机载激光雷达的作物叶面积指数定量反演

为了进一步挖掘激光雷达在植被垂直结构探测上的潜力,通过引入Kuusk的多层均匀冠层方向反射模型的单次散射部分,基于激光雷达发射和回波波形的高斯特征,模拟作物激光雷达回波,建立了作物叶面积体密度和叶面积指数的反演方法。模型输入参数的敏感性分析显示：G函数对反演结果的影响比土壤和叶片反射率大。最后利用“黑河综合遥感联合试验”的数据对反演方法进行了验证：反演的作物叶面积体密度与实测数据基本一致,叶面积指数反演的相对误差为12.5%。结果表明该方法可以有效反演作物叶面积体密度和叶面积指数,为作物结构参数反演提供了新的途径。     

基于低空无人机成像光谱仪影像估算棉花叶面积指数

农作物叶面积指数(leaf area index,LAI)遥感监测具有快速、无损的优势。该文以低空无人机作为遥感平台,使用新型成像光谱仪获取的农田高光谱影像数据对棉花LAI进行反演。利用影像高光谱分辨率的特点,针对传统固定波段植被指数(fixed-bandvegetation index,F_VI)进行改进,通过动态搜索相应植被指数定义所使用波段范围内的反射率极值的方法,计算与各类植被指数对应的极值植被指数(extremum vegetation index,E_VI)。分别以原始全波段光谱反射率、连续投影算法(successive projections algorithm,SPA)提取的有效波段反射率以及各类F_VI和E_VI作为自变量,使用最小二乘和偏最小二乘(partial least squares,PLS)回归等方法构建LAI遥感估算模型。结果显示:1)以植被指数为自变量的模型估算效果(验证R2最高为0.85)优于以光谱反射率作为自变量的模型(验证R2最高为0.59);2)使用E_VI作为自变量能够显著提高LAI的估测精度(验证R2最大提高了0.11);3)使用PLS回归算法结合多个E_VI建立的LAI-E_VIs-PLS模型精度最高。使用LAI-E_VIs-PLS模型对棉花地块高光谱影像进行反演,制作棉花LAI空间分布图,取得良好的估算结果(验证R2=0.88,RMSE=0.29),为农作物LAI遥感监测提供了新的技术手段。     

基于无人机高光谱遥感的冬小麦叶面积指数反演

叶面积指数(leaf area index,LAI)是评价作物长势和预测产量的重要依据。光谱特征信息作为高光谱遥感的突出优势在追踪LAI动态变化方面极其重要;然而,围绕光谱特征信息所开展的无人机高光谱遥感反演作物LAI的相关研究鲜有报道。该文利用ASD Field Spec FR Pro 2500光谱辐射仪(ASD Field Spec FR Pro 2500 spectroradiometer,ASD)和Cubert UHD185 Firefly成像光谱仪(Cuber UHD185 Firefly imaging spectrometer,UHD185)在冬小麦试验田进行空地联合试验,基于获取的孕穗期、开花期以及灌浆期地面数据和无人机高光谱遥感数据,估测冬小麦LAI。该文选择同步获取的冬小麦冠层ASD光谱反射率数据作为评价无人机UHD185高光谱数据质量的标准,依次从光谱曲线变化趋势、光谱相关性以及目标地物光谱差异三方面展开分析,结果表明458~830 nm(第3~96波段)的UHD185光谱数据可靠,可使用其探测冬小麦LAI,这为今后无人机UHD185高光谱数据的使用提供了参考。该文研究对比分析了UHD185数据计算的红边参数和光谱指数与冬小麦LAI的相关性,结果表明:12种参数中比值型光谱指数RSI(494,610)与LAI高度正相关,是估测LAI的最佳参数;基于比值型光谱指数的对数形式lg(RSI)构建的线性模型展现出lg(RSI)与lg(LAI)较优的线性关系(决定系数R2=0.737,参与建模的样本个数n=103),且lg(LAI)预测值和lg(LAI)实测值高度拟合性(R2=0.783,均方根误差RMSE=0.127,n=41,P0.001);该研究为利用无人机高光谱遥感数据开展相关研究积累了经验,也为发展无人机高光谱遥感的精准农业应用提供了参考。     

