帽儿山地区森林冠层叶面积指数的地面观测与遥感反演

叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effective LAI,LAIe).本文以东北林业大学帽儿山实验林场为研究区,利用LAI2000观测森林冠层LAIe,并结合TRAC观测的叶片聚集度系数估算了森林冠层LAI,并通过分析基于Landsat5-TM数据计算的不同植被指数与LAIe之间的关系,建立了该区森林LAI遥感估算模型.结果表明:研究区阔叶林的LAI和LAIe基本相当,而针叶林的LAI比LAIe大27%;减化比值植被指数(reduced simple ratio,RSR)与该区LAIe的相关性最好(R2=0.763,n=23),最适合该区LAI的遥感提取.当海拔<400 m时,LAI随海拔高度的上升而快速增大;当海拔在400～750 m时,LAI随海拔高度的上升缓慢增大;当海拔>750 m时,LAI呈下降趋势.研究区森林冠层LAI与森林地上生物量存在显著的正相关关系.     

基于森林群落和光谱曲线特征分异的福建省森林生物量遥感反演

基于福建省Landsat8 OLI影像,利用混合像元分解模型筛选出"纯净"的植被像元,提取296个调查样地对应植被像元的红光和近红外波段的中心波长(分别CWR和CWNIR)及其对应的反射率(分别R和NIR),构建以(NIR-R)/(CWNIR-CWR)为特征指数的叶生物量回归模型。然后根据针叶林、阔叶林及针阔混交林叶生物量与干、枝、叶所组成的地上生物量的关系方程,结合福建省植被覆盖分类数据,估测了整个福建省针叶林、阔叶林、混交林的地上生物量,并绘制了福建省地上生物量分布图。结果表明:红光和近红外两个波段反射率和其中心波长所组成的斜率与叶生物量相关性显著,与针叶林、阔叶林、混交林叶生物量的精度分别达到70.55%、68.89%、51.75%,采用这种方法对福建省叶生物量和地上总生物量进行估算,并进行精度验证,其中,针叶林、阔叶林、混交林叶物量的模型误差(RMSE)分别达到29.2467 t/hm~2(R~2=66.64%)、14.0258 t/hm~2(R~2=61.13%)、10.1788 t/hm~2(R~2=55.43%),地上总生物量的模型精度分别达到49.8315 t/hm~2(R~2=54.65%)、45.1820 t/hm~2(R~2=49.01%)、41.5131 t/hm~2(R~2=38.79%),这说明,采用红光波段和近红外波段与其中心波长所组成的斜率估测森林叶生物量,进而估算其地上总生物量的方法是可行的。     

基于MODIS数据的北京植被叶面积指数的时空变化

利用MODIS产品中分辨率为1 km的LAI数据,结合植被功能型分类,分析了2010—2012年北京植被LAI的月际变化与空间分布特征;并在GIS与Timesat软件的支持下,探讨了各种植被类型LAI的多年变化。结果表明:在2010—2012年的不同月份中,北京的植被LAI较高值均出现在西北山地、东北山地、怀柔北部等林地,并呈现从东北山地至西南向中心城区的递减分布;其次,基于LAI叠加分析的结果显示,各种植被类型LAI年平均值的波动均较小,在2010—2012年,针叶林、阔叶林、草地、作物LAI的年均值波动范围分别在1.45~1.50、1.24~1.27、0.90~0.92和0.48~0.51;此外,Savitzky-Golay平滑滤波结果表明,针叶林、阔叶林、草地、作物LAI的月际变化基本体现了植被的生长轨迹。     

四川岷山火溪河地区人为干扰后的植被组成及分布

以平武县地形图、植被图、野外植被调查结果为数据源制作研究区植被图和数字高程模型(Digital Elevation Model,DEM),在Arcview GIS平台下通过植被分类和图层叠加分析法,研究了人为干扰后植被的组成及植被类型在主要地形梯度上的分布特征。结果表明:(1)受人为干扰特别是森林采伐活动影响,研究区针阔混交林消失,针叶林面积缩小,阔叶林和灌丛面积增加。(2)植被类型在海拔梯度上分布规律性明显;除农田外,其它植被类型在坡度上的面积比例与研究区面积在各坡度段所占比例很相似;针叶林、阔叶林、农田、灌丛对坡向有明显选择性。(3)人为干扰活动改变了研究区植被类型及其面积比重,并使植被在海拔段上的分布带有明显人类干扰的痕迹。     

