水稻冠层高光谱特征变量与叶片叶绿素含量的相关性研究

为了探讨水稻冠层光谱对叶片叶绿素含量的响应规律,以双季早稻为材料,设置不同施氮量处理的田间试验,测定水稻冠层光谱和叶片叶绿素含量,计算基于冠层反射光谱的特征变量,研究水稻冠层高光谱特征变量与叶片叶绿素含量之间的关系。结果表明:施用氮肥对反射光谱有明显的影响,在可见光范围内,不施氮处理的反射率高于施氮处理,尤其在波长550 nm左右的绿峰处显著增加,在近红外区反射率随施氮量的增加而增加;与叶绿素含量相关性较好的光谱位置参数是红边位置和红谷反射率,随着叶绿素含量的增加,红谷反射率降低,红边位置向长波方向移动;比值植被指数R800/R550、R750/R553和R990/R553,以及色素比值指数PSSRa、PSSRb与chla、chlb、chl(a+b)呈极显著正相关,可以作为水稻冠层叶片叶绿素监测的特征变量。     

不同氮素水平下超高产夏玉米冠层的高光谱特征

为明确超高产夏玉米冠层高光谱特性,对不同生育期、不同氮肥处理下普通玉米与超高产夏玉米冠层高光谱特性进行了比较;对超高产夏玉米冠层高光谱反射率及其衍生的植被指数与干物质积累、叶绿素含量间的相关性进行了分析。结果表明,不同生育期,超高产夏玉米近红外波段的高光谱反射率均大于普通玉米,且在生育后期变化缓慢。开花期与灌浆期之间,出现"红边平台"现象,生育后期红边位置"蓝移"变化量显著小于普通玉米。随着施氮量的增加,超高产夏玉米冠层高光谱反射率及红边位置变化不大,而红边幅值与红边面积呈逐步增大趋势。在相关性分析中,由950、760、810、870nm构成的各类植被指数及红边幅值、红边面积与超高产夏玉米干物质积累量间的相关性最好,红边位置λred可用于估算叶绿素总量、叶绿素a、叶绿素b及类胡萝卜素的含量。不同氮肥下,超高产夏玉米冠层高光谱特征与普通玉米相比具有较大差异,研究结果能为超高产夏玉米生理特性的研究提供支持。     

橡胶树叶片高光谱特征分析

从光谱曲线特征和光谱变换特征分析橡胶树(Hevea brasiliensis)叶片反射曲线特征.结果表明,蓝边、红边、黄边位置特征分别出现于525 nm、725 nm、550 nm波段附近,红谷位置特征变化较大,并提取了红边积分面积等重要光谱变量特征.叶片氮含量与反射光谱的相关分析表明橡胶树叶片氮素敏感波段为700～1300 nm,其中730 nm处相关性最好,达到0.8422的极显著水平,以730 nm处的反射率与叶片氮含量建立线性模型,其复相关系数R2达到0.7094.     

辽西不同针叶被害率的油松冠层光谱特征

通过对辽宁西部大面积油松冠层反射光谱的测定,分析了不同针叶被害率的油松冠层光谱反射率的差异.结果表明:在可见光波段,健康植被和不同针叶被害率的油松冠层光谱均符合绿色植物的光谱特征,但针叶被害率大于60％的油松冠层的红谷不十分明显;在近红外波段,随着针叶被害率的减少,780～1350 nm波段范围的光谱反射率增大,1450～1800和1950 ～2350 nm波段范围的光谱反射率下降.随着针叶被害率的增加,红边拐点波长位置向短波方向移动,即出现“蓝移”现象.不同针叶被害率与红边特征参数和多种植被指数均具有显著或极显著的相关关系,其中,以DVI(1470,860)为参数所建模型能更好地监测油松冠层针叶被害率.     