Modeling leaf area index from litter collection and tree data in a deciduous broadleaf forest

Leaf area index (LAI) is an important index in ecological and meteorological studies. The litter trap method is commonly used to measure LAI in deciduous forests. To reduce the time consumed in sorting leaf litterfall by species in the litter trap method, we developed four models to predict LAI using litter traps and tree census data. The local dominance model, which estimates the leaf litterfall amount of each species by their local dominance, predicted mean and spatial variability of LAI most accurately compared to the 2 models that did not take into account spatial heterogeneity of species distribution within a forest or the model that estimated litterfall amount from leaf dispersal function. Therefore, this model can be employed instead of sorting leaf litter by species. Furthermore, we found that leaf mass per area (LMA) of at least 10 dominant species are essential for accurate estimation of LAI. Present results suggest that spatial variability of LAI is mainly due to spatial variance of leaf litterfall followed by spatial heterogeneity of species distribution within a forest, and difference in LMA among species.     

基于无人机遥感影像的大豆叶面积指数反演研究

作物叶面积指数的遥感反演是农业定量遥感研究热点之一,利用无人机遥感监测系统获取农作物光谱信息精确反演叶面积指数对精准农业生产与管理意义重大。本研究以山东省嘉祥县一带的大豆种植区为试验区,设计以多旋翼无人机为平台同步搭载Canon Power Shot G16数码相机和ADC-Lite多光谱传感器组成的无人机农情监测系统开展试验,分别获取大豆结荚期和鼓粒期的遥感影像。使用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合田间同步实测叶面积指数(leaf area index,LAI)数据,采用经验模型法分别构建了单变量和多变量LAI反演模型,通过决定系数(R2)、均方根误差(RMSE)和估测精度(EA)3个指标筛选出最佳模型。研究表明,有选择性地分时期进行农作物的叶面积指数反演是必要的,鼓粒期作为2个生育期中大豆LAI反演的最佳时期,其NDVI线性回归模型对大豆LAI的解释能力最强,R2=0.829,RMSE=0.301,反演大豆LAI最准确,EA=85.4%,生成的鼓粒期大豆LAI分布图反映了当地当时大豆真实长势情况。因此,以多旋翼无人机为平台同步搭载高清数码相机和多光谱传感器组成的无人机农情监测系统对研究大豆叶面积指数反演是可行性,可作为指导精准农业研究的一种新方法。     

An improved estimate of leaf area index based on the histogram analysis of hemispherical photographs

Leaf area index (LAI) is a key parameter that affects the surface fluxes of energy, mass, and momentum over vegetated lands, but observational measurements are scarce, especially in remote areas with complex canopy structure. In this paper we present an indirect method to calculate the LAI based on the analyses of histograms of hemispherical photographs. The optimal threshold value (OTV), the gray-level required to separate the background (sky) and the foreground (leaves), was analytically calculated using the entropy crossover method (Sahoo, P.K., Slaaf, D.W., Albert, T.A., 1997. Threshold selection using a minimal histogram entropy difference. Optical Engineering 36(7) 1976–1981). The OTV was used to calculate the LAI using the well-known gap fraction method. This methodology was tested in two different ecosystems, including Amazon forest and pasturelands in Brazil. In general, the error between observed and calculated LAI was ～6%. The methodology presented is suitable for the calculation of LAI since it is responsive to sky conditions, automatic, easy to implement, faster than commercially available software, and requires less data storage.     

Non-destructive estimation of sunflower leaf area and leaf area index under different water regime managements

Leaf area (LA) is a valuable parameter in many agronomic and plant physiological studies. Its measurement is time consuming and involves leaf destruction. Therefore, there is a tendency in using simple, fast, non-destructive, and electronic devices methods to estimate LA. The aim of this study was to estimate LA across different water regime treatments using a combination of leaf mass and leaf dimensions of sunflower (Helianthus annuus L.). For this purpose, different leaf sizes were collected from plants during the growing season on different time intervals. Experiment was conducted during 2012 summer time in Sari Agriculture Sciences and Natural Resources University, Iran. On field leaf dimension measurements were carried out, and leaves sketches were put on paper, scanned and then areas were measured using AutoCAD software. Multivariate linear and non-linear regression models were constructed between LA and other leaf components measured. All constructed models provided highly significant correlations (r = 0.90–0.99) between LA and different leaf components. The exponential model [LA = 0.619 [(L × W)^0.5]^2.019] provided the best estimation of sunflower LA (R² = 0.993). In conclusion, the simple and quick models developed in this study could predict the sunflower LA and leaf area index (LAI) with high precision.     