川西亚高山森林恢复的空间格局分析

以米亚罗林区为例,利用森林样地调查和遥感影像解译方法,通过森林植被图与数字地形的叠加,分析了川西亚高山森林大规模采伐和更新后,主要森林植被类型外貌与起源之间的联系,以及各类型分布的地形分异规律和空间格局.结果表明,大规模采伐和更新后,森林植被类型的外貌与起源相关,老龄针叶林为保留下来的原始林,中幼龄针叶林为人工林,落叶阔叶林为天然次生林,而针阔混交林中既有天然次生的成分,也有人工、天然更新共同作用的成分.海拔2 800~3 600 m是米亚罗的主要伐区,森林恢复表现出坡向分异:人工更新的中幼龄针叶林主要分布于阳坡、半阳坡;落叶阔叶林和针阔混交林受天然更新的影响,主要分布于阴坡、半阴坡.老龄针叶林主要保留在海拔3 600 m以上.恢复过程中各种森林植被类型镶嵌分布,景观破碎化严重.     

川西亚高山森林恢复的空间格局分析

以米亚罗林区为例,利用森林样地调查和遥感影像解译方法,通过森林植被图与数字地形的叠加,分析了川西亚高山森林大规模采伐和更新后,主要森林植被类型外貌与起源之间的联系,以及各类型分布的地形分异规律和空间格局.结果表明,大规模采伐和更新后,森林植被类型的外貌与起源相关,老龄针叶林为保留下来的原始林,中幼龄针叶林为人工林,落叶阔叶林为天然次生林,而针阔混交林中既有天然次生的成分,也有人工、天然更新共同作用的成分.海拔2 800～3 600 m是米亚罗的主要伐区,森林恢复表现出坡向分异:人工更新的中幼龄针叶林主要分布于阳坡、半阳坡;落叶阔叶林和针阔混交林受天然更新的影响,主要分布于阴坡、半阴坡.老龄针叶林主要保留在海拔3 600 m以上.恢复过程中各种森林植被类型镶嵌分布,景观破碎化严重.     

利用MODIS数据评估冷冻灾害对广东植被的损害

2008年初我国南方地区发生的极端冷冻灾害对植被造成了极大破坏。以广东为例,利用MODIS数据,对比分析了受损植被与正常植被的NDVI变化;利用ISODATA非监督分类方法提取受损植被分布信息,借助90m SRTM DEM数据研究受损植被与地形因子的关系,发现广东植被受损严重,受损植被总面积达19 011.06 km²。受损植被受海拔高程、坡度、坡向等地形因子影响较大;受损植被主要集中于海拔大于400 m,坡度小于15°的阴坡区域;通过MODIS土地覆盖图与受损植被的复合分析,研究了该地区受损植被的类型,发现受损植被以常绿阔叶林、混交林、落叶阔叶林、灌丛、落叶针叶林及常绿针叶林为主。     

黑龙江省大兴安岭森林生物量空间格局及其影响因素

基于样地实测数据和EVI指数,定量分析了黑龙江省大兴安岭森林生物量空间格局,并利用ArcGIS软件的空间分析与统计工具,分析了气候区、海拔、坡度、坡向和植被类型对森林生物量空间格局的影响.结果表明: 黑龙江省大兴安岭森林生物量为350 Tg,空间上呈聚集分布,生物量有巨大的增长空间.森林生物量密度大小顺序为:寒温带湿润区(64.02 t·hm^-2)＞中温带湿润区(60.26 t·hm^-2);各植被类型生物量密度大小顺序为:针阔混交林(65.13 t·hm^-2)＞云冷杉林(63.92 t·hm^-2)＞偃松 落叶松林(63.79 t·hm^-2)＞樟子松林(61.97 t·hm^-2)＞兴安落叶松林(61.40 t·hm^-2)＞落叶阔叶混交林(58.96 t·hm^-2).随海拔和坡度的增大,森林生物量密度先减小后增加,并且阴坡大于阳坡.大兴安岭森林生物量空间格局随气候区、植被类型和地形因子的梯度变化表现出差异性,在区域尺度上估算生物量密度时,需要充分考虑这种空间差异性.     

