利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI)——以岷江上游为例

短周期的低分辨率遥感数据为大面积估算LAI及季节动态和物候趋势提供了有利工具,但基于高分辨率LAI的遥感估算模型在低分辨率遥感数据上应用有很大的不确定性。研究利用LAI-2000冠层分析仪与跟踪辐射和冠层结构测量仪(TRAC),测定了岷江上游流域范围内490块野外调查样地(50m×50m样方)的LAI数据,结合同期较高精度卫星数据(TM)建立了不同植被类型的LAI-NDVI算法,在经过传感器的相对校正后,将这种算法应用到同期分辨率较低的MODIS数据和SPOT VEGETATION数据上。结果表明,30m分辨率的TM LAI的均值为4.53,250m MODIS LAI的均值为3.55,1000m VGT LAI的均值为4.20,随着栅格分辨率的降低,总体标准差有增加的趋势,并且LAI值也有不同程度的低估,其中MODIS LAI值被低估约22%。但利用TM LAI数据验证MODIS和VGT LAI数据后发现,250m的MODIS数据预测误差在30%左右,1000m的SPOT数据预测误差则高达50%,空间重采样分析表明,栅格分辨率的降低是导致预测误差扩大的主要原因,而这也是岷江流域植被分布破碎化的体现。     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

利用不同分辨率卫星影像的NDVI数据估算叶面积指数(LAI) ——以岷江上游为例

短周期的低分辨率遥感数据为大面积估算LAI及季节动态和物候趋势提供了有利工具,但基于高分辨率LAI的遥感估算模型在低分辨率遥感数据上应用有很大的不确定性。研究利用LAI-2000冠层分析仪与跟踪辐射和冠层结构测量仪(TRAC),测定了岷江上游流域范围内490块野外调查样地（50m×50m样方）的LAI数据,结合同期较高精度卫星数据（TM）建立了不同植被类型的LAI-NDVI算法,在经过传感器的相对校正后,将这种算法应用到同期分辨率较低的MODIS数据和SPOT VEGETATION数据上。结果表明,30m 分辨率的TM LAI的均值为4.53,250m MODIS LAI的均值为3.55,1000m VGT LAI的均值为4.20,随着栅格分辨率的降低,总体标准差有增加的趋势,并且LAI值也有不同程度的低估,其中MODIS LAI值被低估约22%。但利用TM LAI数据验证MODIS 和VGT LAI数据后发现,250m的MODIS数据预测误差在30%左右,1000m的SPOT数据预测误差则高达50%,空间重采样分析表明,栅格分辨率的降低是导致预测误差扩大的主要原因,而这也是岷江流域植被分布破碎化的体现。     

落叶收集法测定叶面积指数的快速取样方法

叶面积指数(LAI)是植被冠层结构的一个重要参数,它不仅是许多生态和气候模型的重要输入变量,而且是生态系统动态变化监测的一个重要指标。LAI可通过各种间接和直接的手段来观测,而间接观测的LAI值常常需要直接的观测数据来校验。落叶收集法是一种广泛使用的直接观测LAI的方法,过去的研究还未发现有涉及落叶收集的取样技术及其观测精度的内容。对长白山和北京地区落叶阔叶林的落叶进行了3a的观测,每年一次性收集落叶样品分析,研究结果表明:①不同层次落叶的含水量差异巨大,且落叶含水量的日变化明显。上下层落叶的含水量绝对值差异高达10%以上,日变化绝对值差异高达20%以上。因此,在野外收集落叶样本时,为减小落叶含水量变化所导致的LAI观测误差,应从上到下直到地面进行取样,且尽可能多地收集落叶样本。②在落叶阔叶人工林和天然林里,无论样地的大小(1hm2或30m×30m样地),无论取样单元的大小(1m2或25m2分辨率),林内的LAI分布很不均匀,LAI介于0-15.5(1m2分辨率的1hm2样地)或者2.6-9.1(25m2分辨率的30m×30m样地)。③要准确测定落叶林的LAI,收集落叶的样地面积越大越好,且尽量选择地势平坦的样地。对于1hm2或者30m×30m大小的样地,可随机布设一个10m×10m的小样地来观测,精度分别可达85%、80%。④10m×10m小样地的LAI观测,可将其分为4个相邻的5m×5m小样进行取样。对每个5m×5m小样,快速的取样方法是:Ⅰ.随机布设6个1m2取样,这样取样可以保证在99%概率水平上,100m2、30m×30m和1hm2样地的LAI观测精度分别为90%、75%、70%左右。Ⅱ.随机布设11个1m2取样。可以保证在99%概率水平上,100m2、30m×30m和1hm2样地的LAI观测精度分别为94%、80%、75%左右。     

