP</EM>呈增加趋势(趋势斜率b&gt;0), 显著性水平高于99%和95%的区域占研究区总面积的13.43%和20.34%, 主要分布在西部地区; <EM>NPP</EM>呈降低趋势(趋势斜率b&lt;0), 显著性水平高于99%和95%的区域占研究区面积的0.75%和3.77%, 主要分布在中、东部地区, 尤以该区长江和黄河等沿线区分布更为集中, 变化显著性也更高。三江源<EM>NPP</EM>的年际变化趋势的气候驱动力分析表明, 整个区域水平上该地区植被生产力受气候变化的主导, 西部地区暖湿化趋势, 造成了该地区生产力较为明显的、大范围的增加趋势; 但东、中部地区则主要受人类活动的影响, 特别是长江、黄河等河流沿线, 是人类居住活动密集的地区, 造成这些地区放牧压力较大、草地退化严重, 而该地区暖干化趋势加剧了这一过程。

DOI

[ Wang J B, Liu J Y, Shao Q Q, et al.Spatial-temporal patterns of net primary productivity for 1988-2004 based on GLOPEM-CEVSA model in the “Three-river Headwaters” region of Qinghai Province, China[J]. Chinese Journal of Plant Ecology, 2009,33(2):254-269. ]

[15]
Liu R G, Chen J M, Liu J Y, et al.Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research[J]. Journal of Environmental Management, 2007,85:649-658.lt;h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219&ndash;2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316&ndash;1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft<sup>&reg;</sup> platform, a software system designed for processing MODIS data, to generate 250&#xA0;m, 500&#xA0;m and 1&#xA0;km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.</p>

DOI PMID

[16]
Dai S P, Zhang B, Wang H J, et al.Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981-2006): Impacts of climate changes and human activities[J]. Science in Chinese: Series D, 2008,51(1):67-78.Spatiotemporal variations of Chinese Loess Plateau vegetation cover during 1981-2006 have been investigated using GIMMS and SPOT VGT NDVI data and the cause of vegetation cover changes has been analyzed, considering the climate changes and human activities. Vegetation cover changes on the Loess Plateau have experienced four stages as follows: (1) vegetation cover showed a continued increasing phase during 1981-1989; (2) vegetation cover changes came into a relative steady phase with small fluctuations during 1990-1998; (3) vegetation cover declined rapidly during 1999-2001; and (4) vegetation cover increased rapidly during 2002-2006. The vegetation cover changes of the Loess Plateau show a notable spatial difference. The vegetation cover has obviously increased in the Inner Mongolia and Ningxia plain along the Yellow River and the ecological rehabilitated region of Ordos Plateau, however the vegetation cover evidently decreased in the hilly and gully areas of Loess Plateau, Liupan Mountains region and the northern hillside of Qinling Mountains. The response of NDVI to climate changes varied with different vegetation types. NDVI of sandy land vegetation, grassland and cultivated land show a significant increasing trend, but forest shows a decreasing trend. The results obtained in this study show that the spatiotemporal variations of vegetation cover are the outcome of climate changes and human activities. Temperature is a control factor of the seasonal change of vegetation growth. The increased temperature makes soil drier and unfavors vegetation growth in summer, but it favors vegetation growth in spring and autumn because of a longer growing period. There is a significant correlation between vegetation cover and precipitation and thus, the change in precipitation is an important factor for vegetation variation. The improved agricultural production has resulted in an increase of NDVI in the farmland, and the implementation of large-scale vegetation construction has led to some beneficial effect in ecology. &copy; Science in China Press 2008.

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[17]
刘军会,高吉喜.气候和土地利用变化对中国北方农牧交错带植被覆盖变化的影响[J]. 应用生态学报,2008,19(9):2016-2022.lt;FONT face=Verdana>基于1986、2000年中国北方农牧交错带的遥感影像及研究区的气象数据，利用植被<BR>覆盖度信息提取模型研究了1986—2000年间研究区植被覆盖度的时空变化，并分析了气候<BR>和土地利用变化对植被覆盖度变化的影响.结果表明：1986—2000年，研究区高盖度植被的面积缩减，低盖度植被的面积增加；植被覆盖升高区主要位于该区东北段的东部、北段的西部和西北段的西部，其他地段的植被覆盖明显退化；植被覆盖度与降水、干燥度指数呈正相关，与温度呈负相关；不同土地利用类型的植被覆盖度变化方向和程度各异.<BR></FONT>