基于MODIS的东北地区自然植被生产力对干旱的响应

干旱对全球陆地生态系统影响广泛,中国东北地区干湿分异典型,干旱面积有逐步增大趋势,为了解东北地区自然植被生产力对干旱的响应,基于MODIS公开的数据产品、降水数据和scPDSI,采用统计学方法,在明确2002—2013年东北地区干旱分布特征的基础上,分析自然植被NPP、LAI和CUE的时空变化规律,探究自然植被对独立干旱事件和持续干旱的响应,结果表明:①从2002—2013年平均水平上看,森林的NPP和LAI年平均值明显高于草地,CUE年平均值略低于草地;②森林和草地均通过降低NPP和LAI来应对独立干旱事件,森林的NPP在干旱年过后第3年显著低于干旱前,LAI在干旱年过后第2年显著高于干旱前,而且这种变化效应至少持续到干旱年过后第4年;草地的NPP和LAI仅在独立干旱年当年有显著变化;③随着干旱的持续,森林NPP增加的比例有扩大趋势,LAI_(Baseline)较低的森林在持续干旱时ΔLAI_(Dryn)增加的可能性越大;草地对持续干旱也具有一定的适用能力,而且NPP_(Baseline)、LAI_(Baseline)和CUE_(Baseline)较低的草地,在持续干旱时ΔNPP_(Dryn)、ΔLAI_(Dryn)和ΔCUE_(Dryn)增加的可能性越大;④无论是独立干旱事件还是持续干旱,森林或草地的CUE变化很少达到显著性水平,变化规律的显著性也低于NPP和LAI。该项研究将为提高干旱对自然生态系统影响的评估能力做出贡献。     

山西阳泉地区乔木林地上碳密度遥感估测

为了建立基于遥感影像和环境因子的森林碳密度估测的有效方法,本文基于2009年森林清查数据和SPOT遥感影像,以山西省阳泉地区为例,采用生物量换算因子连续函数法对研究区乔木林地上生物量和碳密度进行估算;在此基础上,选取遥感影像、环境因子(海拔、坡度、坡向等)为自变量,利用增强型BP神经网络建立研究区乔木林碳密度估算模型并输出空间分布图。结果表明:阳泉地区乔木林生物量为552774 t,碳密度为11.38t·hm-2;从不同林型、林龄和起源的生物量及碳密度来看,针叶林、幼龄林、人工林的生物量最大,阔叶林、成熟林、天然林的碳密度最大;采用增强型BP神经网络可以很好地模拟乔木林碳密度,针叶林、阔叶林、针阔混交林仿真结果的平均相对误差和平均相对误差的绝对值分别2.40%、6.87%、-4.09%和6.83%、2.77%、3.99%;基于BP神经网络模型输出乔木林碳密度空间分布图,模拟精度达到85.05%,进一步验证了人工神经网络能为森林碳密度提供快速准确的估测,为今后的森林资源调查和管理提供了科学依据。     

内蒙古不同类型草地叶面积指数遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,反演LAI是植被遥感的重要研究内容之一。根据在内蒙古呼伦贝尔和锡林浩特草原利用LAI 2000观测的草地LAI,比较了不同植被指数(SR、RSR、EVI、NDVI、SAVI和ARVI)估算不同类型草地LAI的能力,建立了基于Landsat-5 TM遥感数据的LAI估算模型,并利用LAI观测数据对模型进行了检验,生成了研究区内草地LAI分布图,据此对MODIS LAI产品一致性进行了评价。结果表明,在呼伦贝尔和锡林浩特两个研究区,RSR与LAI的相关性最高(R²分别为0.628、0.728,RMSE分别为0.512、0.490),在低密度草地,RSR的优势更为明显;验证表明,根据RSR建立的LAI估算模型的精度可达70%;利用TM数据生成的两个地区的LAI(TM LAI)空间变化明显,锡林浩特草地的LAI值整体上低于呼伦贝尔草地;在呼伦贝尔和锡林浩特,MODIS LAI产品与TM LAI一致性分别为0.566,0.323,MODIS LAI产品高估了呼伦贝尔草地LAI值,而在锡林浩特研究区则存在低估现象。     

