, 庞国伟, 程圣东Evaluating the potential of vegetation restoration in the Loess Plateau
GAOHaidong, PANGGuowei, CHENGShengdong通讯作者:
收稿日期:2016-10-25
修回日期:2017-03-20
网络出版日期:2017-07-12
版权声明:2017《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
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1 引言
植被是陆地生态系统的主体,在气候调节、生物多样性维持、水土保持等方面发挥着重要作用[1]。由于暴雨集中,土质疏松,植被覆盖度低,导致黄土高原成为世界上水土流失最严重的地区[2-4]。随着退耕还林（草）及其他水土保持措施的实施[5-7],黄土高原生态环境明显好转,且黄河输沙量显著降低：头道拐—潼关区间的年均输沙量从1951-1979年的13.4亿t,降至1980-1999年的7.3亿t,2000-2010年,进一步降至3.2亿t[8]。2014年9月,中国政府决定,到2020年,再完成退耕283万hm2。因此,一方面,评估植被恢复效果及其未来发展方向显得十分重要;另一方面,研究黄土高原植被恢复潜力,对于指导未来黄土高原植被建设,科学预测黄河水沙变化,具有重要意义。目前,关于植被恢复潜力研究,主要从水分植被承载力角度进行,郭忠升等[9]给出的水分植被承载力定义是：土壤水分紧缺地区补充给土壤的部分雨水所能承载植物的最大负荷。根据该定义,王延平等[10]研究发现,陕北米脂土壤水分可承载的苜蓿最大产量为3992.2~4173.7 kg/hm2;陕北黄土丘陵沟壑区台地土壤水分可承载的杏树生物量为3728 kg/hm2,坡地为2423 kg/hm2[11];在保证土壤水分可持续利用,并避免2 m以下土壤干化的基础上,刘丙霞等[12]模拟结果表明神木六道沟流域柠条和紫花苜蓿的最大土壤水分植被承载力分别为4800 kg/hm2、1200 kg/hm2。可见,目前关于植被恢复潜力的研究,主要集中在小流域以及主要水土保持物种上,整个黄土高原尺度上的植被恢复潜力研究尚未见报道。
遥感技术的出现,为大尺度、长时期、实时动态监测提供了可能[13-14],成为研究区域植被时空变化的重要手段[15]。而基于SPOT/VGT数据计算的NDVI（Normalized Difference Vegetation Index）指数[16-17],被广泛地用于研究植被覆盖变化及其与气候、环境之间的响应关系[18]。植被受土壤、气候、地形的综合影响,随着地理信息科学的进步和发展,在大尺度上进行植被恢复潜力研究成为可能。
本文采用1999-2013年SPOT VGT NDVI数据集,通过运用线性回归、Hurst指数分析法、统计学方法以及地理空间分析技术,首先对黄土高原植被恢复效果和未来恢复趋势进行了分析;其次根据干旱指数、地形特征、地理分区建立了黄土区和土石山区植被响应曲线,并进行了植被恢复潜力的研究,计算了整个黄土高原植被恢复潜力指数;最后对黄土高原植被恢复措施的适宜性进行了探讨。研究结果以期为黄土高原生态文明建设提供科学支撑。
2 材料与方法
2.1 黄土高原概况
黄土高原位于100°52′E~114°33′E、33°41′N~41°16′N之间（图1）,总面积64.6万km2。黄土高原地势西北高、东南低,千沟万壑,地形破碎。气候属大陆性季风气候,多年平均温度9~12℃,多年平均降水量从西北到东南变化于100~800 mm之间,降水集中在6-9月,且以暴雨为主。植被由东南向西北可划分为森林带、森林草原带、典型草原带、荒漠草原和草原化荒漠带。黄土高原地表主要由黄土覆盖,厚度一般50~200 m,黄土疏松绵软,水土流失严重,面积达45.4万km2,是黄河泥沙的主要来源区。
显示原图|下载原图ZIP|生成PPT图1黄土高原分区图
-->Fig. 1Zoning map of the Loess Plateau
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土地利用以草地、耕地以及林地为主,是中国重要的旱作农业区。2013年总人口11421万人,其中农业人口占总人口的65%,人口密度177人/km2。城镇居民人均可支配收入22000元,农民人均纯收入6995元,恩格尔系数33%,经济发展水平较低。
2.2 数据来源与处理
2.2.1 黄土高原分区 黄土高原地区地理分区图来源于黄土高原科学数据中心（http://loess.geodata.cn）,该数据集是把1:100万地形图和遥感影像数据结合后,按地貌特征及治理方向,将整个黄土高原共划分为黄土丘陵沟壑区、黄土高塬沟壑区、风沙区、河谷平原区以及土石山区5大区（图1）。黄土丘陵沟壑区和黄土高塬沟壑区土壤类型以黄绵土为主,疏松多孔,颗粒组成以细沙粒和粉粒为主,剖面发育不明显,有机质含量低,土层软绵,透水性及可耕性良好,土壤侵蚀严重;土石山区主要的土壤类型为褐土和栗钙土,褐土保水保肥性能好,但有机质含量较低,氮磷不足,土壤质地比较粘重,开垦后易发生水土流失。栗钙土在形成过程中多存在弱度的石膏化和盐化过程,表层为栗色或暗栗色的腐殖质,腐殖质层以下为含有多量灰白色斑状或粉状石灰的钙积层;风沙区主要的土壤类型为风沙土,风沙土主要由粗质机械组成,成土作用微弱,很难形成十分成熟的土壤和完整的剖面,一般发育成A-C型剖面;汾渭平原主要发育有塿土,而银川平原和河套平原多分布有灌淤土。2.2.2 数字高程模型（DEM） 数字高程模型（DEM）来源于中国科学院计算机网络信息中心地理空间数据云（http://www.gscloud.cn）,坐标系统为UTM/WGS 84,空间分辨率为30 m。经过拼接和裁剪,获得整个黄土高原DEM,在GIS软件中分别提取坡度和坡向,以15°为界限,将坡度分为15°以下和15°以上两个等级;坡向分为阴坡和阳坡两个类别,其中阳坡为南坡、西坡、西南以及西北坡,阴坡为北坡、东坡、东北以及东南坡。在GIS软件支持下,根据坡度和坡向,将地形叠置分为4类,分别是小于15°阴坡、小于15°阳坡、大于15°阴坡和大于15°阳坡,面积比例分别为37.81%、36.54%、12.89%、12.76%（图2）。
显示原图|下载原图ZIP|生成PPT图2黄土高原地形特征图
-->Fig. 2Terrain characters on the Loess Plateau
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2.2.3 气象数据 本文共收集黄土高原降水数据272站,气象数据122站,使用FAO Penman-Monteith公式计算参考蒸散发（ET0）：
式中：ET0为参考蒸散发（mm/d）;Rn为参考作物冠层表面净辐射（MJ/（m2·d））;G为土壤热通量（MJ/（m2·d））;T为2 m处日平均气温（℃）;U2为2 m处日平均风速（m/s）;es为饱和水汽压（kPa）;ea为实际水汽压（kPa）;△为饱和水汽压与温度曲线的斜率（kPa/℃）;γ为干湿表常数（kPa/℃）。
在GIS软件支持下,使用克里格插值法对降水量（P）和参考蒸散发（ET0）进行空间插值,并使用式（2）计算干旱指数（图2）：
式中：r为干旱指数;ET0为年参考蒸散发（mm）;P为年降水量（mm）。
2.2.4 NDVI数据集 NDVI数据为长时间序列SPOT Vegetation植被指数数据集,来源于VITO Earth Observation（http://www.vito-eodata.be/）。时间从1999年1月-2013年12月,空间分辨率为1 km,时间分辨率为逐旬。采用最大值合成法（Maximum Value Composite, MVC）,获得逐月和逐年的SPOT VEG NDVI数据。
植被覆盖度由式（3）反演获得：
式中：fc为植被覆盖度;NDVI为所求像元的植被指数;NDVImax和NDVImin分别为研究区内NDVI的最大值和最小值。
2.2.5 土地利用数据 土地利用数据来源于2013年中国1:10万土地利用数据库,在Landsat TM和中国环境1号卫星（HJ-1）影像的基础上,采用人机交互解译获得。土地利用一级类型评价精度为94%,二级类型分类精度为91%[19]。
2.3 分析方法
2.3.1 趋势分析 线性回归方法被广泛用于分析植被覆盖的变化趋势[20],计算方法为：式中：P为1999-2013年NDVI变化速率;i为年序号,从1999年到2013年,i依次取1到15;n为研究时段长度;NDVIi为第i年的NDVI值。NDVI变化幅度值（E）计算式为[21]：
E值可以反映NDVI随时间的变化方向,E>0,表示植被覆盖为增加趋势,数值越大增加越快;反之,表示植被覆盖呈下降趋势;E = 0表示植被覆盖无变化。
2.3.2 Hurst指数 Hurst指数有效预测时间序列未来变化趋势,被广泛用于水文学、气候学以及生态学等领域[22-23]。计算方法如下：
对于一时间序列{ξ(t)},t=1, 2, …, n,对于任意正整数τ,定义：
均值序列：
累计离差：
极差：
标准差：
若存在R/S∝τH,则说明时间序列存在Hurst现象,H值称为Hurst指数,其值可在双对数坐标系（lnτ, lnR/S）中用最小二乘法拟合得到。Hurst指数介于0到1之间：H = 0.5,表明该时间序列是一个随机序列,在未来变化中没有趋势;H>0.5,表明该时间序列是一个持续性序列,未来发展趋势和现在相同,且H越接近于1,持续性越强;H<0.5,表明时间序列具有反持续性,未来发展趋势和现在相反,且H越接近于0,反持续性越强。
2.3.3 植被恢复潜力确定方法 关于植被恢复潜力研究,目前研究主要从水分的植被承载力入手,研究一定水分条件下部分物种的植被承载能力,本文采取统计分析方法进行植被恢复潜力研究。原则是：“生境越相似的区域,植被恢复潜力越接近”。基于此原则,采用的分析方法如下：首先根据黄土高原分区图（图1）,将整个黄土高原分为黄土区（1）、土石山（2）、平原区（3）以及风沙区（4）4个大区,各区内土壤条件基本相似。在此基础上,叠加黄土高原地形特征图（图2）的4个地形分类（Ⅰ.坡度小于15°,阴坡;Ⅱ.坡度小于15°,阳坡;Ⅲ.坡度大于15°,阴坡;Ⅳ.坡度大于15°,阳坡）,将整个黄土高原分为16类。最后将干旱指数（图3）分为[1.20, 1.50)、[1.50, 1.75)、[1.75, 2.00)、[2.00, 2.25)、[2.25, 2.50)、[2.50, 2.75)、[2.75, 3.00)、[3.00, 3.25)、[3.25, 3.50)、[3.50,3.75)、[3.75, 4.00)、[4.00, 4.50)、[4.50, 5.00)、[5.00, 5.50)、[5.50, 6.00)、[6.00, 6.50)、[6.50, 7.00)、[7.00, 7.50)、[7.50, 8.00)、[8.00, 8.50)、[8.50, 9.00)、[9.00, 9.50)、[9.50, 10.00)、[10.00, 10.50)、[10.50, 11.00)、[11.00, 11.50)、[11.50, 12.00)、[12.00, 12.50)、[12.50, 13.00)、[13.00, 13.50],共30个类,和上述16个分类进行叠加,共形成480个计算分区。在每个计算分区内,统计多年平均植被覆盖度的平均值、75%分位数值、90%分位数值以及最大值。根据“生境越相似的区域,植被恢复潜力越接近”原则,在相似的生境条件下,应该有基本相同的植被覆盖度。例如,目前某个区（土壤、干旱指数以及地形条件均一致）的植被覆盖度的最大值为0.85,则有理由认为,这一地区的植被恢复潜力就是0.85,即该区内其他地方植被覆盖度小于0.85的地区,植被覆盖度都有潜力恢复到目前植被覆盖度的最大值0.85。为了避免统计偏差,本文采取90%分位数的植被覆盖度值作为某一立地条件下的植被恢复潜力值。
显示原图|下载原图ZIP|生成PPT图3黄土高原多年平均干旱指数图