夏玉米叶面积指数增长模型的研究

以玉米多品种多年试验资料，研究了反映区域叶面积指数(LAI)动态变化的模拟模型，该模型以积温指标表示的生育阶段为自变量，综合不同地理位置、品种、播期、密度等的影响，是一个扩展的Logistic叶面积生长模型，经检验可很好地模拟不同生育阶段叶面积指数动态变化，可用于区域作物生长模拟模型和区域作物生长监测及遥感估产。     

Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies

We have shown previously that the leaf area index of uniform crops can be determined by using the sun's beam as a probe. Here, we show theoretically and empirically that the leaf area index of vegetation with large gaps can be measured by suitably averaging the local gap frequency, as detected by the transmission of the direct beam of the sun. The recommended procedure is to average the transmission linearly over a horizontal path ten times the characteristic width of a leaf and take the logarithm of this mean. The average of logarithms for many such paths is shown to be linearly related to the leaf area index. The estimate of leaf area index obtained by this procedure is more accurate than that obtained by averaging the transmission once over the full path. The analysis has been confirmed with measurements of crops of sorghum and wheat. The method is simple and appears most appropriate for forests.     

利用时序合成孔径雷达数据监测水稻叶面积指数

为了确定全极化雷达数据监测水稻叶面积指数动态变化的精度,该文对水稻叶面积指数与后向散射系数进行了各生长阶段建模比较。采用广东雷州地区多时相多入射角精细全极化Radarsat-2数据,结合水稻全生育期地面样方实测数据,首先分析多入射角归一化后四极化(vertical-horizontal polarization,VH;vertical-vertical polarization,VV;horizontal-horizontal polarization,HH;horizontal-vertical polarization,HV)、比值极化HH/VV后向散射系数与水稻叶面积指数(leaf area index,LAI)随时间变化特征以及在营养生长阶段、生殖生长阶段和全生育期的相关关系,提取相关系数高于0.8的极化与生长阶段进行水云模型建模,最终生成多期水稻LAI反演分布图,并验证该数据反演水稻各生长阶段LAI的精度,探索SAR数据追踪区域尺度水稻长势的可行性。结果表明,在地形较为平坦的水稻集中连片种植区,VV、HH/VV后向散射系数与LAI在营养生长期、全生育期极显著相关(P0.01),相关系数均高于0.83。营养生长阶段VV、HH/VV水云模型拟合决定系数分别为0.77、0.87,全生育期VV、HH/VV水云模型拟合决定系数分别为0.73、0.8,营养生长阶段模型优于全生育期模型。精细四极化SAR数据监测区域尺度水稻LAI动态变化具有应用潜力,优选的极化模型为进一步的水稻长势监测提供依据。     

基于机器学习的棉花叶面积指数监测

为实现基于机器学习和无人机高光谱影像进行棉花全生育期叶面积指数(Leaf Area Index,LAI)监测,该研究基于大田种植滴灌棉花,在不同品种及不同施氮处理的小区试验基础上,对无人机获取的高光谱数据分别采用一阶导(First Derivative,FDR)、二阶导(Second Derivative,SDR)、S...     

水稻叶面积指数及产量信息的空间结构性分析

通过对水稻抽穗期叶面积指数及产量信息的采样研究,探讨了叶面积指数及产量信息的空间结构性,并建立了半方差函数模型,结果表明该变量具有明显的区域化变量特征和较好的空间结构特征.该文引入区域化变量理论,弥补了单纯采用经典概率统计法对作物信息进行分析的片面性.水稻抽穗期叶面积指数与产量信息之间呈抛物线关系,相关性达显著水平,说明可以通过对叶面积指数的科学调控,使一定尺度范围内的水稻产量获得全面提高.对作物信息进行空间结构性和定量化研究为精确农业的实施提供了必要的技术支撑.     