基于多植被指数组合的冬小麦地上干生物量高光谱估测

为了探究多种植被指数组合与偏最小二乘回归(PLSR)结合对于提高冬小麦地上干生物量估测精度的影响,本研究以氮运筹试验为基础,比较分析了18种植被指数与冬小麦地上干生物量的相关性,筛选出相关性较好的植被指数,建立多种植被指数组合的PLSR模型,并对模型进行评价比较。结果表明:除叶绿素归一化植被指数(NPCI)外各植被指数均与冬小麦地上干生物量有良好的相关性,中分辨率陆地叶绿素成像指数(MTCI)、绿色归一化植被指数(GNDVI)、改进红边比值植被指数(MSR705)和特征色素简单比值指数c(PSSRc)4个植被指数相关系数绝对值均达到0.800以上;多植被指数组合构建的PLSR模型中,以PSSRc、MSR705和MTCI 3个植被指数建立的复合式模型建模集(R2=0.719,RMSE=0.316)和验证集(R2=0.696,RMSE=0.346)表现最佳。因此,多种植被指数组合与偏最小二乘回归(PLSR)结合能有效提高冬小麦地上干生物量的估测精度,为更好地实现冬小麦地上干生物量高光谱遥感估测提供有效技术途径。     

青海祁连瑞香狼毒的光谱差异特征提取

瑞香狼毒是分布在青海省高寒草甸的主要毒害草之一,近年来其迅速蔓延对当地畜牧业危害严重并使草地生态系统日趋退化.在海北州祁连县选取狼毒分布的典型退化草甸,采用2012—2014年狼毒盛花期获取的实测光谱数据,分析狼毒与牧草的光谱差异性.结果表明： 在350~900 nm的可见光 近红外波段,狼毒顶花的光谱反射特征明显异于狼毒叶片和同期牧草等绿色背景,顶花与绿色背景的光谱反射率差异主要体现在红谷和蓝谷.随着盖度的增加,狼毒群落光谱反射率整体升高,在近红外反射峰处狼毒群落与牧草群落光谱反射率具有最大差值,且不同盖度狼毒群落之间的差异性最明显.顶花与绿色背景以及狼毒群落与牧草群落的一阶导数光谱差异均体现在黄边幅值和蓝边幅值.狼毒群落盖度与光谱特征参量的线性回归分析表明,红谷与狼毒群落盖度的相关性最好(R²=0.94),反演狼毒群落盖度的精度最高.盛花期区分狼毒与牧草的主要光谱特征参量为红谷、蓝谷与近红外反射峰,其对应的红、蓝及近红外波段的组合可用于构建狼毒提取的敏感指数.     

灌丛化草原灌木和草本植物光谱特征差异及灌木盖度反演——以内蒙古镶黄旗为例

灌丛化草原已成为我国干旱、半干旱地区一种重要的植被类型,但目前有关灌丛化草原灌木和草本植物光谱特征以及灌木盖度的遥感反演研究鲜有报道。相比传统的野外调查方法,基于遥感影像的灌木盖度反演为实现长时间、大范围灌丛化草原灌木盖度监测提供了可能。该研究综合利用灌木和草本植物光谱特征差异以及季相差异,以内蒙古镶黄旗灌丛化草原区为例,通过线性模型和多端元混合光谱分解模型,实现了利用中分辨率Landsat卫星影像的灌木盖度反演。对镶黄旗优势灌木和草本植物群落的光谱特征分析表明,小叶锦鸡儿(Caragana microphylla)灌木群落的红边斜率、归一化植被指数和改进红边归一化植被指数值均高于以羊草(Leymus chinensis)、克氏针茅(Stipa krylovii)为优势种的草本植物群落,并且其红边位置有"红移"趋势。两种模型反演所得镶黄旗灌丛化草原区灌木盖度平均值均为13%,绝大多数区域灌木盖度低于25%。相比基于盛夏时节影像的多端元混合光谱分解模型,利用灌木和草本植物季相特征差异建立的基于初秋时节影像的线性模型更适合灌丛化草原灌木盖度的遥感反演。     

夏玉米叶片全氮、叶绿素及叶面积指数的光谱响应研究

在夏玉米的不同生长阶段,测定冠层光谱,结合农学参数(LTN、CHL．C、CHL．D、LAI),研究了农学参数的光谱响应规律及其与冠层光谱参量(反射率R、一阶微分光谱R′和红边位置REP)的相关性,并建立预测数学模型,结果表明,拔节一抽雄期,伴随着植株生长和群体壮大,红边斜率缓慢增加,红边位置呈逐渐偏向长波方向的“红移”,至抽雄期群体稳定后而停止;开花-乳熟期,随着个体衰老和群体减弱,红边斜率减小,红边位置又呈偏向短波方向的“蓝移”。利用一阶微分光谱和红边位置推算夏玉米冠层CHL．D不仅是可行的,而且反演精度较高,最适模型分别为指数(R^2=0．9487)和抛物线(R^2=0．9392)。     