-->Fig. 3Multi-year average drought index in the Loess Plateau
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3 结果与分析
3.1 黄土高原植被恢复现状与趋势分析
3.1.1 黄土高原NDVI时空变化特征 根据最大值合成法生成的黄土高原逐年NDVI图像（图4）,统计了整个黄土高原地区NDVI平均值。1999-2013年黄土高原年均NDVI值从0.454增加至0.613,经M-K检验,呈极显著上升趋势（p<0.01）,回归方程为：式中：T为年份。
显示原图|下载原图ZIP|生成PPT图41999年和2013年黄土高原典型年份NDVI分布图
-->Fig. 4NDVI in the Loess Plateau from SPOT NDVI data in 1999 and 2013
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3.1.2 黄土高原NDVI值变化趋势 根据式（4）计算的NDVI变化幅度值（E）表明：整个黄土高原地区E值最小值为-53.96%,最大值为60.10%,平均值为13.52%。E<0的面积比例仅为3.53%;E>0的比例为96.47%,其中,0~20%比例为76.28%、20%~40%比例为20.12%,大于40%比例为0.07%。根据计算的E值空间分布图来看,增加趋势最明显的区域主要位于黄土高原中部的“河龙区间”（图5）,该区域E值平均值为21.59%。这一地区是黄河泥沙的主要来源区,是中国最强烈的侵蚀产沙中心,由于自然和人文特征的独特性,该地区的水沙问题一直是地球科学领域研究的热点[24],同时又是黄土高原生态建设的关键区域,遥感结果显示出该地区植被恢复卓有成效。
3.1.3 黄土高原植被恢复的未来趋势特征 通过计算黄土高原15期NDVI值的Hurst指数,得到黄土高原植被恢复的未来趋势（图6）,进一步研究黄土高原植被恢复方向。根据计算结果,Hurst指数小于0.5表示不可持续,占整个研究区面积的31%。高于0.5表示可持续,占总面积的69%。运用ArcGIS将NDVI变化幅度值（E）（图5）与Hurst指数（图6）叠加,得出,H>0.5且E>0持续改善的地区面积占整个黄土高原总面积的67.08%,可见,黄土高原三分之二的地区植被得到持续改善。
显示原图|下载原图ZIP|生成PPT图51999-2013年黄土高原NDVI变化幅度值
-->Fig. 5Range of NDVI change (E) from 1999 to 2013
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显示原图|下载原图ZIP|生成PPT图6黄土高原NDVI Hurst指数图
-->Fig. 6NDVI Hurst exponent in the Loess Plateau
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3.2 黄土高原植被响应曲线
按照地理特征,黄土高原可以分为黄土丘陵沟壑区、黄土高塬沟壑区、风沙区、河谷平原区以及土石山区。本文将黄土丘陵沟壑区和黄土高塬沟壑区合并为黄土区,共分为黄土区、土石山区、风沙区以及河谷平原区4个分区。黄土区和土石山区是黄土高原植被建设的重点区域。因此,选择黄土区（代码为1）和土石山区（代码为2）为研究对象,分析植被响应曲线。在黄土区内,土层较厚,且土壤类型单一,主要为黄绵土。土石山区土层较薄,主要的土壤类型为褐土。
显示原图|下载原图ZIP|生成PPT图7黄土区植被响应曲线
-->Fig. 7Vegetation response curve in loessal areas
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分析过程如下：首先在整个黄土高原区域内,随机生成10000个数据点;其次,为了避免人工植被、水域以及建设用地对植被响应曲线的影响,根据2013年黄土高原土地利用类型图,将位于耕地、水域以及建设用地的随机点剔除;再次,使用ArcGIS的Extract Multi Values to Points工具,提取剔除后的随机点所在的地形因子（坡度坡向4类）、分区代码、干旱指数以及植被覆盖度;最后分区、分地形拟合干旱指数和植被覆盖度关系曲线,制作植被响应曲线。
3.2.1 黄土区 黄土区植被覆盖度（y）和干旱指数（x）呈指数关系（图7）,函数表达式为：
式中：x为干旱指数;y为植被覆盖度;a、b为拟合系数;e为自然对数。在坡度小于15°的缓坡地区,相关系数均大于0.7,而在15°以上的陡坡区域,植被覆盖度和干旱指数的相关性降低,至0.6左右。在陡坡区域,系数b增大,显示出干旱指数对植被覆盖度的影响力降低,这一现象与黄土高原区植被生长的空间格局相符：峁边线是黄土地貌典型的地形特征变换线,在峁边线上部,坡度缓,植被覆盖度高,峁边线下部,坡度陡,植被覆盖度低。
3.2.2 土石山区 相比黄土区,土石山区植被覆盖度显著增大,而植被响应函数均为线性函数最优,同时相关系数下降（图8）。在坡度小于15°的缓坡地区,相关系数均大于0.6,而在15°以上的陡坡区域,植被覆盖度和干旱指数的相关性降低至0.4。在陡坡区域,系数b增大,这点与黄土区规律一致。
显示原图|下载原图ZIP|生成PPT图8土石山区植被响应曲线
-->Fig. 8Vegetation response curve in soil and rock-mountainous areas
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3.3 黄土高原植被恢复潜力
根据2.3.3节植被恢复潜力确定方法,在黄土高原480个分区内,统计多年平均植被覆盖度的平均值、75%分位数值、90%分位数值以及最大值（图9）。以90%分位数的植被覆盖度值作为某一立地条件下的植被恢复潜力值。而0~5°耕地、水域、建筑用地的植被覆盖度保持不变,在GIS软件中,使用土地利用图层进行判断,使得上述3类用地的植被恢复潜力值等于现状植被覆盖度,得到整个黄土高原植被恢复潜力图（图10）。分析得知,黄土高原平均植被恢复潜力为69.75%,从东南至西北递减,从空间分布看,还可以看出一个明显特征是：同一地带,土石山区植被恢复潜力较大。
显示原图|下载原图ZIP|生成PPT图9黄土高原不同立地条件下植被覆盖度统计特征
-->Fig. 9Statistical characteristics of vegetation coverage under different site conditions in the Loess Plateau
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显示原图|下载原图ZIP|生成PPT图10黄土高原植被现状及其恢复潜力图
-->Fig. 10Vegetation status and its restoration potentiality in the Loess Plateau
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根据现状植被覆盖度与植被恢复潜力值的比值,计算可得植被恢复潜力指数（图11）。可以看出,黄土高原植被恢复潜力指数较高的地区目前集中在北方风沙区及西部的丘陵沟壑区,而东南地区,虽然其植被恢复潜力较高,但是目前由于植被恢复良好,其植被恢复潜力指数较低。
显示原图|下载原图ZIP|生成PPT图11黄土高原植被恢复潜力指数
-->Fig. 11Vegetation restoration potentiality index in the Loess Plateau
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4 讨论与结论
4.1 讨论
傅伯杰等[25]根据构建的自然—社会—经济水资源可持续利用耦合框架,研究表明当前黄土高原植被恢复已接近黄土高原水资源植被承载力的阈值,该承载力阈值在383~528 g C m-2 a-1间浮动。根据本文研究结果,黄土高原地区平均植被覆盖度为60.22%,黄土高原平均植被恢复潜力为69.75%,目前植被覆盖度接近潜力值,整个黄土高原植被覆盖度大约有10%左右的提升空间。黄土高原处于干旱区和半干旱区,降水量与NDVI相关性非常明显,选用黄土高原地区的56个气象站,计算了降水量和NDVI的Pearson相关系数。发现相关系数在-0.3~0之间的气象站有3站（比例为5.36%）,相关系数在0~0.3之间的有12站（比例为21.43%）,相关系数在0.3~0.5之间的有18站（比例为32.14%）,相关系数在0.5以上有23站（比例为41.07）。其中,相关系数在0.3以上的气象站比例为73.21%。
不同降水量下,黄土高原植被恢复速度差别明显（图12）。降水量<375 mm的地区（面积占整个黄土高原总面积的34.48%）,NDVI变化幅度值为9.55%;降水量在375~575 mm之间的地区（面积占整个黄土高原总面积的54.44%）,NDVI变化幅度值显著增大,平均达17.18%,其中425~450 mm之间NDVI变化幅度值最大,为19.73%;降水量>575 mm的地区（面积占整个黄土高原总面积的11.08%）,NDVI变化幅度值回落至8.88%左右。
显示原图|下载原图ZIP|生成PPT图12黄土高原不同降水量带植被恢复差别
-->Fig. 12Difference of vegetation restoration for the Loess Plateau under different precipitation thresholds
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黄土高原主要有封禁、种草以及造林三大植被恢复措施。根据调查,整个黄土高原造林措施所占比例高达73.58%,且在不同降水量带下,造林均为主要的植被恢复手段（表1）。在水分不足地区造林,会使土壤干层加剧,并进一步制约植被恢复,黄土高原地区的林地普遍存在下伏土壤干层,并且土壤干层的发育较为严重[26],陡坡土壤水分亏缺比缓坡严重,阳坡土壤干化程度比阴坡更为严重。
Tab. 1
表1
表1黄土高原不同降水量带植被恢复措施构成(%)
Tab. 1Composition of vegetation restoration measures for the Loess Plateau under different precipitation conditions
| 措施 | <375 mm | 375~450 mm | 450~575 mm | >575 mm | 黄土高原 |
|---|---|---|---|---|---|
| 造林 | 66.88 | 73.60 | 76.92 | 75.41 | 73.58 |
| 种草 | 18.16 | 17.09 | 12.63 | 4.21 | 13.96 |
| 封禁 | 14.96 | 9.31 | 10.45 | 20.38 | 12.46 |
| 合计 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 |
新窗口打开
水分条件是黄土高原植被恢复的主要限制因子,植被恢复措施应“因水制宜”。在黄土高原西部和北部,植被恢复有一定的潜力。在降水量< 375 mm的地区,恢复速度较缓,适宜以封禁措施为主。在降水量介于375~575 mm的地区,得益于该区草本植物的快速恢复,植被恢复速度最快,同时为了避免造林带来的土壤干化,该区域植被恢复主要以种草为宜,在坡度较缓的阴坡地带,可以种植灌木,而在部分河滨地带和地下水位较高的沟谷,可以适当种植一些木本植物,物种选择上应以乡土树草种为主,避免单一物种种植。
4.2 结论
