Defining leaf area index for non-flat leaves

To eliminate the confusion in the definition of leaf area index (L) for non-flat leaves, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions. It is shown that the projection coefficient calculated based on half the total intercepting area is close to a constant of 0.5 when the inclination angle of the objects is randomly (spherically) distributed, whereas the calculated results based on the object's largest projected area are strongly dependent on the shape of the objects. Therefore, it is suggested that the leaf area index of non-flat leaves be defined as half the total intercepting area per unit ground surface area and that the definition of L based on the projected leaf area be abandoned.     

Estimation of plant and leaf area index using three techniques in a mature native eucalypt canopy

Abstract Leaf area index (L) is a critical variable in monitoring and modelling forest condition and growth and is therefore important for foresters and environmental scientists to measure routinely and accurately. We compared three different methods for estimating L: a plant canopy analyser (PCA), a point‐quadrat camera method and digital hemispherical photography at a native eucalypt forest canopy at Tumbarumba in southern New South Wales, Australia. All of these methods produced indirect estimates of L based on the close coupling between radiation penetration and canopy structure. The individual L estimates were compared, and the potential advantages and disadvantages of each method were discussed in relation to use in forest inventory and in field data collection programmes for remote sensing calibration and verification. The comparison indicated that all three methods, PCA, digital hemispherical photography and the modified point‐quadrat camera method, produced similar estimates with a standard error between techniques of less than 0.2 L units. All methods, however, provided biased estimates of L and calibration is required to derive true stand L. A key benefit, however, of all of these estimation methods is that observations can be collected in a short period of time (1–2 h of field‐work per plot).     

大麦叶面积指数模拟模型

准确模拟叶面积指数是作物生长模拟模型预测作物生长和产量的关键.本文通过系统分析扬州和武汉地区不同大麦品种高产群体叶面积指数变化动态,建立了大麦群体的叶面积指数模拟模型.大麦叶面积指数是品种叶面积指数扩展的遗传参数和气温日较差、日照时数、辐射量等气候因子及水肥丰缺因子的函数.孕穗抽穗期最大叶面积指数与该期最适叶面积指数是不同的概念,二者之间存在着极显著差异.利用扬州、南京和昆明地区不同品种的播期试验及氮肥试验资料对模型进行了检验,结果表明,模型对大麦叶面积指数的模拟效果较好,模拟值与观测值吻合度高,根均方差RMSE介于0.742～2.865,平均值为1.348.对模拟值与观测值进行y=x的线性回归分析,相关系数R²介于0.511～0.954,均呈极显著正相关.     

Improving the accuracy of indirect methods in estimating leaf area index using three correction schemes in a Larix gmelinii plantation

Aims Woody materials and clumping effects are key error sources in estimating leaf area index (LAI) by optical methods. However, how to correct the error caused by woody materials has not reached consensus. The aims of this study are (1) to evaluate the accuracy of optical methods for estimating effective LAI (L_e) in a deciduous needle leaf forest stand, and (2) to develop a practical correction scheme to improve the accuracy of optical methods in estimating LAI.Methods L_ewas estimated by two indirect methods (i.e., digital hemispherical photography (DHP) and LAI-2000 plant canopy analyzer method (LAI-2000 method) in an annual maximum leaf area period in a Larix gmelinii plantation. Then, we developed three correction schemes to improve the accuracy of indirect methods in estimating LAI. Meanwhile, two direct methods (i.e., litter collection and allometry methods) were used to estimate LAI. Taking LAI from litter collection as a reference, we evaluated the effectiveness of three correction schemes and tested the influence of zenith angle ranges on the correction results.Important findings With zenith angle ranges of 0-45° (rings 1-3), 0-60° (rings 1-4), 45°-60° (ring 4) and 0-75° (rings 1-5), L_eobtained from DHP underestimated LAI from both litter collection and allometry by 19%-32% and 18%-29%, respectively. L_efrom LAI-2000 method with four zenith angles also underestimated LAI from both litter collection and allometry by 9%-30% and 8%-28%, respectively. Although the contribution of woody materials to LAI was overestimated in correction scheme A, it was effective in correcting L_efrom DHP with zenith angles of 45º-60º (ring 4), and also effective for L_efrom LAI-2000 method with zenith angles of rings both 1-3 and 1-4. Correction scheme B was all effective in correcting L_efrom DHP with four zenith angle ranges. Generally, correction scheme C was more effective than other two schemes in correcting L_efrom both DHP and LAI-2000 method with four zenith angle ranges. These results indicate that the zenith angle range is a key factor for determining the accuracy of optical methods in estimating LAI besides woody materials and clumping effects.     