天然草地植被覆盖度的高光谱遥感估算模型

利用ASD FieldSpec Pro FR^TM光谱仪,对内蒙古自治区锡林郭勒盟的天然草地进行高光谱遥感地面观测,并计算天然草地植被覆盖度;选择25个高光谱特征变量与天然草地植被覆盖度进行相关性分析.结果表明,共有17个变量通过极显著性检验,尤以红边波长范围内一阶微分波段值总和(SDr)的相关系数0.781为最高在此基础上将观测数据分成两组:一组观测数据作为训练样本(n=49),运用单变量线性、非线性和逐步回归方法,建立植被覆盖度高光谱遥感估算模型;另一组观测数据作为检验样本(n=32),进行精度检验分析结果显示,采用逐步回归分析方法,运用冠层原始反射率数据估算草地植被覆盖度的效果并不理想;而以红边波长范围内一阶微分波段值的总和(SDr)为变量的线性回归方程是最佳估算模型,模型标准差为10.4%,估算精度为83.99%.     

基于小波分析的大豆叶面积高光谱反演

实测了不同水肥耦合、经营制度及有效营养面积条件下的大豆(Glycinemax)冠层高光谱反射率与叶面积指数(LAI),并对光谱反射率、微分光谱与LAI的关系进行了分析;采用比值植被指数(RVI)与归一化植被指数(NDVI)建立了大豆LAI反演模型;采用小波分析对采集的光谱反射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆LAI进行估算。结果表明:大豆LAI与光谱反射率在可见光波段呈负相关;在近红外波段呈正相关;微分光谱在红边处与大豆LAI密切相关(R2=0.92);RVI与NDVI可以提高大豆LAI的估算精度(R2分别达0.79、0.84);各植被指数各有优缺点,应根据需要进行选择;小波能量系数回归模型可以进一步提高大豆叶面积的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆LAI回归确定系数R2高达0.884;以4个和6个小波能量系数建立LAI回归分析模型(R2分别达0.92、0.93),2个模型LAI预测值与大豆LAI实测值线性回归确定性系数R2分别为0.90、0.92。比较可知,小波分析可以对高光谱进行特征变量提取,进而反演大豆生理参数,并且反演的LAI精度较光谱反射率、微分光谱及植被指数都有明显提高,小波分析在植被生理参数的高光谱提取方面有着广阔的应用前景。     

Primary Productivity and Precipitation-Use Efficiency in Temperate Grassland in the Loess Plateau of China

Clarifying spatial variations in aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of grasslands is critical for effective prediction of the response of terrestrial ecosystem carbon and water cycle to future climate change. Though the combination use of remote sensing products and in situ ANPP measurements, we quantified the effects of climatic [mean annual precipitation (MAP) and precipitation seasonal distribution (PSD)], biotic [leaf area index (LAI)] and abiotic [slope gradient, aspect, soil water storage (SWS) and other soil physical properties] factors on the spatial variations in ANPP and PUE across different grassland types (i.e., meadow steppe, typical steppe and desert steppe) in the Loess Plateau. Based on the study, ANPP increased exponentially with MAP for the entire temperate grassland; suggesting that PUE increased with increasing MAP. Also PSD had a significant effect on ANPP and PUE; where more even PSD favored higher ANPP and PUE. Then MAP, more than PSD, explained spatial variations in typical steppe and desert steppe. However, PSD was the dominant driving factor of spatial variations in ANPP of meadow steppe. This suggested that in terms of spatial variations in ANPP of meadow steppe, change in PSD due to climate change was more important than that in total annual precipitation. LAI explained 78% of spatial PUE in the entire Loess Plateau temperate grassland. As such, LAI was the primary driving factor of spatial variations in PUE. Although the effect of SWS on ANPP and PUE was significant, it was nonetheless less than that of precipitation and vegetation. We therefore concluded that changes in vegetation structure and consequently in LAI and/or altered pattern of seasonal distribution of rainfall due to global climate change could significantly influence ecosystem carbon and water cycle in temperate grasslands.     