1999年退耕还林（草）以来,黄土高原植被覆盖度呈显著上升趋势,黄土高原地区目前植被覆盖度为60.22%,而植被恢复潜力为69.75%,整个黄土高原植被覆盖度大约有10%左右的提升空间,主要集中在北方风沙区及西部的丘陵沟壑区。历史时期,黄土高原的植被覆盖均以草本植物为主,本文建议黄土高原植被恢复以封禁和种草为主。同时,为避免造林导致的土壤干化加剧,依据气候—地形—土壤条件的差别,合理选择植被恢复措施,具有十分重要意义。黄土高原植被恢复潜力和植被恢复适宜性研究是一个长期系统的工作,本文重点探讨了植被恢复潜力,而关于黄土高原植被恢复的适宜性,未来仍需深入研究。The authors have declared that no competing interests exist.
参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子
| [1] | Long-term spatial–temporal dynamics of vegetation coverage is a key problem of issues include global climate change study, regional ecological process monitoring and ecosystem management. Based on SPOT-VGT 10-day composite data over the Circum Bohai Bay Region from 1999 to 2009, this paper performs the Mann–Kendall test and calculates the trend Slope ( β ) and Hurst index of time series data to study the temporal trends and long-range dependence of Normalized Difference Vegetation Index (NDVI) on 102km spatial scale, and plots monthly calendar and seasonal succession map using spatial analysis techniques, and then analyze and reveal the spatial–temporal characteristics of vegetation coverage. Main findings are as follows: (1) dense vegetation coverage mainly distribute in mountainous and hilly areas dominated by forest and shrub, followed by vast plain areas with well developed agricultural industry, and low vegetation coverage mainly distribute along part of coastal beaches, surrounding most of urban areas and large inland water bodies and in the semi-arid agro-pasture intertwined zone at the north-west corner of Hebei province. (2) Overall, vegetation coverage is high in summer–autumn and low in winter–spring and the increasing trend from 1999 to 2009 on monthly, seasonal and annual scales are significant in most areas. An increased seasonal difference is detected because the improving trend of vegetation coverage in growing seasons is more remarkable than that in non-growing seasons. Furthermore, significant long-range dependence of NDVI time series is detected in most areas which indicate that the trend of vegetation coverage change from 1999 to 2009 will definitely persist in the near future. (3) Both variation of vegetation coverage in the past and its long-range dependence show significant spatial differences on macro spatial scale, in detail, improving trend of vegetation coverage in Liaoning province is very significant and it will persist in the near future because more than 85% of the pixels in Liaoning province has positive Slope ( β ) and Hurst index great than 0.5, correspondingly, only very mild signal of enlarged seasonal difference is detected in Liaoning province. On the contrary, very large extent of vegetation coverage in Beijing–Tianjin–Hebei area and Shandong province show significant degrading trend and enlarged seasonal difference despite the overall improving trend in these areas in the past 1102years. Areas with degrading trend of vegetation coverage in the past and future trends point to continuing degrading account for 6.71% and 9.84% in Beijing–Tianjin–Hebei area and Shandong province, respectively, while it is below 6.00% in Liaoning province. (4) Areas with degrading trend of vegetation coverage in the past and future trends point to continuing degrading show the overall spatial pattern of scattering on macro scale and aggregating on fine scales, specifically, areas surrounding large inland water bodies, along part of coastal beaches and surrounding urban areas indicates strong tendency of degrading in the past and prospective degrading trend in the near future. (5) Complicated driving forces of vegetation coverage change on macro spatial scale are revealed by the spatial patterns of the coupling index between Slope ( β ) and Hurst index, in detail, rapid and extensive urbanization undergone on multi-spatial scales is the most important man-made force of vegetation coverage degrading, large scale agricultural activities overall improve the vegetation coverage and enlarge its seasonal difference; while factors include water level changes near large inland water bodies, water–salt dynamics at coastal beach and so on are the main natural driving forces of vegetation degradation. The findings in this paper are useful for wise regional ecosystem management in Circum Bohai Bay Region. |
| [2] | Geomorphic evidence shows that most of the agriculturally used slopes in the Old and New Worlds had already been affected by soil erosion in earlier, prehistoric times. Early descriptions of soil erosion are often very vague. With regard to the Roman Times, geomorphic evidence shows seemingly opposing results, ranging from massive devastation to landscapes remaining stable for centuries. Unfortunately, historical documentation is lacking. In the following centuries, historical records become more frequent and more precise and observations on extreme soil erosion events are prominent. Sometimes they can be clearly linked to geomorphic evidence in the field. The advent of professional soil conservation took place in the late eighteenth century. The first extensive essay on soil conservation known to the Western world was published in Germany in 1815. The rise of professional soil conservation occurred in the late nineteenth and early twentieth centuries. Soil remediation and flood prevention programs were initiated, but the long-term success of these actions remains controversial. In recent years, increasing interest is to recover any traditional knowledge of soil management in order to incorporate it into modern soil conservation strategies. The study shows that local and regional variations in natural settings, cultural traditions, and socioeconomic conditions played a major role for the dynamics and the rates of soil erosion on a long-term perspective. Geomorphic evidence and historical sources can often complement each other, but there should be also an awareness of new pitfalls when using them together. |