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。     

基于数码相片Gamma校正的水稻叶面积指数估算

随着数码相机的日益普及,利用数码相机进行作物叶面积指数(LAI)测量不断得以应用。由于数码相机成像时会对入射光辐射强度进行Gamma编码变换,输出的相片DN(Digital Number)值与入射光辐射强度呈非线性关系,会造成在确定相片中植被叶片与背景的分割阈值时出现误差,并最终导致LAI估算存在较大不确定性。以水稻为研究对象,获取不同生长期水稻冠层相片并结合同步LAI 2000测量的LAI数据,基于相片Gamma校正原理,对水稻不同生长期冠层相片进行Gamma校正,在此基础上利用冠层孔隙率方法,估算不同生长期水稻LAI。结果表明,经过Gamma校正相片估算的水稻LAI总体精度有显著提高,相片估算的IMAGE LAI与LAI-2000测量值比较的决定系数达到0.71(P0.05)。在整个观测期内,两种方法观测的LAI值在时间变化趋势上表现一致,但在不同生长期内存在差别,在水稻分蘖期和拔节期相片估算的IMAGE LAI要高于LAI-2000测量值,孕穗期到抽穗期期间IMAGE LAI低于LAI-2000测量值,乳熟期到成熟期IMAGE LAI又高于LAI-2000的观测结果。     

林下植被对遥感估算马尾松LAI的影响

叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。     

基于影像纹理特征的川西南山地常绿阔叶林有效叶面积指数的空间分析

遥感是获取叶面积指数（LAI）信息的最有吸引力的选择之一,但目前基于遥感数据的叶面积指数估测精度有限.本文以川西南山地常绿阔叶林为研究对象,基于地面调查的83个20 m×20 m样地和SPOT5数据,运用灰度共生矩阵法提取影像单波段、简单波段比图和主成分图的纹理信息,以不同图像处理方式的纹理参数作为辅助变量进行地统计分析估算有效LAI（LAIe）.结果表明： LAIe与不同方式处理图像的纹理参数存在不同程度的相关性,其中,与B₁波段、B₁/B₄和PC₁的均质性呈极显著相关关系.与以归一化植被指数（NDVI）为辅助变量相比,以纹理参数B₁波段、B₁/B₄和PC₁的均质性作为辅助变量估测LAIe的精度均有所提高,分别提高5.3%、11.0%、14.5%,还能在一定程度上降低统计误差.以NDVI、PC₁均质性作为辅助变量的LAIe空间地统计估测模型最优（R²＝0.840,RMSE=0.212）.本研究结果为合理地选择除植被指数外的其他辅助变量估测区域LAI的空间分布提供了一种新的思路和方法.     

植被叶片及冠层层次含水量估算模型的建立

利用LOPEX'93数据库中7个鲜叶片含水量(Cw)和光谱反射率实测数据,基于光谱指数法,在叶片层次,用47个随机样本建立Cw与不同光谱指数的统计模型,并用另外20个样本验证.结果表明,Cw的两种表征形式相对含水量FMC和等价水深EWT在提取叶片Cw时差异较大,EWT与各光谱指数的相关性较FMC高,但FMC对叶片Cw的反演精度高于EWT.而反演精度更高的是基于最优子集回归建立的光谱指数线性模型.Ratio₉₇₅是叶片层次提取Cw的普适光谱指数.冠层层次,利用PROSPECT+SAILH耦合模型,模拟在不同叶面积指数LAI和Cw下的冠层光谱.为了剔除背景影响,更好地提取冠层Cw,提出用近红外和短波红外波段反射率构造土壤可调节水分指数(SAWI),该指数与其他光谱指数的比值能明显地剔除土壤背景影响,更准确地提取冠层Cw.Ratio₉₇₅的改进型光谱指数(Ratio₉₇₅-0.9)/(SAWI+0.2)能用来提取叶面积指数LAI从0.3到8.0,Cw从0.0001cm到0.07cm的冠层Cw,研究表明精度较高.     

