水稻冠层光谱特征及其与LAI的关系研究

氮素营养是影响作物生长与产量的最主要限制因子之一。准确及时地监测或诊断出作物氮素营养状况,对提高氮素利用效率和作物管理水平、减少过度施氮造成的环境污染具有重要意义。本研究在不同施氮水平处理的水稻试验小区,对水稻整个生长期内冠层反射光谱进行了较系统、密集的测定,同时测定了几个重要生育期水稻的叶面积指数。研究结果表明:随着施氮量的增加,水稻冠层光谱在各生育期间呈现出一定的规律性,在近红外部分(710～1 220 nm),冠层光谱反射率随着施氮水平的提高而升高,而在可见光部分(460～680 nm),水稻冠层的光谱反射率反而逐渐降低。经冠层光谱差异显著性检验发现:水稻灌浆期以前,对施氮水平最为敏感的波段是绿光(560～610 nm)和近红外(710～760 nm)部分;转换为归一化植被指数(NDVI)以后,差异最显著的是(R760-R560)/(R760+R560)。不同氮肥处理的水稻LAI随时间变化曲线大致都呈抛物线型,中低水平施氮肥水稻LAI随时间的变化曲线比较平缓,而高水平施氮肥LAI曲线则变化比较剧烈。冠层光谱反射与叶面积的相关分析结果表明:在水稻抽穗前,叶面积与冠层光谱反射率相关性较差;而抽穗后,叶面积与冠层光谱有较高的相关性。     

遥感植被指数与冬小麦绿叶面积系数和麦土比的关系

一、引言研究作物冠层反射光谱特征与冠层状态参数之间的关系,是作物遥感估产的基础。由可见光和近红外波段反射率组成的植被指数随作物冠层状态参数变化的规律,在国外文献中已有报导。鉴于作物冠层覆盖百分率与冠层光谱特性密     

磷营养胁迫对冬小麦冠层光谱的影响

因为磷素重要的营养作用,其胁迫的存在影响冬小麦的正常生长。借助地面遥感仪器获取冬小麦在磷营养胁迫下的多个生育期里的冠层光谱数据并对其影响特征进行了分析。利用因子分析方法提取主因子与含有丰富信息的光谱变量,并结合极显著水平(0.01)的均值比较与检验过程考察了冬小麦冠层光谱,确定了对磷营养胁迫敏感的光谱波段:760nm,810nm和870nm与950nm,并在此基础上结合冬小麦对磷素的吸收利用特征选定了运用冠层光谱敏感波段反射率探测和区分磷营养胁迫的关键生育期:拔节期。结果同时表明,对冬小麦磷营养胁迫而言,近红外区间(760nm~1100nm)光谱反射特征的区分能力要强于可见光区。本文同时指出了研究与发展利用遥感技术进行营养胁迫监测的方法和着重点。     

植物冠层光辐射分布的快速深度缓存算法

针对植物冠层内光分布计算过程中存在的计算复杂度大、耗时长等问题,提出一种快速深度缓存算法。采用动态选取光照投影平面和确定光照分布密度等方法,设计一套快速计算植物冠层光分布的模拟系统。应用该系统模拟水稻孕穗期冠层内的光分布情况,结果表明,该方法在保持原有算法精度的同时,能缩短计算时间。     

用高光谱数据反演健康与病害落叶松冠层光合色素含量的模型研究——基于2005年吉林省敦化、和龙两市落叶松冠层采样测量数据

健康落叶松与遭受病害落叶松的冠层光谱反射率曲线具有明显差异,利用反映这种差异的光谱特征参数建立回归模型,可为反演落叶松冠层光合色素含量进而诊断落叶松健康状况提供方法和途径。以吉林省延边州敦化、和龙两市林场中健康的和遭受落叶松早落病侵害的人工落叶松林为调查对象,在对野外采集的14个落叶松冠层样本进行光谱测量及光合色素含量测量的基础上,选取8个对落叶松冠层光合色素含量变化反映敏感的光谱参数参与建立其光合色素含量的一元线性回归和多元逐步回归模型。研究结果表明,不同健康程度的落叶松冠层光谱曲线在其可见光及近红外波段有3个比较明显的特征差异处,分别位于光谱曲线的“绿峰”、“红谷”和“红边”位置。利用反映这些差异的8个光谱特征参数建立落叶松冠层光合色素含量的回归模型,除 “红边”这一参数回归效果不令人满意外,其余7个参数均得到了较好的回归效果,其中利用峰谷波长差Dgr建立的关于总叶绿素和叶绿素b含量的一元回归模型R²值分别达到0.842 8和0.749 8,利用NDGI建立的关于叶绿素a和类胡萝卜素含量的一元回归模型R²值分别达到0.875 8和0.789 7;多元逐步回归模型的回归效果与一元回归模型相比,各判定系数R²值均有所提高,总叶绿素、叶绿素a、b和类胡萝卜素含量的回归模型R²值分别达到0.885、0.910、0.839和0.862。     