| [3] | The Revised Universal Soil Loss Equation (RUSLE) was used in conjunction with geographic information system (GIS) mapping to determine the influence of land use and topography on soil erosion on the Loess Plateau during the period 2000 to 2010. The average soil erosion on the Loess Plateau was 15.202t02ha 61021 02yr 61021 in 2000–2010. Most of the Loess Plateau fell within the minimal and low erosion categories during 2000 to 2010. Forest, shrub and dense grassland provided the best protection from erosion, but the decadal trend of reduced soil erosion was greater for the lower vegetation cover of woodland and moderate and sparse grassland. Midslopes and valleys were the major topographical contributors to soil erosion. With slope gradient increased, soil erosion significantly increased under the same land use type, however, significant differences in soil erosion responding to slope gradients differed from land uses. The results indicate that the vegetation restoration as part of the Grain-to-Green Program on the Loess Plateau has been effective. |
| [4] | Policy plays a very important role in natural resource management as it lays out a government framework for guiding long-term decisions, and evolves in light of the interactions between human and environment. This paper focuses on soil and water conservation (SWC) policy in the Yellow River Basin (YRB), China. The problems, rural poverty, severe soil erosion, great sediment loads and high flood risks, are analyzed over the period of 1949–present using the Driving force–Pressure–State–Impact–Response (DPSIR) framework as a way to organize analysis of the evolution of SWC policy. Three stages are identified in which SWC policy interacts differently with institutional, financial and technology support. In Stage 1 (1949–1979), SWC policy focused on rural development in eroded areas and on reducing sediment loads. Local farmers were mainly responsible for SWC. The aim of Stage 2 (1980–1990) was the overall development of rural industry and SWC. A more integrated management perspective was implemented taking a small watershed as a geographic interactional unit. This approach greatly improved the efficiency of SWC activities. In Stage 3 (1991 till now), SWC has been treated as the main measure for natural resource conservation, environmental protection, disaster mitigation and agriculture development. Prevention of new degradation became a priority. The government began to be responsible for SWC, using administrative, legal and financial approaches and various technologies that made large-scale SWC engineering possible. Over the historical period considered, with the implementation of the various SWC policies, the rural economic and ecological system improved continuously while the sediment load and flood risk decreased dramatically. The findings assist in providing a historical perspective that could inform more rational, scientific and effective natural resource management going forward. |
| [5] | Increased soil erosion on sloped land has become a significant environmental concern in China that has been attributed to human activities such as deforestation, over-cultivation, and over-grazing of livestock. In order to reduce soil erosion on sloped lands, the Chinese government has responded by implementing large-scale, ecological rehabilitation programs, including the "Grain for Green" reforestation project. This program involves financial incentives to transition farmers into other economic activities with the goal of reducing ecological pressures and degradation. Because of the scope and potential impacts from these programs, detailed research is needed to understand their social and ecological effects. This paper reports on research conducted in Tianquan County, Sichuan Province, and Wuqi County, Shaanxi Province, that evaluates the effects of the program upon local economies and household livelihood systems. The paper argues that the successful conversion of farmland under "Grain for Green" depends upon local government involvement, local economic development, and funding for local projects. Without economic development within rural economies, we conclude that farmers will remain dependent upon continued subsidy assistance to meet the policy's ambitious environmental restrictions, thereby undermining the program's long-term sustainability. |