多尺度遥感综合监测我国北方典型草原区植被盖度

利用多尺度遥感影像综合进行全球和区域尺度的土地利用/覆盖变化(LUCC)研究是最近全球变化研究的重要方向之一。本文综合利用野外群落样方、数字相机、ETM+影像、NOAA/AVHRR影像,在遥感、GIS和GPS支持下,对我国北方典型草原区植被盖度进行了综合监测、模拟与分析。结果表明:(1) 利用经处理后的数字相机影像测量盖度的结果准确性较高,可以作为植被盖度测量的标准结果,反映真实的覆盖特征,并用以验证利用其它方法测量结果的精度。(2) 利用野外1 m2样方网格法目视估测的植被盖度结果变化较大,不稳定。本次实验中,与数字相机测量结果相比,样方估测的盖度普遍偏高,平均偏差为9.92%;但两者相关性较好(r2=0.89)。(3) 采用Gutman模型ETM+影像、NOAA/AVHRR影像反演植被盖度的结果与数字相机测量结果偏差分别为7.03%、7.83%,ETM+像元分解NOAA像元后得到的植被盖度与数字相机测量结果偏差5.68%。三者与数字相机测量结果的相关系数r2分别为0.78、0.61和0.76。(4)利用野外实测植被盖度数据直接与NOAA-NDVI影像建立统计模型估算植被盖度的精度较低(r2=0.65),而通过空间分辨率介于两者之间的ETM+影像进行转换后,该精度得到一定的提高(r2=0.80)。利用像元分解的方法提高了大尺度植被盖度监测的精度,是利用遥感数据进行尺     

Canopy structure in savannas along a moisture gradient on Kalahari sands

Measurements of tree canopy architecture were made at six savanna sites on deep, sandy soils, along a gradient of increasing aridity. There was substantial variation in the leaf area estimated within each site, using the same sample frame, but different measurement techniques. The trends in canopy properties in relation to the aridity gradient were consistent, regardless of the technique used for estimating the properties. The effective plant area index for the tree canopy (the sum of the stem area index and the leaf area index (LAI)) declined from around 2 to around 0.8 m² m^?2 over a gradient of mean annual rainfall from 1000 to 350 mm. Stems contributed 2–5% of the tree canopy plant area index. Since the tree canopy cover decreased from 50% to 20% over this aridity range, the leaf area index within the area covered by tree canopies remained fairly constant at 3–4 m² m^?2. Tree leaves tended from a horizontal orientation to a more random orientation as the aridity increased. On the same gradient, the leaf minor axis dimension decreased from around 30 mm to around 3 mm, and the mean specific leaf area decreased from 14 to 5 m² kgha^?1. There was good agreement between LAI observed in the field using a line ceptometer and the LAI inferred by the MODIS sensor on the Terra satellite platform, 2 months later in the same season.     

高光谱植被指数与水稻叶面积指数的定量关系

基于不同水稻品种、施氮水平和不同生育期下的大田试验,确立了水稻叶面积指数（LAI）与冠层光谱特征参数的定量关系.结果表明：水稻叶面积指数与部分高光谱植被指数存在良好的相关性,其中原始光谱组成的2波段差值指数（DI）形式相关性最好,其次为比值（RI）和归一化（NI）植被指数.相关最好的原始光谱植被指数是由近红外波段组成的差值指数DI(854,760),相关最好的一阶导数光谱植被指数是红光和近红外光组成的导数差值指数DI(D₆₇₆, D₇₇₈),但总体上导数光谱指数不如原始光谱指数与LAI关系密切.独立试验数据检验结果表明,以差值指数DI(854,760)为变量建立的水稻LAI监测模型具有较好的表现,可用于水稻LAI的估测.     