3类典型温带山地森林的叶面积指数的季节动态: 多种监测方法比较

 叶面积指数(leaf area index, LAI)是定量描述冠层结构的最有效指标之一。鉴于森林冠层三维结构的高度复杂性和异质性, 迄今仍没有形成统一标准的LAI测量方法。该文利用LAI-2000冠层分析仪、CI-110冠层分析仪和半球摄影法(digital hemispherical photograph, DHP), 对北京东灵山地区以蒙古栎(Quercus mongolica)为主的落叶阔叶林、华北落叶松(Larix gmelinii var. principis-rupprechtii)林和油松(Pinus tabuliformis)林的有效叶面积指数(effective leaf area index, LAI_e)进行了动态监测, 探寻其季节变化规律。为准确地估算温带山地主要森林类型的LAI, 对光学仪器测量值进行了去除木质成分、聚集效应等校正, 与基于凋落物收集法的相应实测值进行了比较分析。结果表明: 3种典型森林在生长季期间叶片生长均呈现单峰型; 3种光学仪器测量方法的同期LAI_e数值大小顺序为: LAI-2000冠层分析仪>DHP>CI-110冠层分析仪。光学仪器的直接测量值LAI_e包含了木质成分的贡献, 钝化了季节动态的变化幅度, 这对有明显季节交替的落叶林尤为突出。经校正, LAI-2000冠层分析仪和DHP的测量值与实测值都表现出显著的相关性, 其中LAI-2000冠层分析仪最适于采用基于空隙大小的校正方法, 而基于空隙度和空隙大小的综合算法则是校正DHP的最佳选择。结合经济成本和野外实际操作等因素考虑, DHP具有更大的推广优势, 特别适用于温带山地落叶林。     

台湾植被分类方案

复杂的自然条件导致面积仅为36 000多平方公里的台湾兼有热带和寒温带的群落类型,堪称中国乃至世界植被之缩影.它的植被分类也是对中国乃至世界植被分类的补充.台湾植被分类采用的是外貌-区系原则,分类单位和系统自上而下是:植被型纲、植被型目、植被型、群团组、群团和群丛.高级单位(植被型纲、植被型目和植被型)以群落的生态外貌为依据;中、低级单位主要依据种类组成,兼顾优势种和标志种的指示意义.在中级单位中(群团组、群团),重视优势种的作用,在低级单位中(群丛)更重视特征种(或标志种)的意义.按此原则将台湾植被划分为5个植被型纲(森林、灌丛、草本植被、岩原植被、沼泽和水生植被),29个植被型目(寒温性针叶林,凉温性针叶林,暖温性针叶林,暖性针叶林、落叶阔叶林、常绿阔叶落叶阔叶混交林、常绿苔藓林、常绿硬叶林、常绿阔叶林、热带雨林、热带季雨林海岸林、暖性竹林、常绿针叶灌丛、革叶灌丛、落叶阔叶灌丛、常绿阔叶灌丛、刺肉灌丛、竹灌丛、草甸、疏灌草地、干热早生草地、岩屑堆稀疏群落、石隙植被、木本沼泽、草本沼泽、藓类沼泽、淡水水生植被和咸水水生植被),53个植被型.列举了各个植被型中的重要群团.     

台湾植被分类方案

复杂的自然条件导致面积仅为36000多平办公里的台湾兼有热带和寒温带的群落类型,堪称中国乃至世界植被之缩影。它的植被分类也是对中国乃至世界植被分类的补充。台湾植被分类采用的是外貌-区系原则,分类单位和系统自上而下是：植被型纲、植被型目、植被型、群团组、群团和群丛。高级单位(植被型纲、植被型目和植被型)以群落的生态外貌为依据;中、低级单位主要依据种类组成,兼顾优势种和标志种的指示意义。在中级单位中(群团组、群团),重视优势种的作用,在低级单位中(群丛)更重视特征种(或标志种)的意义。按此原则将台湾植被划分为5个植被型纲(森林、灌丛、草本植被、岩原植被、沼泽和水生植被),29个植被型目(寒温性针叶林,凉温性针叶林,暖温性针叶林,暖性针叶林、落叶阔叶林、常绿阔叶落叶阔叶混交林、常绿苔藓林、常绿硬叶林、常绿阔叶林、热带雨林、热带季雨林海岸林、暖性竹林、常绿针叶灌丛、革叶灌从、落叶阔叶灌丛、常绿阔叶灌丛、刺肉灌从、竹灌丛、草甸、疏灌草地、干热旱生草地、岩屑堆稀疏群落、石隙植被、木本沼泽、草本沼洋、藓类沼泽、淡水水生植被和成水水生植被),53个植被型。列举了各个植被型中的重要群团。     

Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

In this study we examined ecosystem respiration (R_ECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of R_ECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of R_ECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of R_ECO. The maximum seasonal leaf area index (LAI_MAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature T_ref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r²=0.52, P<0.001, n=104) even within each PFT. Besides LAI_MAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (N_depo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAI_MAX) which performed well in predicting the spatio‐temporal variability of R_ECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands.     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