| [6] | Soil erosion in terrestrial ecosystems, as an important global environmental problem, significantly impacts on environmental quality and social economy. By protecting soil from wind and water erosion, terrestrial ecosystems supply human beings with soil erosion control service, one of the fundamental ecosystem services that ensure human welfare. The Loess Plateau was one of the regions in the world that suffered from severe soil erosion. In the past decades, restoration projects were implemented to improve soil erosion control in the region. The Grain-to-Green project, converting slope croplands into forest or grasslands, launched in 1999 was the most massive one. It is needed to assess the change of soil erosion control service brought about by the project. This study evaluated the land cover changes from 2000 to 2008 by satellite image interpretation. Universal Soil Loss Equation (USLE) was employed for the soil erosion control assessment for the same period with localized parameters. Soil retention calculated as potential soil erosion (erosion without vegetation cover) minus actual soil erosion was applied as indicator for soil erosion control service. The results indicate that ecosystem soil erosion control service has been improved from 2000 to 2008 as a result of vegetation restoration. Average soil retention rate (the ratio of soil retention to potential soil loss in percentage) was up to 63.3% during 2000–2008. Soil loss rate in 34% of the entire plateau decreased, 48% unchanged and 18% slightly increased. Areas suffering from intense erosion shrank and light erosion areas expanded. Zones with slope gradient of 8°–35° were the main contribution area of soil loss. On average, these zones produced 82% of the total soil loss with 45.5% of the total area in the Loess Plateau. Correspondingly, soil erosion control capacity was significantly improved in these zones. Soil loss rate decreased from 500002t02km 612 02yr 611 to 360002t02km 612 02yr 611 , 690002t02km 612 02yr 611 to 470002t02km 612 02yr 611 , and 850002t02km 612 02yr 611 to 550002t02km 612 02yr 611 in the zones with slope gradient of 8°–15°, 15°–25°, and 25°–35° respectively. However, the mean soil erosion rate in areas with slope gradient over 8° was still larger than 360002t02km 612 02yr 611 , which is far beyond the tolerable erosion rate of 100002t02km 612 02yr 611 . Thus, soil erosion is still one of the top environmental problems that need more ecological restoration efforts. |
| [7] | . A total of 704 channels, 967 tunnel inlets and 547 mass movements were identified in the study watershed. On the basis of their location and morphology, all the channels were classified into four types: headwater gullies, hillside gullies, valleyside gullies and ephemeral river channels. Tunnels are associated with 79% of headwater gullies, 48% of hillside gullies, 3% of valleyside gullies and none of ephemeral river channels. Mass movements are dominated by falls in headwater gullies, falls and slides in hillside gullies, and soil creeps in ephemeral stream channels. Statistical tests indicate that there are significant differences in physiographic variables between tunneled and untunneled gullies. Tunnel formation in gullies is intricately affected by topographic conditions, land uses, knickpoint distribution, soil materials and mass movements. |
| [8] | The sediment load of China[rsquor]s Yellow River has been declining. Analysis of 60 years of runoff and sediment load data attributes this decline to river engineering, with an increasing role of post-1990s land use changes on the Loess Plateau. |
| [9] | . |
| [10] | 土壤水分植被承载力是黄土高原生态环境建设和可持续发展的核心。该文根据陕北黄土区4种不同立地条件下苜蓿地（MedicagosativaL.）连续3a的降雨、径流、土壤水分动态和生物产量的小区定位观测结果,研究分析了自然降水与土壤水分补给、土壤水分补给与地上部生物量、地上部生物量与土壤水分消耗的关系;并采用FAO法和水量平衡法分别计算出了苜蓿地土壤水分的承载力。结果表明：苜蓿地土壤水分补给量与地上部生物量呈线性关系,地上部生物量与土壤水分消耗量呈二次函数关系。用FAO法估算可得陕北黄土区土壤水分可承载的苜蓿最大产量为3992.2～4173.7kg/hm2;而根据水量平衡原理计算可得陕北黄土区苜蓿地可承载的地上部生物量为2600～3500kg/hm2,比FAO法低16.07%～33.52%。坡向、坡位相同时,坡度增大,承载力降低;坡向、坡度相同时,下坡承载力大于上坡;坡度、坡位相同时,南坡承载力小于北坡。 . 土壤水分植被承载力是黄土高原生态环境建设和可持续发展的核心。该文根据陕北黄土区4种不同立地条件下苜蓿地（MedicagosativaL.）连续3a的降雨、径流、土壤水分动态和生物产量的小区定位观测结果,研究分析了自然降水与土壤水分补给、土壤水分补给与地上部生物量、地上部生物量与土壤水分消耗的关系;并采用FAO法和水量平衡法分别计算出了苜蓿地土壤水分的承载力。结果表明：苜蓿地土壤水分补给量与地上部生物量呈线性关系,地上部生物量与土壤水分消耗量呈二次函数关系。用FAO法估算可得陕北黄土区土壤水分可承载的苜蓿最大产量为3992.2～4173.7kg/hm2;而根据水量平衡原理计算可得陕北黄土区苜蓿地可承载的地上部生物量为2600～3500kg/hm2,比FAO法低16.07%～33.52%。坡向、坡位相同时,坡度增大,承载力降低;坡向、坡度相同时,下坡承载力大于上坡;坡度、坡位相同时,南坡承载力小于北坡。 |
| [11] | <p><font face="Verdana">根据台地和26°坡地杏林地连续3年的降雨、冠层截留、地表径流、土壤水分和生物量的定位观测结果，研究分析陕北黄土区自然降水与土壤水分补给、土壤水分补给与生物量、土壤水分消耗与生物量的关系。提出台地土壤水分可承载的杏树生物量为3 728 kg·hm<sup>-2</sup>,坡地为2 423 kg·hm<sup>-2</sup>，台地杏林地适宜的果实产量为4 714 kg·hm<sup>-2</sup>, 坡地杏林地适宜的产量为3 064 kg·hm<sup>-2</sup>。建议综合应用水保工程、修剪、保墒、花果控制、生长激素等措施，平衡利用雨水资源,实现杏产业的可持续发展。</font></p> . <p><font face="Verdana">根据台地和26°坡地杏林地连续3年的降雨、冠层截留、地表径流、土壤水分和生物量的定位观测结果，研究分析陕北黄土区自然降水与土壤水分补给、土壤水分补给与生物量、土壤水分消耗与生物量的关系。提出台地土壤水分可承载的杏树生物量为3 728 kg·hm<sup>-2</sup>,坡地为2 423 kg·hm<sup>-2</sup>，台地杏林地适宜的果实产量为4 714 kg·hm<sup>-2</sup>, 坡地杏林地适宜的产量为3 064 kg·hm<sup>-2</sup>。建议综合应用水保工程、修剪、保墒、花果控制、生长激素等措施，平衡利用雨水资源,实现杏产业的可持续发展。</font></p> |
| [12] | [D]. [D]. |