基于PROSAIL辐射传输模型的毛竹林叶面积指数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI) 冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明： PROSAIL模型各输入参数的敏感性由高到低依次为LAI>叶绿素含量(C_ab)>叶片结构参数(N)>平均叶倾角(ALA)>等效水厚度(C_w)>干物质含量(C_m),并以LAI、C_ab两个主要敏感因子用于构建毛竹林LAI 冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0%,但也存在反演LAI平均值高于实际值的问题.     

南方人工林叶面积指数的摄影测量

利用CI-110冠层分析仪、鱼眼镜头数码照片分析,结合胸径-叶片半表面积模型对千烟洲不同森林类型(20年生)的叶面积指数进行了测定,并对3种方法进行了比较.共调查74块样地.研究对象主要为湿地松(Pinus elliottii)林、马尾松(Pinus massoniana)林、杉木(Cunninghamia lanceolata)林.针对上述3种林型,CI-110观测值分别为1.355(0.38～2.85)、1.265(0.62～2.55)和2.140(0.76～3.31);鱼眼镜头测定值依次为1.339(0.71～1.79)、1.491(1.11～1.92)和1.984(1.26～2.59).利用胸径-叶片半表面积模型测定的叶面积指数(范围)依次为4.53(2.24～6.34)、3.88(1.19～6.28)和6.63(3.5～8.37).间接仪器法测定的值明显小于实测值,但具有一定的相关性.胸径-叶片半表面积模型测定叶面积指数最为可靠,而鱼眼镜头测定法比冠层分析仪测定精度高.     

光谱植被指数与水稻叶面积指数相关性的研究

 综合分析比较了几种常见光谱植被指数与水稻（Oryza sativa）叶面积指数的相关性及其预测力。结果表明,植被指数的预测力在水稻营养生长旺盛期间最好。植被指数的预测力主要依赖于叶面积指数(LAI)的整体变化范围。因此,综合不同生育时期和氮肥处理的试验资料,光谱植被指数能准确地预测LAI的变化。LAI与各植被指数均呈曲线相关,与比值植被指数(RVI)、再归一化植被指数(RDVI)和R810/R560显著幂相关,与归一化植被指数(NDVI)、垂直植被指数(PVI)、差值植被指数(DVI)、土壤调整植被指数(SAVI)和转换型土壤调整指数(TSAVI)显著指数相关。其中,近红外与绿光波段的比值R810/R560的预测力最佳。用不同移栽秧龄、不同密度、不同水分和氮肥处理的数据对R810/R560的表现进行了检验,结果表明估算精度平均为91.22%,估计的均方差根(RMSE)平均为0.480　5,平均相对误差为-0.013。表明宽波段光谱植被指数可以准确地用来监测水稻叶面积指数。     

Key canopy traits drive forest productivity

Quantifying the mechanistic links between carbon fluxes and forest canopy attributes will advance understanding of leaf-to-ecosystem scaling and its potential application to assessing terrestrial ecosystem metabolism. Important advances have been made, but prior studies that related carbon fluxes to multiple canopy traits are scarce. Herein, presenting data for 128 cold temperate and boreal forests across a regional gradient of 600 km and 5.4°C (from 2.4°C to 7.8°C) in mean annual temperature, I show that stand-scale productivity is a function of the capacity to harvest light (represented by leaf area index, LAI), and to biochemically fix carbon (represented by canopy nitrogen concentration, %N). In combination, LAI and canopy %N explain greater than 75 per cent of variation in above-ground net primary productivity among forests, expressed per year or per day of growing season. After accounting for growing season length and climate effects, less than 10 per cent of the variance remained unexplained. These results mirror similar relations of leaf-scale and canopy-scale (eddy covariance) maximum photosynthetic rates to LAI and %N. Collectively, these findings indicate that canopy structure and chemistry translate from instantaneous physiology to annual carbon fluxes. Given the increasing capacity to remotely sense canopy LAI, %N and phenology, these results support the idea that physiologically based scaling relations can be useful tools for global modelling.     

Modelling Plant Responses to Elevated CO2: How Important is Leaf Area Index?

BACKGROUND AND AIMS: The problem of increasing CO(2) concentration [CO(2)] and associated climate change has generated much interest in modelling effects of [CO(2)] on plants. While variation in growth and productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated [CO(2)] on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including [CO(2)] concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated [CO(2)] and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising [CO(2)] and discusses related implications for process-based modelling. MODEL: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of [CO(2)]. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of [CO(2)] elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply. CONCLUSIONS: Progress in estimating plant growth and productivity under rising [CO(2)] is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.     