三种回归分析方法在Hyperion影像LAI反演中的比较

借助GPS进行地面精确定位,利用LAI-2000冠层分析仅在攸县黄丰桥林场开展130个样地(60m×60m)的叶面积指数(Leaf Area Index,LAI)测量.采用FLAASH模块对Hyperion数据进行大气校正并与地面同步冠层观测数据进行拟合,通过研究地面实测LAI与Hyperion影像波段及其衍生的系列植被指数(NDVI、RVI等)的相关性,筛选出估算叶面积指数的植被指数因子.应用曲线估计、逐步回归及偏最小二乘三种回归分析技术分别建立叶面积指数的最优估算模型.结果表明:参与建模的因子中,比值植被指数(RVI)与LAI的相关性最大,敏感性最高,其次是SARVI0.1,NDVI705,NDVI,SARVI0.1,SARVI0.25;曲线估计、逐步回归分析和偏最小二乘回归三种分析方法所建的6个回归模型中,偏最小二乘回归的拟合效果最好,预测值与实测值的决定系数R2为0.84、曲线估计的拟合效果最低,预测值与实测值的决定系数R2为0.64;建模精度分析表明,选用5-6个自变量因子进行LAI建模是可靠的,以6个植被因子建立的偏最小二乘回归模型预测精度最高.     

Detecting soil salinity with MODIS time series VI data

Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky–Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky–Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59–0.92. EVI-SI was linearly correlated with EC_e in cropland with a high model fit (R² = 0.85). The relationship of EVI-SI and EC_e fit best with a binomial line and EVI-SI was able to explain 70% of the variance of EC_e. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R² = 0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization.     

基于叶面积指数估算植被总初级生产力

长时间序列的陆地碳通量数据在全球生态环境变化研究中具有重要意义。采用MODIS GPP(Gross Primary Productivity)算法,基于GIMMS LAI3g,MODIS15和Improved-MODIS15三种叶面积指数(LAI),估算了全球2000至2010年的植被总初级生产力(GPP)。该估算的GPP数值经过全球20个通量站点的验证,并结合MODIS17分析了它们在时空变化上的异同。结果表明:(1)4种GPP精度如下:GPP_(MOD17)GPP_(impro_MOD15)GPP_(LAI3g)GPP_(MOD15)。(2)4种GPP整体上具有一致的季节波动,冬季和夏季整体好于春季和秋季。GPP_(LAI3g)的4个季节精度较相近,而GPP_(MOD17)除了春秋季外其它季节都较好。(3)GPP_(LAI3g)在中等GPP值分布区的估值相对较高,其全球总GPP大体为(117±1.5)Pg C/a,GPP_(MOD17)和GPP_(impro_MOD15)相近且都低于该值。(4)GPP_(LAI3g)和GPP_(impro_MOD15)在大约63.29%的陆面上呈显著(P0.05)的正相关关系,它们和GPP_(MOD17)在LAI不确定性小的地区呈显著的正相关关系。GPP_(LAI3g)和GPP_(MOD15)正相关分布面积占比为40.61%。     

A model for seasonality and distribution of leaf area index of forests and its application to China

Abstract. We have constructed a phenological model of leaf area index (LAI) of forests based on biological principles of leaf growth. Field data of maximum LAI from 794 plots with mature or nearly mature stand ages over China were used to parameterize and calibrate the model. New measurements of maximum LAI from 16 natural forest sites were used to validate the simulated maximum LAI. The predictions of seasonal LAI patterns were compared with seasonal changes derived from the 1‐km satellite AVHRR‐NDVI data for nine undisturbed forest sites in eastern China. Then, we used the model to map maximum LAI values for forests in China. Model results indicated that the PhenLAI model generally predicted maximum LAI well for most forest types, even when maximum LAI is > 6. This suggests an ecological approach to the saturation problem in satellite detection of high forest LAI where the relationship between NDVI and LAI reaches an asymptote near a projected LAI value of 5 or 6. Furthermore, the predictions of seasonal LAI patterns in timing and dynamics were generally consistent with the satellite NDVI changes, except for monsoon forest and rain forest in south China where satellite detection of seasonal variation in leaf area is hardly possible. Compared with average projected LAI measurements of global forests from 809 field plots in literature data, our maximum LAI values were close to the global literature data for most of Chinese forests, but the average area‐weighted maximum LAI for all forests of China (6.68 ± 3.85) was higher than the global mean LAI of the 809 field plots (5.55 ± 4.14). We believe that forest LAI in China is commonly > 6, especially in tropical rainforest, subtropical evergreen broad‐leaved forest, temperate mixed forest, and boreal/alpine spruce‐fir forest where satellite detection of high LAI is hardly possible.