| [13] | Ecosystems provide services necessary for the livelihoods and well-being of people. Quantifying and mapping supplies and demands of ecosystem services is essential for continuous monitoring of such services to support decision-making. Area-wide and spatially explicit mapping of ecosystem services based on extensive ground surveys is restricted to local scales and limited due to high costs. In contrast, remote sensing provides reliable area-wide data for quantifying and mapping ecosystem services at comparatively low costs, and with the option of fast, frequent, and continuous observations for monitoring. In this paper, we review relevant remote sensing systems, sensor types, and methods applicable in quantifying selected provisioning and regulatory services. Furthermore, opportunities, challenges, and future prospects in using remote sensing for supporting ecosystem services' quantification and mapping are discussed. |
| [14] | Maintaining the integrity of riparian ecosystems whilst continuing to reserve and extract water for other purposes necessitates a greater understanding of relationships between riparian vegetation and water availability. The Normalised Difference Vegetation Index (NDVI) is a good indicator for identifying long-term changes in vegetated areas and their condition. In this study, we use regression tree analysis to investigate long term NDVI data (23 years) at semi-arid riparian areas in the Namoi catchment, Australia. Climatic factors (temperature and rainfall), surface water (flow and flooding) and groundwater levels are analysed collectively. We find that in general maximum temperature is the variable that primarily splits NDVI values, followed by antecedent 28-day rainfall and then inter-flood dry period and groundwater levels. More rain is required in the warmer months compared to cooler months to achieve similar mean NDVI values in tree patches or areas of high NDVI in riparian zones, presumably because of higher evaporation. Inter-flood dry period is shown to be important for maintenance of NDVI levels, particularly when rainfall is limited. Shallower groundwater levels sustain the NDVI and hence vegetation greenness when conditions are cooler and wetter. |
| [15] | Upland forested areas of watersheds undergo changes due to many factors including ecological succession, natural disturbances and human activity. The rate of natural and man-induced ecological changes in these landscapes is a function of the structural and functional characteristics of the component ecosystems. Analyzing spatial patterns and detecting fragile areas are thus crucial for making previsions about the chance and rate of disturbance propagation within and between the ecosystems. In this study we have tested the hypothesis of occurrence of a relationship between the extent of temporal change and spatial heterogeneity of mixed forested areas in the watershed of two Italian volcanic lakes by using remotely sensed data. Landsat images were acquired in summer 1987, 1992 and 2000, when Nature Reserves were established, and the temporal variation in the Normalized Difference Vegetation Index (NDVI) was determined by the change detection analysis. To analyze the spatial variability of NDVI, semivariograms were calculated using data from five randomly chosen forested areas (10 km 2 -wide) per watershed. Results show that NDVI varied greatly across the two study sites and most of the variation was spatially structured. NDVI varied also over time. A linear positive relationship was observed between the number of pixels changing between dates and the semivariogram range, as the maximum distance of spatial dependence estimated from the starting NDVI image. Spatial homogeneity of NDVI is thus suggested as an indicator of intrinsic fragility (i.e. susceptibility to change) of mixed forests and the semivariogram range as a rapid estimator that can be considered by forest managers and agencies. |
| [16] | Plant phenology is one of the main indicators of climate or other environmental processes. This paper assesses the detection accuracy of start of season (SOS) and end of season (EOS) for grassland vegetation in north China from 2001 to 2010 using SPOT-VEGETATION normalized difference vegetation index (NDVI) data sets and in situ observations. The cumulative NDVI is calculated and fitted using a logistic model to identify phenological transition dates. The curvature of the fitted logistic models predicts phenological transition dates that correspond to the times at which the curvature in the yearly integrated NDVI exhibits local minimums or maximums. Validating with in situ observations, phenological dates are extracted from satellite time series data and are accurate to within 10 days. The spatial trends of SOS and EOS are very similar for 2001-2010. SOS mainly occurs from the day of year (DOY) 110 to DOY 170, and EOS occurs from DOY 240 to DOY 300. SOS displays a marked delay from south to north, while EOS gradually advances, indicating regional differences in climate and terrain. However, the effect of latitude and longitude on the average EOS of alpine grasslands is not significantly different, while SOS at low latitude and high longitude is 10 days earlier than at high-latitude and high-longitude regions. We detected an overall advance in SOS of 3.1 days over 10 years, and a 1.3-day delay in EOS. However, the amplitude is low (about 5 days) and the changes in most regions are not significant (close to zero). The results in this paper are concordant with many reported studies that explored the phenology of grasslands in North China, which is an important component of global grasslands science. |