蒙古高原草原火行为的时空格局与影响因子

采用GIS空间分析方法和L3JRC遥感卫星数据,研究了2000-2007年间蒙古高原草原火行为的时空分布规律,比较了中国内蒙古自治区和蒙古人民共和国草原火行为的差异,分析了植被、气候与人文因素等对草原火行为的影响.结果表明：不同植被类型间的过火率存在极显著差异（P<0.001）,为草甸草原>典型草原>荒漠草原,蒙古人民共和国草原过火率显著高于中国内蒙古自治区(P<0.001),过火频次的分布格局与过火迹地相一致.草原火行为存在明显的年际变化特征,草甸草原(r²=-0.54,P<0.05)和典型草原（r²=-0.61,P<0.05）的年过火率与年降雨量呈负相关关系;草原火集中在降水较少、风速较大的春、秋两季.中国内蒙古自治区的人口密度和载畜密度远高于蒙古人民共和国,而过火率则相反,表明人文因素,尤其是过度放牧是导致中国内蒙古自治区和蒙古人民共和国火行为差异的主要原因.     

Temporal intraspecific trait variability drives responses of functional diversity to interannual aridity variation in grasslands

Interannual climate variation alters functional diversity through intraspecific trait variability and species turnover. We examined these diversity elements in three types of grasslands in northern China, including two temperate steppes and an alpine meadow. We evaluated the differences in community‐weighted means (CWM) of plant traits and functional dispersion (FDis) between 2 years with contrasting aridity in the growing season. Four traits were measured: specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen concentration (LNC), and the maximum plant height (H). CWM for SLA of the alpine meadow increased in the dry year while that of the temperate steppe in Qinghai showed opposing trends. CWM of LDMC in two temperate steppes became higher and CWM of LNC in all grasslands became lower in the dry year. Compared with the wet year, FDis of LDMC in the alpine meadow and FDis of LNC in the temperate steppe in Qinghai decreased in the dry year. FDis of H was higher in the dry year for two temperate steppes. Only in the temperate steppe in Qinghai did the multi‐FDis of all traits experience a significant increase in the dry year. Most of the changes in CWM and FDis between 2 years were explained by intraspecific trait variation rather than shifts in species composition. This study highlights that temporal intraspecific trait variation contributes to functional responses to environmental changes. Our results also suggest it would be necessary to consider habitat types when modeling ecosystem responses to climate changes, as different grasslands showed different response patterns.     

内蒙古草甸草原与典型草原地下生物量与生产力季节动态及其碳库潜力

地下根系是草原生态系统的重要组成部分,其生物量及其净生产力对地下碳库具有直接与间接作用,分析地下生物量季节动态与周转对深入揭示草原生态系统碳库动态及其固碳速率与潜力具有重要意义。应用钻土芯法对不同利用方式或管理措施下内蒙古草甸草原、典型草原地下生物量动态及其与温度、降水的相关性研究表明:草甸草原和典型草原地上生物量季节动态均为单峰型曲线,与上月降水显著正相关(P0.05),但地下生物量季节动态表现为草甸草原呈"S"型曲线,典型草原则是双峰型曲线,与温度、降水相关性均不显著(P0.05);两种草原根冠比和地下生物量垂直分布均为指数函数曲线,根茎型草原地下生物量集中在土壤0—5 cm,丛生型草原地下生物量集中于土壤5—10 cm,根冠比值在生长旺季(7—8月份)最小。草甸草原地下净生产力及碳储量范围分别为2167—2953 g m-2a-1和975—1329 gC m-2a-1,典型草原为2342—3333 g m-2a-1和1054—1450 gC m-2a-1,地下净生产力及其碳储量约为地上净生产力及其碳储量的10倍,具有较大的年固碳能力,且相对稳定;地下净生产力与地上净生产力呈显著负相关性(P0.05);地下生物量碳库是地上生物量碳库的10倍左右,适度放牧可增加地下生产力,但长期过度放牧显著降低其地下生物量与生产力,并使其垂直分布趋向于浅层化。     