机载高光谱数据提取冬小麦冠层叶绿素含量的模型分析及验证

遥感提取叶绿素含量的方法是精准农业的重要研究方向之一,但是如何用冠层光谱数据有效地提取叶绿素含量仍然是一个难点。本文用光谱指数TCARI和OSAVI的组合建立提取冬小麦冠层叶绿素含量的关系式,并使用实验田获取的冬小麦冠层光谱以及与之同步的机载高光谱传感器OMIS数据进行了验证。通过误差分析讨论了该方法用于遥感高光谱数据时需要注意的问题,表明大气校正的精度,传感器的信噪比以及波段中心的漂移是模型反演精度的主要制约因素。     

基于TM影像的冬小麦苗期长势与植株氮素遥感监测研究

叶面积指数和叶片氮素含量是决定小麦群体长势的重要生理指标,也是制定栽培管理措施的必要依据。利用遥感监测小麦返青后的叶面积指数和叶片氮素含量,便于及时采取施肥、灌溉、中耕等调控措施,达到优质、高产稳产、高效的目的。本文使用TM影像数据与实地GPS定位相结合的方法,研究了冬小麦返青后叶面积指数及植株氮素含量的变化态势。结果表明:(1)TM影像的NDVI的地域性差异较大,且随纬度呈现极明显的线型负相关变化态势;(2)将用NDVI反演的LAI与实测的LAI进行比较,二者较为一致,其均方差根(RMSE)为0.111;(3)利用NDVI监测的小麦植株氮素含量与实地观测的植株氮素含量较为相近,二者的RMSE为0.085。总之,利用TM影像的NDVI可以快速、精确地监测返青期小麦的LAI和植株氮素营养状况。同时,本研究结果也可为冬小麦返青期的苗情诊断和管理决策提供及时、准确的信息支持。     

水稻冠层图像的背景分割方法研究

对水稻冠层图像中背景分割的方法进行了研究。应用杂草识别中常用的灰度化方法(超绿色法)进行实验,并提出了灰度化方法,然后运用迭代法获得最佳阈值将灰度图像转化为二值图像,最后对二值图像进行形态学腐蚀、膨胀的处理。并通过实验验证了该方法的优越性和可行性。     

基于ETM和图像融合的优势植被冠层叶面积指数和消光系数的遥感反演

叶面积指数和消光系数是表征植被群体冠层结构及光能利用的地球表层下垫面参量,国内外对叶面积指数的遥感反演有较多的研究与应用,但对消光系数的遥感反演尚不多见。我国南方少见单一大面积的均匀植被分布。为更好地匹配叶面积指数和光合有效辐射(用于估算消光系数)的实测数据,反映植被混交和疏密不均的状态,以Landsat ETM作为遥感信息源,通过HSV、Brovey和Gram-Schmidt(GS)3种图像融合方法的比较,选取效果最佳的图像融合方法,将ETM融合成空间分辨率为15 m的多光谱数据。以鄱阳湖源头梅江流域为研究区,在实测优势植被叶面积指数和光合有效辐射的基础上,利用植被指数法经验公式法反演流域的叶面积指数,并根据Beer-Lambert定律,建立了流域优势植被冠层消光系数的反演模型。在此基础上,反演了流域植被冠层叶面积指数和消光系数的空间分布,为SWAT植物生长模式的修正提供输入数据基础。     

在水分胁迫下棉花冠层叶片全氮含量的高光谱遥感估算模型研究

研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:①随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;②.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;③由一阶微分光谱衍生出基于光谱“红边”位置变量的分析方法,使我们认识到“红边”的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;④如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。     

The Red Edge Parameters Diversification Disciplinarian and Their Application in Winter Wheat

Some red edge parameters( λ red, Min λ 6oo-72o, d λ,red, d λ min, d λ red / d λ min, ∑ d λ 680-750, and λ nir) and the relationship between these parameters and the parameters of biochemistry and biophysics of winter wheat were studied by regression analysis. The results indicated that there existed some changes in these red edge parameters in the whole growth stages,and there were strong correlations between red edge parameters and pramters of biochemistry and biophysics. Thus, the red edge parameters were found valuable for assessment of wheat parameters of biochemistry and biophysics. The λ red can be used to estimate the soluble sugar content and the chlorophyll content. The d λ red was the best estimator of total nitrogen content. LAI can be estimated by Min λ 600-720 satisfactorily.     

Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests

This paper presents the analysis of radiative transfer assumptions underlying moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) algorithm for the case of spatially heterogeneous broadleaf forests. Data collected by a Boston University research group during the July 2000 field campaign at the Earth Observing System (EOS) core validation site, Harvard Forest, MA, were used for this purpose. The analysis covers three themes. First, the assumption of wavelength independence of spectral invariants of transport equation, central to the parameterization of the MODIS LAI and FPAR algorithm, is evaluated. The physical interpretation of those parameters is given and an approach to minimize the uncertainties in its retrievals is proposed. Second, the theoretical basis of the algorithm was refined by introducing stochastic concepts which account for the effect of foliage clumping and discontinuities on LAI retrievals. Third, the effect of spatial heterogeneity in FPAR was analyzed and compared to FPAR variation due to diurnal changes in solar zenith angle (SZA) to asses the validity of its static approximation.     

冬小麦品质的影响因素及高光谱遥感监测方法

研究了小麦品质的分类及其构成因素与环境条件之间的关系，各品质因素之间的关系。运用相同栽培条件下不同品种品质指标间的关系和变化规律，研究了品种因素对小麦品质的影响程度以及品种因素与品质指标之间的相关性，得出相同环境条件下籽粒的蛋白质含量与湿面筋含量、沉降值、吸水率、形成时间和稳定时间之间存在极显著的相关性。并利用不同品种、不同肥水条件下的作物关键生育时期的生化参量与光谱指数进行分析，得出开花期冬小麦叶片的类胡萝卜素与叶绿素a的比值与结构不敏感植被指数(SIPI)之间存在极显著的正相关，决定系数达到0．7207，冬小麦体内的全氮含量与类胡萝卜紊与叶绿素口的比值之间存在极显著的负相关，决定系数为0．7245，并通过分析开花期冠层生化组分与籽粒品质指标间的相关性，得出开花期叶片全氮与籽粒蛋白质、湿面筋、干面筋和沉降值之间存在极显著的正相关，表面运用开花期光谱指数来反演叶片全氮含量，进而用来预测预报籽粒品质是切实可行的。     

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r²) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

基于DSP的恒压频比控制SVPWM方式的研究和实现

电压空间矢量(SVPWM)调制方式把逆变器和电机看作一个整体处理,具有所得模型简单,便于数字化实现,并具有转矩脉动小、噪声低、电压利用率高等优点。近年来在变频调速系统中逐渐得到了应用和重视。本文叙述了空间电压矢量的基本原理。讨论了不对称SVPWM波形及两种对称SVPWM波形的产生方法及优缺点。利用数字信号处理器TMS320F240产生基于恒压频比控制的对称SVPWM调制波形。实验结果表明SVPWM的调制波。和准优化PWM有异曲同工之处,其实质也是一种带谐波注入的调制方法。     

Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rondônia, Brazil

This study analyzed the relationships between soil fertility and remotely sensed measures over three pasture chronosequence sites in the state of Rondônia, in the western Brazilian Amazon region. Remotely sensed measures included shade, nonphotosynthetic vegetation (NPV), green vegetation (GV) and soil (derived from spectral mixture analysis), and the normalized difference vegetation index (NDVI). These were correlated against soil fertility parameters such as phosphorus, potassium, calcium, and base saturation. In temporal analysis, it was observed that NPV dominated the spectral responses of pasture canopies and tended to increase with pasture age as well. The increase of NPV appeared to be related to the decline of soil fertility, but soil texture variation also played a role. In the correlation analysis, soil P, known as the most limiting nutrient for pasture productivity, showed the highest correlation with remotely sensed measures, followed by soil K and base saturation. However, this result was not observed at the sites where nutrient availability was very low.     

UML中用例间关系的研究

该文深入研究了用例模型中用例之间的关系:包含、扩展、泛化。从事件流的细节剖析了用例关系的执行过程,显示了他们的本质特征。给出了具体的描述方式。并给出了具体的实例。