| [17] | To understand the variation and patterns of vegetation coverage in the Yellow River Basin, as well as to promote regional ecological protection and maintain ecological construction achievements, MOD13Q1 data at a resolution of 250 m were used to calculate the annual average normalised difference vegetation index (NDVI) in a time series from 2000 to 2010. Using a variation coefficient, a Theil en Median trend analysis, the Mann揔endall test, and the Hurst index method, this study investigated the temporal and spatial variations of vegetation coverage characteristics of the Yellow River Basin. The results showed that (1) the vegetation coverage of the Yellow River appeared to have an overall trend of high in the southeast and west and low in the northwest; (2) the averaged NDVI of the whole basin fluctuated in a range of 0.3 to 0.4 from 2000 to 2010 (from 2000 to 2004 there were larger variations and these have been growing rapidly since 2005); (3) the NDVI was stable, 73.4% of the vegetation-coverage area fluctuated with a low-to-medium amplitude, while 27.6% of the area varied by a large amplitude; (4) the regions with improved vegetation coverage (62.9%) were far greater than the degraded regions (27.7%), while the sustained invariant area accounted for 9.4% of the total vegetation coverage regions; and (5) 86% of the vegetation-covered area was positively sustainable. The areas with sustainable improvement accounted for 53.7% of the total vegetation coverage area; the invariant area accounted for 7.8%. The area with sustainable degradation was 24.5%; the future variation in trends of the residual (14%) could not be determined. Therefore, continuous attention must be given to the variation in trends of vegetation in the sustainably degraded and underdetermined regions. |
| [18] | Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l bservation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth. |
| [19] | Land-use/land-cover changes (LUCCs) have links to both human and nature interactions. China’s Land-Use/cover Datasets (CLUDs) were updated regularly at 5-year intervals from the late 1980s to 2010, with standard procedures based on Landsat TMETM+ images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows.Land-use changes (LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years (1990–2010). The area of cropland change decreased in the south and increased in the north, but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included (1) an accelerated expansion of built-up land in the Huang-Huai-Hai region, the southeastern coastal areas, the midstream area of the Yangtze River, and the Sichuan Basin; (2) shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China; (3) continuous transformation from rain-fed farmlands in northeast China to paddy fields; and (4) effectiveness of the “Grain for Green” project in the southern agricultural-pastoral ecotones of Inner Mongolia, the Loess Plateau, and southwestern mountainous areas. In the last two decades, although climate change in the north affected the change in cropland, policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century, the anthropogenic factors that drove variations in land-use patterns have shifted the emphasis from one-way land development to both development and conservation.The “dynamic regionalization method” was used to analyze changes in the spatial patterns of zoning boundaries, the internal characteristics of zones, and the growth and decrease of units. The results revealed “the pattern of the change process,” namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning, variations in unit boundaries, and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the “pattern” and “process” of land use and the causes for changes in different types and different regions of land use were explored. |
| [20] | |
| [21] | The use of normalized difference vegetation index (NDVI) data acquired with multiple satellite sensors has become a necessity in research fields such as agriculture, land-use and land-cover change and changes in the natural environment, where fast changes are taking place. A good understanding of these changes is a strong requirement of long-time-series monitoring programmes. In this paper, VEGETATION 10-day composite (VGT-S10) NDVI data with a 165×651 km resolution, covering the period from April 1998 to December 2006 and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data with a 865×658 km resolution, covering the period form April 1998 to December 2003 are used. The differences between the datasets were analysed to enable an unbiased comparison between the two datasets and to enable the description of the characteristics of non-system related differences between the NDVI values acquired from the VGT and AVHRR sensors. A correlation analysis was applied to