利用3种校正方案提高间接法测定兴安落叶松人工林叶面积指数的精度

木质部和集聚效应是影响间接法测定叶面积指数(LAI)精度的主要因素, 尤其是木质部的校正一直存在争议。针对这一问题, 该研究首先利用半球摄影法(DHP)和LAI-2000植物冠层分析仪法(LAI-2000法) 2种间接法测定了小兴安岭兴安落叶松(Larix gmelinii)人工林叶面积最大时期的有效LAI (L_e), 然后提出了A、B、C 3种校正方案来提高间接法的测定精度。同时, 利用凋落物法和异速生长方程法2种直接法测定LAI, 以凋落物法测定值为标准来评估3种校正方案的校正效果, 并检验天顶角范围对校正结果是否存在显著影响。结果表明: 在0-45° (1-3环)、0-60° (1-4环)、45°-60° (4环)及0-75° (1-5环) 4种不同天顶角范围内, DHP测定的L_e比凋落物法、异速生长方程法测定值分别低估19%-32%和8%-29%; 而LAI-2000法也得到相似的结论, 分别低估9%-30%和8%-28%。虽然校正方案A高估了木质部对LAI的贡献, 但在45º-60º天顶角范围内, 能有效地校正DHP测定的L_e, 在1-3环和1-4环天顶角范围内, 能有效地校正LAI-2000法测定的L_e。4种天顶角范围内, 校正方案B均能有效地校正DHP测定的L_e。整体来看, 4种天顶角范围内, 校正方案C对DHP和LAI-2000法测定值的校正效果均优于其他2种方案。研究结果表明除木质部和集聚效应外, 天顶角范围的选择也是决定间接法测定LAI精度的重要因素。     

Estimation of deciduous forest leaf area index using direct and indirect methods

This study evaluated one semi-direct and three indirect methods for estimating leaf area index (LAI) by comparing these estimates with direct estimates derived from litter collection. The semi-direct method uses a thin metallic needle to count a number of contacts across fresh litter layers. One indirect method is based on the penetration of diffuse global radiation measured over the course of a day. The second indirect method uses the LAI-2000 plant canopy analyser (PCA) which measures diffuse light penetration from five different sky sectors simultaneously. The third indirect method uses the Demon portable light sensor to measure the penetration of direct beam sunlight at different zenith angles over the course of half a day. The Poisson model of gap frequency was applied to estimate plant area index (PAI) from observed transmittances using the second and third methods. Litter collection from 11 temperate decidous forests gave values of LAI ranging from 1.7 to 7.5. Estimates based on the needle method showed a significant linear relationship with LAI values obtained from litter collections but were systematically lower (by 6–37%). PAI estimates using all three indirect techniques (fixed light sensor system, LAI-2000 and Demon) showed a strong linear relationship with LAI derived from litter collection. Differences, averaged over all forest stands, between PAI estimates from each of the three indirect methods and LAI from litter collections were below 2%. If we consider that LAI=PAI–WAI (wood area index) then, all three indirect methods underestimated LAI by an additional factor close to the value of WAI. One reason could be a local clumping of architectural canopy components: in particular, the spatial dispositions of branchlets and leaves are not independent, leading to a non-random relationship between the distributions of these two canopy components.     

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。     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

利用冠层光谱估测烟草叶面积指数和地上生物量

综合多种烟草类型、品种及肥料处理因素,分析了17种光谱参数与烟草叶面积指数（LAI）、地上鲜生物重（AFW）、地上干生物重（ADW）的关系,建立逐步回归模型对烟草LAI、AFW、ADW进行估测并结合相关分析筛选出相应的特征变量。结果表明：5个回归方程的复确定系数R^2、回归系数相伴概率均达到显著水平。包含17个光谱参量的逐步回归方程筛选出的第一自变量均为Rg／Rr,相关分析及散点图分析亦得出Rg／Rr与LAI、AFW、ADW相关系数分别为0．759、0．611、0．647,R^2为0．576、0．3727、0．4184,均达到极显著水平,证明烟草LAI、AFW、ADW的特征变量为Rg／Rr。仅采用8种植被指数建立模型,证明利用比值植被指数（RVI）估测LAI、ADW亦是可行的。经过统计检验,建立的模型估测效果均较好,估测值与实测值的相关性均达到显著水平,其中包含特征变量Rg／Rr的回归模型估测效果优于RVI构建的模型。表明采用高分辨率光谱或宽波段光谱提取光谱变量可对烟草LAI、AFW、ADW进行监测,并可根据数据条件选择有效的估测模型,为烟草遥感数据分析提供方法。     

