Algorithm for global leaf area index retrieval using satellite imagery

Leaf area index (LAI) is one of the most important Earth surface parameters in modeling ecosystems and their interaction with climate. Based on a geometrical optical model (Four-Scale) and LAI algorithms previously derived for Canada-wide applications, this paper presents a new algorithm for the global retrieval of LAI where the bidirectional reflectance distribution function (BRDF) is considered explicitly in the algorithm and hence removing the need of doing BRDF corrections and normalizations to the input images. The core problem of integrating BRDF into the LAI algorithm is that nonlinear BRDF kernels that are used to relate spectral reflectances to LAI are also LAI dependent, and no analytical solution is found to derive directly LAI from reflectance data. This problem is solved through developing a simple iteration procedure. The relationships between LAI and reflectances of various spectral bands (red, near infrared, and shortwave infrared) are simulated with Four-Scale with a multiple scattering scheme. Based on the model simulations, the key coefficients in the BRDF kernels are fitted with Chebyshev polynomials of the second kind. Spectral indices - the simple ratio and the reduced simple ratio - are used to effectively combine the spectral bands for LAI retrieval. Example regional and global LAI maps are produced. Accuracy assessment on a Canada-wide LAI map is made in comparison with a previously validated 1998 LAI map and ground measurements made in seven Landsat scenes.     

Estimating leaf area index from satellite data

A method for estimating leaf area index from visible and near infrared measurements of vegetation above a soil background is applied to a Landsat Thematic Mapper data set. Some constants required for the procedure are inferred from the scattergram of data values. The resulting image illustrates variability of leaf area index over an agricultural area. The mixed-pixel case, corresponding to low-resolution data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) is also discussed, and a vegetation index is suggested for both high- and low-resolution data. Consideration of the two types of data leads to the suggestion that a sampled high spatial resolution sensor (50-100 m) be added to the AVHRR in order to permit accurate inference of vegetation conditions over agricultural areas     

基于小波变换与支持向量机回归的冬小麦叶面积指数估算

叶面积指数(LAI)是作物长势诊断及产量预测的重要参数。通过对冬小麦采样点的高光谱曲线进行连续小波变换(CWT),然后利用小波系数与LAI 建立支持向量机回归(SVR)模型,实现冬小麦不同生育时期的叶面积指数估算。通过对所研究方法与选取的植被指数、偏最小二乘(PLS)回归等5种方法的反演结果进行统计分析。结果表明:利用连续小波变换确定的LAI 的敏感波段为680、739、802、895 nm,对应尺度分别为8、4、9 和8,对应小波系数的LAI 回归确定系数(R2)明显高于冠层反射率的回归确定系数;利用小波系数与LAI 建立的SVR 模型的反演精度最高,模型实测值与预测值的检验精度(R2)为0.86,均方根误差(RMSE)为0.43;而常用植被指数(归一化植被指数,NDVI;比值植被指数,RVI)建立的估测模型对冬小麦多个生育时期LAI 反演精度最低(R2 0.76,RMSE0.56)。因此利用连续小波变换进行数据预处理,能更好地筛选出对叶面积指数敏感的信息,LAI 回归方法比较结果表明,SVR 比PLS 更适合于LAI 的估测,通过将CWT 与SVR 结合(CWT-SVR)能实现不同生育时期冬小麦叶面积指数的遥感估算。     

Estimating leaf area index from satellite imagery using Bayesian networks

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks.     