validate a linear relationship between the two types of NDVI products. This study led us to conclude that most of the Chinese land surfaces elicit good linearity between the VGT and GIMMS NDVI values. It also indicated that the correlations partly depend on vegetation density. A pixel-based one-dimensional linear regression was used to describe the relationship between the two datasets. Significance testing demonstrates that the model is valid for most land-cover types occurring in China. Finally, the VGT NDVI covering the period from 2003 to 2006 is converted to the GIMMS NDVI for the same period. A comparison of the trends calculated with the VGT NDVI and the GIMMS NDVI from the period 1998 to 2006 demonstrates the validity of the regression model when evaluated in detail. |
| [22] | |
| [23] | Rainwater harvesting could increase the resilience of ecosystems on the Loess Plateau and thus ensure the sustainability of livelihoods that depend on them. As such, it is a key component of strategies for adapting to global climate change. In this study, we used a new method to quantify the rainwater harvesting potential (RWHP) across the whole Loess Plateau and to characterize its spatial and temporal variation over the last four decades on the basis of the variable infiltration capacity model. It was found that that the mean RWHP of the study region was 731.1065×6510865m3, and the average water layer thickness was 114.3465mm. There is considerable scope for rainwater harvesting across the Loess Plateau as a whole, to the extent that it could potentially provide enough water to implement the ‘Grain for Green’ Project. The annual average RWHP decreased slightly from 1971 to 2010, and Hurst exponent analysis indicated that this trend will exhibit long-term persistence. The annual RWHP was highest in the southeast of the Loess Plateau and lowest in the northwest. Areas with high RWHP values tended to be clustered around the middle reach of the Yellow River. For most areas, there was no significant change between 1971 and 2010. Those areas for which there was a significant decrease in RWHP were primarily located around the upper–middle reaches of the Weihe River, the upper reach of Jinghe River, the eastern Guanzhong Plain, the Qinhe River watershed and the area around Dongsheng. Quantitative assessments of RWHP are likely to be useful for guiding the development and use of innovative rainwater harvesting technologies around the world and could help to relieve the problems caused by water shortages on the Loess Plateau while simultaneously eliminate the major cause of soil erosion. Copyright 08 2012 John Wiley & Sons, Ltd. |
| [24] | 以河龙区间42个流域为对象，在流域地貌格局信息提取和侵蚀产沙过程特征指标计算及其相互关系分析的基础上，探讨地貌格局对流域侵蚀产沙过程的影响。结果表明：①在河道系统水平，河流数量、长度等几何特征指标和河流分叉率（Rb12）、分级率（Rd32）、相邻级别间的河流长度比等形状特征指标与流域侵蚀模数显著相关；②在流域系统水平，坡度粗糙度、相对高差、圆度比、高长比是影响流域侵蚀产沙过程的主要指标，其中坡度粗糙度是最根本的解释变量；③各地貌格局因子间相互作用复杂，且对侵蚀过程的影响要强于泥沙输移过程，其通径分析模型对流域侵蚀模数、输沙模数和泥沙输移比变化的解释度分别为65％、33％和20％。这对正确认识影响流域侵蚀产沙过程的格局因素和建立准确的过程模型，具有重要参考价值。 . 以河龙区间42个流域为对象，在流域地貌格局信息提取和侵蚀产沙过程特征指标计算及其相互关系分析的基础上，探讨地貌格局对流域侵蚀产沙过程的影响。结果表明：①在河道系统水平，河流数量、长度等几何特征指标和河流分叉率（Rb12）、分级率（Rd32）、相邻级别间的河流长度比等形状特征指标与流域侵蚀模数显著相关；②在流域系统水平，坡度粗糙度、相对高差、圆度比、高长比是影响流域侵蚀产沙过程的主要指标，其中坡度粗糙度是最根本的解释变量；③各地貌格局因子间相互作用复杂，且对侵蚀过程的影响要强于泥沙输移过程，其通径分析模型对流域侵蚀模数、输沙模数和泥沙输移比变化的解释度分别为65％、33％和20％。这对正确认识影响流域侵蚀产沙过程的格局因素和建立准确的过程模型，具有重要参考价值。 |
| [25] | China[rsquor]s /`Grain for Green[rsquor] revegetation programme has potential to help mitigate climate change. However, the increased water demand in the Loess Plateau is approaching a level that will impact on water availability to meet human demand. |
| [26] | 78Soil-water scarcity results from the excessive depletion of deep soil water by artificial/natural vegetation, strong evapotranspiration, and long-term insufficient rainwater supply, may lead to soil desiccation, and serious soil desiccation will gradually lead to the formation of a dried soil layer (DSL). DSL has great negative effect on ecological and hydrological process, e.g., impacts the water cycle in soil–plant–atmosphere systems by cutting off water interchange between upper soil and groundwater, leads to soil degradation, regional microclimate environment aridity, failure to afforestation and reduction of vegetation, and poor renewal by natural germination, and so on. 78Recently, many literatures have reported that DSL distributed widely in the Loess Plateau of China. However, very little work has been done on the spatial structures and patterns of DSL and its related factors on the entire Loess Plateau. 78Therefore, we pre-selected 382 sampling sites across the entire Loess Plateau region (620 000 km 2) based on mapped information using an intensive sampling design. A total of 17 906 disturbed soil samples from various soil depths were collected. Using classical statistics, principal component analysis, residual maximum likelihood (REML), and geostatistical methods, we investigated and characterized DSLs and their spatial distribution. 78A comprehensive knowledge of DSL variability can help scientists and policy makers take effective measures to improve the efficiency of vegetation restoration, water management, and DSL control/restoration. |