玉米叶面积指数动态模拟的最适野外观测资料

基于锦州农田生态系统野外观测站2005-2011年多个品种的玉米大田试验资料,结合已经建立的玉米叶面积指数动态普适模型,探讨了准确模拟玉米叶面积指数动态所需的最适野外观测资料.结果表明: 准确模拟玉米叶面积指数动态至少需要3年的野外观测数据,且每年在生育期内至少需要进行4次观测.玉米生育期内的理想观测应为在出苗后20 d左右进行第1次观测,此后每月观测1次.
     

自动曝光对半球摄影法测量叶面积指数及其季节变化的影响

自动曝光是影响半球摄影法(DHP)测量叶面积指数(LAI)精度的重要误差源之一.本研究基于小兴安岭地区的阔叶红松林、白桦次生林、红松人工林和兴安落叶松人工林,利用DHP和LAI-2200植物冠层分析仪分别测量6—9月每个月中旬的LAI,首先比较两种方法测量LAI的差异性,再检验森林类型和测量时期对建立两种方法测定值间的相关关系是否存在显著影响,最后构建适于校正不同森林类型不同时期自动曝光对DHP测量LAI产生误差的经验模型.结果表明: 4种森林类型4个时期内,在自动曝光设置下DHP测量的LAI比LAI-2200测量值低估20%～49%;森林类型对构建两种方法测量LAI值的经验模型不存在显著影响,而测量时期存在显著影响.本研究构建的A、B两种分类经验模型,分别适用于校正4种森林类型在6和9月、7和8月DHP测量的LAI.经分类经验模型校正后,DHP测量4种森林类型4个时期的LAI值提高了45%～79%,测量精度可提高到83%～94%.通过DHP和LAI-2200测量LAI值间的经验模型,可有效校正自动曝光对DHP测量LAI的影响,极大地提高其测量精度,为使用DHP快捷、高效地测量不同森林类型的LAI及其季节动态提供技术支持.     

Leaf lifespan as a determinant of leaf structure and function among 23 amazonian tree species

Summary The relationships between resource availability, plant succession, and species' life history traits are often considered key to understanding variation among species and communities. Leaf lifespan is one trait important in this regard. We observed that leaf lifespan varies 30-fold among 23 species from natural and disturbed communities within a 1-km radius in the northern Amazon basin, near San Carlos de Rio Negro, Venezuela. Moreover, leaf lifespan was highly correlated with a number of important leaf structural and functional characterisues. Stomatal conductance to water vapor (g) and both mass and area-based net photosynthesis decreased with increasing leaf lifespan (r²=0.74, 0.91 and 0.75, respectively). Specific leaf area (SLA) also decreased with increasing leaf lifespan (r²=0.78), while leaf toughness increased (r²=0.62). Correlations between leaf lifespan and leaf nitrogen and phosphorus concentrations were moderate on a weight basis and not significant on an area basis. On an absolute basis, changes in SLA, net photosynthesis and leaf chemistry were large as leaf lifespan varied from 1.5 to 12 months, but such changes were small as leaf lifespan increased from 1 to 5 years. Mass-based net photosynthesis (A/mass) was highly correlated with SLA (r²=0.90) and mass-based leaf nitrogen (N/mass) (r²=0.85), but area-based net photosynthesis (A/area) was not well correlated with any index of leaf structure or chemistry including N/area. Overall, these results indicate that species allocate resources towards a high photosynthetic assimilation rate for a brief time, or provide resistant physical structure that results in a lower rate of carbon assimilation over a longer time, but not both.     

Plastic changes of leaf mass per area and leaf nitrogen content in response to canopy openings in saplings of eight deciduous broad-leaved tree species

Leaf nitrogen content per area (N_area) is a good indicator of assimilative capacity of leaves of deciduous broad-leaved trees. This study examined the degrees of increase in N_area in response to canopy openings as leaf mass per area (LMA) and leaf nitrogen content per mass (N_mass) in saplings of eight deciduous broad-leaved tree species in Hokkaido, northern Japan. Five of the species were well-branched species with a large number of small leaves (lateral-growth type), and the other three species were less-branched species with a small number of large leaves (vertical-growth type). The degrees of increase in N_area were compared between the two crown types. In closed-canopy conditions, leaves of the vertical-growth species tended to have a lower LMA and higher N_mass than those of the lateral-growth species, which resulted in similar N_area for both. LMA increased in canopy openings in the eight species, and the degrees of increase were not largely different between the lateral- and vertical-growth species. On the contrary, N_mass was unchanged in canopy openings in the eight species. As a result, N_area of each species increased in canopy openings in proportion to the increase in LMA, and the degrees of increase in N_area were similar in the lateral- and vertical-growth species. Therefore, this study showed that the degrees of increase in N_area were not correlated with the crown architecture (i.e., the lateral- and vertical-growth types).     