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau. These include the alpine meadow and steppe and degraded alpine meadow and steppe. This study aimed at developing a method to estimate aboveground biomass (AGB) for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition (VIUPD) from the spectra to estimate AGB. First, we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type. Next, we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables. At last, we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately, all eight vegetation indices provided good estimates of AGB, with the best predictor of AGB varying among different ecosystems. When AGB of all the five ecosystems was estimated together using a simple linear model, VIUPD showed the lowest prediction error among the eight vegetation indices. The regression models containing dummy variables predicted AGB with higher accuracy than the simple models, which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index (VI). These results suggest that VIUPD is the best predictor of AGB among simple regression models. Moreover, both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models. Therefore, ground-based hyperspectral measurements are useful for estimating AGB, which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.     

Remote sensing monitoring upon the grass production in China

Using MODIS remote sensing data and ground truth data, a thorough investigation was conducted to monitor the productivity of grasslands in China for the year 2005. The Normalized Difference Vegetation Index (NDVI) was first computed from the MODIS data. Then the data from the NDVI images were used to correlate with the grass yield data from the ground sampling campaigns. Six regional models were accordingly established from the correlation for estimation of grass production in the six main types of steppe in China. The main results from the estimation could be summarized as follows: (1) High grass productivity in 2005 was obtained in the following 3 regions: the grassland covering Hulunbuir, Xilinhaote, and the western Daxing'anling, the region including the eastern Qinghai, the northwestern Sichuan and the mid-southern Gansu, and the northwestern Xinjiang region. (2) Total hay output from the grasslands in China amounted to 294213.86 thousand tons in 2005 with an average yield of 829.67 kg/hm2. (3) The following 7 provinces were the largest grass producers in China: Inner Mongolia, Qinghai, Xinjiang, Sichuan, Tibet, Heilongjiang and Gansu. For example, Inner Mongolia produced 60370.82 thousand tons of hay in 2005, and hence became the No. 1 grass producer of China. (4) Among the steppe types, the following 5 had the largest grass production: Alpine meadow, Temperate steppe, Low-land meadow, Temperate meadow steppe and Montane meadow with total production accounting for 62.2% in China. (5) Grass production of the entire China in August 2005 remained at the same level as that in August 2004. However, the situations of major grassland provinces were different: grass production in both Qinghai and Gansu in 2005 increased to 9.02% and 3.63%, respectively, when compared with that in 2004. The grass production in Inner Mongolia decreased by 3%, while the production in Tibet, Xinjiang and Sichuan remains unchanged when compared with that in 2004. These results were very important for grassland administration, pasture grazing and grassland ecosystem studies in China.     

Remote sensing monitoring upon the grass production in China

Using MODIS remote sensing data and ground truth data, a thorough investigation was conducted to monitor the productivity of grasslands in China for the year 2005. The Normalized Difference Vegetation Index (NDVI) was first computed from the MODIS data. Then the data from the NDVI images were used to correlate with the grass yield data from the ground sampling campaigns. Six regional models were accordingly established from the correlation for estimation of grass production in the six main types of steppe in China. The main results from the estimation could be summarized as follows: (1) High grass productivity in 2005 was obtained in the following 3 regions: the grassland covering Hulunbuir, Xilinhaote, and the western Daxing'anling, the region including the eastern Qinghai, the northwestern Sichuan and the mid-southern Gansu, and the northwestern Xinjiang region. (2) Total hay output from the grasslands in China amounted to 294213.86 thousand tons in 2005 with an average yield of 829.67 kg/hm2. (3) The following 7 provinces were the largest grass producers in China: Inner Mongolia, Qinghai, Xinjiang, Sichuan, Tibet, Heilongjiang and Gansu. For example, Inner Mongolia produced 60370.82 thousand tons of hay in 2005, and hence became the No. 1 grass producer of China. (4) Among the steppe types, the following 5 had the largest grass production: Alpine meadow, Temperate steppe, Low-land meadow, Temperate meadow steppe and Montane meadow with total production accounting for 62.2% in China. (5) Grass production of the entire China in August 2005 remained at the same level as that in August 2004. However, the situations of major grassland provinces were different: grass production in both Qinghai and Gansu in 2005 increased to 9.02% and 3.63%, respectively, when compared with that in 2004. The grass production in Inner Mongolia decreased by 3%, while the production in Tibet, Xinjiang and Sichuan remains unchanged when compared with that in 2004. These results were very important for grassland administration, pasture grazing and grassland ecosystem studies in China.     