[ Liu J H, Gao J X.Effects of climate and land use change on the changes of vegetation coverage in farming-pastoral ecotone of Northern China[J]. Chinese Journal of Applied Ecology, 2008,19(9):2016-2022. ]

[18]
刘纪远,邵全琴,樊江文.三江源生态工程的生态成效评估与启示[J].自然杂志,2013,35(1):40-46.lt;p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 介绍了对青海三江源生态保护与建设工程所取得的生态成效开展科学监测与评估的主要方法和评估结果，回答了一系列公众和决策者关心的有关生态工程成效和取得成效原因的科学问题，并分析了工程取得生态成效的主要局限性。在此基础上提出了工程成效评估对决策者和工程执行者的主要启示以及建议。</p>

[ Liu J Y, Shao Q Q, Fan J W.Ecological construction achievements assessment and its revelation of ecological project in Three Rivers Headwaters Region[J]. Chinese Journal of Nature, 2013,35(1):40-46. ]

[19]
Zhang J P, Zhang L B, Liu W L, et al.Livestock-carrying capacity and overgrazing status of alpine grassland in the Three-River Headwaters region, China[J]. Journal of Geographical Sciences, 2014,24:303-312.The Three-River Headwaters region in China is an ecological barrier providing environmental protection and regional sustainable development for the mid-stream and downstream areas, which also plays an important role in animal husbandry in China. This study estimated the grassland yield in the Three-River Headwaters region based on MODIS NPP data, and calculated the proper livestock-carrying capacity of the grassland. We analyzed the overgrazing number and its spatial distribution characteristics through data comparison between actual and proper livestock-carrying capacity. The results showed the following: (1) total grassland yield (hay) in the Three-River Headwaters region was 10.96 million tons in 2010 with an average grassland yield of 465.70 kg/hm(2) (the spatial distribution presents a decreasing trend from the east and southeast to the west and northwest in turn); (2) the proper livestock-carrying capacity in the Three-River Headwaters region is 12.19 million sheep units (hereafter described as "SU"), and the average stocking capacity is 51.27 SU [the proper carrying capacity is above 100 SU/km(2) in the eastern counties, 60 SU/km(2) in the central counties (except Madoi County), and 30 SU/km(2) in the western counties]; and (3) total overgrazing number was 6.52 million SU in the Three-River Headwaters region in 2010, with an average overgrazing ratio of 67.88% and an average overgrazing number of 27.43 SU/km2. A higher overgrazing ratio occurred in Tongde, Xinghai, Yushu, Henan and Zkog. There was no overgrazing in Zhiduo, Tanggula Township and Darlag, Qumerleb and Madoi. The remainder of the counties had varying degrees of overgrazing.

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[20]
Jiang C, Zhang L.Climate change and its impact on the eco-environment of the Three-Rivers Headwater Region on the Tibetan Plateau, China[J]. International Journal of Environmental Research and Public Health, 2015,12:12057-12081.This study analyzes the impact of climate change on the eco-environment of the Three-Rivers Headwater Region (TRHR), Tibetan Plateau, China. Temperature and precipitation experienced sharp increases in this region during the past 57 years. A dramatic increase in winter temperatures contributed to a rise in average annual temperatures. Moreover, annual runoff in the Lancang (LRB) and Yangtze (YARB) river basins showed an increasing trend, compared to a slight decrease in the Yellow River Basin (YRB). Runoff is predominantly influenced by rainfall, which is controlled by several monsoon systems. The water temperature in the YRB and YARB increased significantly from 1958 to 2007 (p < 0.001), driven by air temperature changes. Additionally, owing to warming and wetting trends in the TRHR, the net primary productivity (NPP) and normalized difference vegetation index (NDVI) showed significant increasing trends during the past half-century. Furthermore, although an increase in water erosion due to rainfall erosivity was observed, wind speeds declined significantly, causing a decline in wind erosion, as well as the frequency and duration of sandstorms. A clear regional warming trend caused an obvious increasing trend in glacier runoff, with a maximum value observed in the 2000s.

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