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。     

小麦叶面积指数与冠层反射光谱的定量关系

在分析不同氮素水平下小麦叶面积指数（LAI）和冠层光谱反射率随生育期变化模式的基础上,确立了LAI与冠层光谱反射率及光谱参数的相关关系,提出了小麦LAI的敏感光谱参数及预测方程.结果表明,小麦LAI和近红外短波段（760～1 220 nm）反射率都随施氮量的增加呈上升趋势,可见光波段反射率则相反;从拔节期到成熟期,LAI和近红外短波段反射率均表现为先上升后下降的趋势,而可见光波段（460～710 nm）反射率随生育期的推进先降低后升高,以孕穗期反射率最低,近红外长波段区域（1 480～1 650 nm）反射率的变化与可见光部分相同.LAI与可见光波段反射率呈负相关,与近红外短波段反射率呈极显著正相关,其中以810 nm相关性最好.可以选择RVI(810,510)和DVI(810,560)作为反演小麦LAI的光谱参数.另外,在证明垂直植被指数PVI和转换型土壤调整指数TSAVI对LAI预测能力的同时,发现利用RVI(810,510)、DVI(810,560)和PVI 3个植被指数共同推算小麦LAI的准确度更高.     

利用凋落物法和林木因子模拟小兴安岭阔叶红松林叶面积指数

叶面积指数(LAI)是研究森林生态系统生理生态进程中关键的结构参数之一。目前,凋落物法是在非破坏性条件下能直接测定森林生态系统LAI的最有效的方法,然而将凋落叶按树种分类增加了该方法的实施难度。平均优势度模型、林分优势度模型和局域优势度模型基于凋落物法和林木因子(如胸高断面积basal area,BA;坐标)能精确地预测落叶阔叶林的LAI,而这些模型是否适用于针阔混交林仍未进行验证。以小兴安岭阔叶红松林(Pinus koraiensis)为研究对象,先利用凋落物法测定其LAI,依此为参考对3种模型预测LAI的有效性进行验证,并以红松、冷杉(Abies nephrolepis)、紫椴(Tilia amurensis)、五角槭(Acer mono)、枫桦(Betula costata)和裂叶榆(Ulmus laciniata)为例,探讨了基于凋落物法测定的LAI与BA的相关关系。结果表明:平均优势度模型不适于预测针阔混交林的LAI;林分优势度模型预测效果较好,精度达86%;局域优势度模型预测效果最优,精度高于90%。然而,为准确测定阔叶红松林的LAI,应最少选择测定8个主要树种的比叶面积。基于凋落物法测定的6个树种的LAI与其BA均显著相关(P0.01),最小R2为0.67。研究结果可为快速、准确地测定针阔混交林的LAI提供依据,为非破坏性条件下建立树种的LAI与其BA的相关关系提供参考。     

云冷杉针阔混交林叶面积指数的空间异质性

叶面积指数(Leaf area index,LAI)是森林生态过程的关键参数和描述森林冠层结构的重要指标。用半球摄影技术对吉林省汪清林业局金沟岭林场的10块1 hm2云冷杉针阔混交林的LAI进行测定,采用地统计学的半变异函数和普通克里格法对研究区的LAI的空间异质性进行了分析。结果表明:10块样地的10 m×10 m小样方内以及样地间的LAI离散程度较小,但分布有一定的规律。LAI的空间相关性存在不确定性,可由线性、孔状、高斯3种模型有效的描述,空间相关性存在的尺度范围变异大,中等和强空间相关性的影响范围在15—155m之间,10块样地LAI的空间相关性的影响范围平均为65.637m。普通克里格插值结果显示,LAI的空间分布呈明显的条带状和斑块状的梯度变化。LAI与林分平均胸径、林分平均高和林分平均冠长呈显著负相关,与林分平均冠幅、林分密度以及树种个数呈显著正相关。研究结果可为不同尺度云冷杉针阔混交林LAI的估计提供依据。     

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。