Leaf litterfall,leaf area index,and radiation transmittance in cypress wetlands and slash pine plantations in north-central Florida

The seasonal dynamics of leaf litterfall and leaf area index (LAI, all-sided basis), light penetration and the vertical distribution of surface area index, and the feasibility of estimating LAI from radiation transmittance were studied from April 1993 to March 1994 in the canopies of three cypress (Taxodium ascendens) wetlands and their surrounding slash pine (Pinus elliottii) uplands in Florida flatwoods. Annual leaf litterfall ranged from 324 to 359 g m^–2 in the wetlands, which was very close to the average for 11 sites throughout Florida of 340±26 g m^–2. The seasonal pattern of the normalized LAI obtained for the dominant tree species in the ecosystems could be used to construct the seasonal dynamics of LAI at the ecosystem scale. The vertical distribution of surface area index in the wetlands was significantly different from that in the surrounding pine uplands. The maximum LAI of cypress wetlands in this area was about 8 m² m^–2, which was higher than the maximum of slash pine plantations of 6 m² m^–2. Cypress leaves were strongly erectophile in space. Results showed that the LAI-2000 canopy analyzer could generally be used to estimate forest LAI, whether the forest canopy was closed or not, if an overall clumping index of 2.00 was applied. However, as LAI decreased, the relative error contained in the radiation-based LAI estimates increased. This indicated that foliage clumping at the stand scale was more important than that at the tree or branch scale.     

Light distribution and foliage structure in an oak canopy

 Leaf angle distribution and shoot bifurcation ratio were measured and related to photon flux density (PFD) distribution in an oak canopy. Leaf angle distribution deviated substantially from random and changed markedly throughout the canopy. The observed leaf angle distribution was described by an ellipsoidal function with the single parameter of the distribution, x, changing from 1.6 at the top of the canopy to 3.2 in the lowest branches. In vertically homogeneous canopies, the extinction coefficient for diffuse radiation is expected to decline with increasing leaf area index (LAI). However, in the canopy studied here, the leaf angle distribution changed with height such that the effective extinction coefficient remained constant. Both shoot bifurcation ratio and leaf number per shoot declined with decreasing PFD inside the canopy. Based on these observed relationships, a simple canopy growth model that assumes horizontal homogeneity of the canopy was constructed. Calculations showed that a steady state, when growth in the upper of the canopy is in equilibrium with decline of lower canopy, the total canopy LAI should equal to 4.3. This predicted value of equilibrium LAI is larger than that estimated from measurements of PFD transmission (LAI=3.3), but smaller than that directly determined by litter collection (LAI=6.2 in 1998). Possible reasons for these discrepancies are discussed. Received: 22 June 1998 / Accepted: 7 April 1999     

基于多角度植被指数的马尾松林LAI反演方法

CHRIS/PROBA是目前具有最高空间分辨率（17 m×17 m）的星载多角度高光谱数据,该款数据在反演植被垂直结构参数,如树高、叶面积指数（leaf area index,LAI）等方面具有重要的应用前景。基于四尺度几何光学模型得到马尾松（Pinus massoniana Lamb.）冠层的归一化差分植被指数（normalized difference vegetation index,NDVI）各向异性分布规律,利用CHRIS红光特征波段和近红外特征波段构建一种新型多角度植被指数（normalized hotspot-dark-spot difference vegetation index,NHDVI）,并将其应用于CHRIS数据对马尾松林的LAI遥感估算上。结果显示：（1）相比归一化差分植被指数（NDVI）与土壤调节植被指数（soil adjusted vegetation index,SAVI）而言,NHDVI能很好地融合光谱信息与角度信息,与地面实测LAI的决定系数达到0.7278;（2）利用NHDVI-LAI统计回归模型方法来反演LAI值,将得到的LAI值与地面实测值进行相关性分析,结果拟合优度达到0.8272,均方根误差RMSE为0.1232。与传统植被指数相比,包含角度信息的多角度植被指数对LAI的反演在精度上有较大提升,同时比基于辐射传输模型的反演方法更简易、实用。