Documenting improvement in leaf area index estimates from MODIS using hemispherical photos for Australian savannas

This paper compares estimates of Leaf Area Index (LAI) obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) collections 4.8 (MC4) and 5.0 (MC5) with ground-based measurements taken along a 900 km north-south transect through savanna in the Northern Territory, Australia. There was excellent agreement for both the magnitude and timing in the annual variation in LAI from MC5 and biometric estimates at Howard Springs, near Darwin, whereas MC4 overestimated LAI by 1-2 m² m⁻² for the first 200 days of the year. Estimates of LAI from MC5 were also compared with those obtained from the analysis of digital hemispherical photographs taken during the dry season (September 2008) based on algorithms that included random and clumped distribution of leaves. Linear regression of LAI from MC5 versus that using the clumping algorithm yielded a slope close to 1 (m = 0.98). The regression based on a random distribution of leaves yielded a slope significantly different from 1 (m = 1.37), with higher Mean Absolute Error (MAE) and bias compared to the clumped analysis. The intercept for either analysis was not significantly different from zero but inclusion of five additional sites that were visually bare or without green vegetation produced a statistically significant offset of +0.16 m² m⁻² by MC5. Overall, our results show considerable improvement of MC5 over MC4 LAI and good agreement between MC5 and ground-based LAI estimates from hemispherical photos incorporating clumping of leaves.     

基于时间序列红外图像的玉米叶面积指数连续监测

针对受田间变化光照影响冠层图像参数计算的精度及自动化程度仍然不高的问题,该文提出了一种基于冠层顶视单角度红外图像序列的玉米叶面积指数(leaf area index,LAI)获取方法。首先,在玉米整个生育期内获取冠层顶部垂直向下红外图像序列,针对冠层图像背景分割易受田间变化光照影响,提出了一种基于绿色植物"红边"现象和冠层图像背景正态分布模型的分割方法,方法计算简便精度高于支持向量机分割。在冠层参数解析阶段,根据玉米叶片球形分布假设,简化了顶视冠层图像的叶片投影函数(G函数),利用Beer-Lambert定律推导了图像冠层孔隙度计算叶面积指数的方法。试验结果表明:该方法与间接测量原理的商业化设备测量值具有较高的相关性,叶面积指数测量的决定系数为0.94。方法应用于2个不同年代品种冠层结构动态变化监测,能够准确反映冠层结构差异,建立了冠层孔隙度与植株干质量(R2=0.95,R2=0.94)植株鲜质量(R2=0.96,R2=0.89)的关系模型,该方法简化了玉米冠层结构参数测量过程,可为田间环境下冠层参数的自动连续监测提供了解决方案。     

基于环境星CCD图像的甘蔗叶面积指数反演方法

及时准确获取甘蔗叶面积指数对于甘蔗长势监测和产量预测具有重要意义。尝试通过构建组合核函数,利用支持向量回归方法建立甘蔗LAI估算模型,并利用新型国产卫星数据环境星CCD图像和准同步的地面观测数据,分别采用指数关系模型、对数关系模型、支持向量回归模型3种方法,以广西甘蔗主产县为例,开展了环境星遥感图像在甘蔗叶面积指数反演试验。结果表明,3种方法都可以对甘蔗LAI进行有效预测,且能获得较好的预测效果,验证了环境星CCD图像在甘蔗LAI反演中的实用性,其中支持向量回归模型反演精度最高：5月份决定性系数R2分别比     

水稻叶面积指数和产量的空间变异性及关系研究

该文运用地质统计学方法分析了水稻叶面积指数与产量的空间分布及关系,结果表明：叶面积指数和产量均近似正态分布;在所研究的条件下,产量和大部分生育阶段(除拔节期)的叶面积指数都具有空间结构;孕穗期、抽穗期和乳熟期叶面积指数与产量在一定范围内具有显著空间相关关系。借鉴指示克立格的思想,提出了指示值分布法,分析了以上3个生育阶段的综合叶面积指数分布与产量的关系,该方法不仅对于水稻的遥感估产和精确农业的实施具有借鉴意义,且对于其它领域研究某一变量与某几个变量的空间分布关系,或某一变量随时间的动态变化与另一个变量的关系具有一定的参考价值。     