中国北方草地植物群落季节生长格局模拟

中国北方草地横跨干旱、半干旱及亚湿润干旱气候区,水分是限制中国北方草地植物群落生产力的主要气候因子.采用基于水分平衡过程的、简单的植物群落模型,利用460个气象站40年气象数据的月平均值,模拟中国北方7种草地类型的季节及年生长、叶片投影盖度(FPC)、蒸发系数(k)及净第一性生产力(NPP).野外观测数据对模型的验证显示模拟结果与观测值相符较好.温性草地自东向西,青藏高原自东南向西北,植物群落的k、NPP与FPC呈递减趋势,显示了中国温性草地自东向西,青藏高原自东南向西北逐渐干旱的水分梯度;其中高寒草甸的3个模拟参数值均最高,高寒草原FPC次于高寒草甸,而NPP却与温性典型草原相近,温性典型荒漠的3个参数最低.高寒草甸、高寒草原、温性草甸草原、温性典型草原、温性荒漠草原、温性草原化荒漠和温性典型荒漠等7种类型草地的畜群承载力约为每公顷5.2、2.3、3.6、2.1、1.0、0.6和0.2只羊单位,区域最适恢复植被盖度分别以93%、79% 、56%、50%、44%、38%和37%为宜.     

Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution

Concomitant changes of annual precipitation and its seasonal distribution within the context of global climate change have dramatic impacts on aboveground net primary productivity (ANPP) of grassland ecosystems. In this study, combining remote sensing products with in situ measurements of ANPP, we quantified the effects of mean annual precipitation (MAP) and precipitation seasonal distribution (PSD) on the spatial variations in ANPP along a climate gradient in Eurasian temperate grassland. Our results indicated that ANPP increased exponentially with MAP for the entire temperate grassland, but linearly for a specific grassland type, i.e. the desert steppe, typical steppe, and meadow steppe from arid to humid regions. The slope of the linear relationship appeared to be steeper in the more humid meadow steppe than that in the drier typical and desert steppes. PSD also had significant effect on the spatial variations in ANPP. It explained 39.4% of the spatial ANPP for the entire grassland investigated, being comparable with the explanatory power of MAP (40.0%). On the other hand, the relative contribution of PSD and MAP is grassland type specific. MAP exhibited a much stronger explanatory power than PSD for the desert steppe and the meadow steppe at the dry and wet end, respectively. However, PSD was the dominant factor affecting the spatial variation in ANPP for the median typical steppe. Our results imply that altered pattern of PSD due to climate change may be as important as the total amount in terms of effects on ANPP in Eurasian temperate grassland.     

新疆天山中段巴音布鲁克高山草地碳含量及其垂直分布

对新疆天山中段巴音布鲁克高山草地(高山草原和高山草甸)的生物量和土壤有机碳进行了测定。结果表明积分和分层两种估算方法得到的土壤有机碳含量没有显著差异,但积分算法的优势在于能推算不同深度的土壤有机碳含量,便于与以往的研究进行比较;高山草甸的生物量和土壤有机碳含量均大于高山草原;其地上生物量分别为71.4和94.9 g C·m^-2,地下生物量分别为1 033.5和1 285.2 g C·m^-2; 1 m深度的土壤有机碳含量分别为25.7和38.8 kg·m^-2;地上生物量呈现较为明显的垂直分布格局,即随着海拔的增加,地上生物量先呈增加趋势,但当海拔超过一定界限后生物量突然下降;土壤含水率是导致南坡(阳坡)土壤有机碳含量空间分异的重要因素,但北坡(阴坡) 土壤有机碳含量还可能与地形、土壤质地等其它因素有关;两种高山草地(高山草原和高山草甸)的根系集中分布在40 cm以内,0~20 cm根系分别占其总量的76%和80%;土壤有机碳集中分布在60 cm以内,0~20 cm土壤有机碳分别占其总量的55%和49%;高山草原根系分布比高山草甸深,但较低的地下/地上比使得其有机碳分布比高山草甸浅。