21000-2300nm波段的小麦冠层光谱特性与叶面积指数和叶片氮含量间的关系及其受叶片水分含量的影响

The effects of leaf water status in a wheat canopy on the accuracy of estimating leaf area index （LAI） and N were determined in this study using extracted spectral characteristics in the 2 000-2 300 nm region of the short wave infrared （SWI） band. A newly defined spectral index, relative adsorptive index in the 2 000-2 300 nm region （RAI2000-2300）, which can be calculated by RAI2000-2300 = （R2224 - R2054） （R2224 ＋ R2054）-1 with R being the reflectance at 2 224 or 2 054 nm, was utilized. This spectral index, RAI2000-2300, was significantly correlated （P 〈 0.01） with green LAI and leaf N concentration and proved to be potentially valuable for monitoring plant green LAI and leaf N at the field canopy scale. Moreover, plant LAI could be monitored more easily and more successfully than plant leaf N. The study also showed that leaf water had a strong masking effect on the 2 000-2 300 nm spectral characteristics and both the coefficient between RAI2000-2300 and green LAI and that between RAI2000-2300 and leaf N content decreased as leaf water content increased.     

基于氮素效应的烤烟叶面积指数动态模拟

为明确不同施氮水平下烤烟群体叶面积指数的动态特征以及其与活动积温的关系,本研究以‘豫烟12’、‘秦烟96’、‘云烟87’为供试材料,设4个施氮水平,分别为N0(0 kg·hm~(-2))、N1(30 kg·hm~(-2))、N2(60 kg·hm~(-2))、N3(90 kg·hm~(-2)),测定分析不同积温下烤烟群体叶面积指数及其动态特征,利用Curve Expert 1.40软件模拟并通过极限值法筛选建立了归一化积温模型,为烤烟群体光合结构的改善提供一定的理论依据。结果表明:1)烤烟群体叶面积指数随移栽后活动积温呈偏度0的单峰曲线变化,而随着施氮水平的增加呈现增加趋势,相同施氮水平下烤烟群体叶面积指数峰值大小表现为‘秦烟96’‘云烟87’‘豫烟12’。2)有理函数模型y=(a+bx)/(1+cx+dx2)具有较好的模拟效果和生物学意义,能够很好地模拟烤烟群体相对叶面积指数随相对活动积温的变化,决定系数为0.980 7**;利用2015年试验数据对模型进行检验,模拟准确度(k)均大于0.958,精确度(R2)均大于0.95,均方根误差RMSE均小于6.04%。3)模型参数在某些品种和施氮水平之间表现出显著差异性,品种和施氮量主要通过调节模型参数b、c、d实现对整个模拟模型的调节。4)烤烟群体相对叶面积指数变化速率曲线呈"N"型变化,反映了烤烟群体叶面积指数的实际变化趋势。5)施氮量对模型次级参数具有调节作用,随着施氮量增加烤烟群体平均叶面积指数、叶面积指数最大值呈增加趋势,可作为烤烟群体叶面积指数氮素调节的重要参考指标。该模型的建立可以为烤烟群体发育动态监测以及烤烟群体叶片光合特性的提升提供理论依据和决策支持。     

全国农作物叶面积指数遥感估算方法

目前对农作物叶面积指数LAI的遥感估算研究多是针对单一作物或是作物种植结构单一的区域,该文运用大尺度农作物叶面积指数的遥感估算方法,在像元尺度上对4个代表性实验站的LAI与归一化植被指数(NDVI)的相互关系进行了回归分析后,得到4种代表性作物种植结构的LAI估算模型,然后结合全国农作物种植结构数据对模型外推,建立了一个全国尺度的遥感模型,并估算了全国作物LAI。该文使用“863”项目山东遥感应用综合试验中的作物LAI观测数据进行了验证,结果表明该模型较其它估算模型达到了较高的精度,最大相对误差为39%,平均的相对误差为19%。该模型的计算结果已经在“中国农情遥感速报”系统中得到了广泛的应用。