利用可见/近红外光谱测定小麦叶面积指数的改进研究

改进了小麦叶面积指数的可见/近红外光谱测定模型。以不同方法实现了小麦冠层反射光谱的预处理,并采用偏最小二乘回归算法（PLS）建立小麦叶面积指数估测模型对其进行比较分析,发现小波除噪结合一阶导数能最有效地消除原始光谱的噪声与背景信息,此时PLS模型校正集与预测集R2分别为0.849与0.835。为进一步优化模型,对经一阶导数结合小波除噪后的光谱采用主成分分析法（PCA）降维,以前4个主成分（含原始光谱84.867%特征信息）为输入变量,采用小二乘支撑向量机回归算法（LS-SVR）建立了小麦叶面积指数估测模型,其校正集与预测集R2分别达0.905与0.883,具有比PLS算法更高的精度。结果表明:以小波除噪结合一阶导数去除小麦冠层反射光谱中的土壤背景信息以提高模型精度是可行的,且LS-SVR是建模的优选方法。     

最小二乘支持向量机用于时间序列叶面积指数预测

遥感反演的叶面积指数（LAI）时间序列被广泛应用于气候模拟、作物长势监测等研究。但遥感数据受天气等因素影响,时间序列的LAI 数据存在缺失。支持向量机（SVM）是一种有效的数据分类和回归预测工具,而最小二乘支持向量机（LS-SVM）是对SVM 的有效改进。以西藏那曲县为例,使用2003-2011 年MODIS LAI 产品,分别用LS-SVM 和SVM 两种方法对研究区域2011 年LAI 时间序列进行预测,并用MODIS 原始LAI 以及部分地面实验样点值进行验证。结果表明,基于LS-SVM 的LAI 时间序列预测算法的精度比基于SVM 的算法高,从而证明LS-SVM 方法能够弥补遥感反演时间序列LAI 数据的缺失问题,对提高时间序列的LAI 遥感产品质量具有重要意义。     

星载激光雷达GLAS与TM光学遥感联合反演森林叶面积指数

通过对地球科学激光测高系统(Geoscience Laser Altimeter System,GLAS)波形数据进行高斯分解,提取精确的波形特征信息,计算出GLAS波形数据激光穿透指数(LPI),基于LPI提出GLAS数据反演叶面积指数(LAI)的新方法,建立了GLAS数据反演森林LAI的模型(R2=0.84,RMSE=0.64),并用留一交叉验证法(LOOCV)对反演模型的可靠性进行了验证,结果表明,该模型没有过度拟合,具有很好的泛化能力,最后通过人工神经网络融合GLAS与TM(Thematic Mapper,专题制图仪)遥感数据实现区域尺度森林LAI反演,用25个实测LAI对反演精度进行了验证,研究表明反演LAI与实测值较为接近,精度较高(R2=0.76,RMSE=0.69),为生态环境研究提供精确的输入参数,为GLAS数据大区域高精度LAI反演提供新的方法和思路.     

基于数据分割与主成分分析的LAI遥感估算

针对叶面积指数(LAI)经典统计反演模型存在估算效果不理想以及反演效率低等问题,提出了一种基于农学物候的数据分割与主成分分析结合的遥感估算方法.综合了原始光谱和微分(或差分)光谱主成分信息作为自变量,融入了以农学物候为先验的数据分割思想,并引入了多尺度建模方式参与反演过程.以冬小麦为实验对象,进行数值模拟和比较分析.结...     

Theoretical limits to the estimation of the leaf area index on thebasis of visible and near-infrared remote sensing data

The Leaf Area Index (LAI) of a plant canopy is an important environmental parameter required by various applications. It would be highly desirable to be able to estimate this parameter on the basis of satellite remote sensing data in the optical spectral range. However, LAI affects the propagation of light in a plant canopy (and therefore its measurable reflectance factor) exclusively through a boundary condition of the equation of radiation transfer. It is shown that LAI may be retrievable accurately and reliably only when the canopy is optically thin enough to allow a significant illumination of the underlying soil, and when the optical properties of this soil are such that the radiance field emerging from this level is sufficiently different from that which would be exhibited by a deeper canopy. The combinations of radiative conditions (soil and plant properties) necessary for the reliable and accurate retrieval of the LAI on the basis of remote sensing reflectance data acquired above the canopy in the red and near-infrared spectral regions are investigated and documented with the help of simulation studies. These results show the retrievability of LAI from remote sensing data in optimal situations, however     

Charge transfer and blooming suppression of charge transfer photodiode area array

A charge-transfer photodiode array combines the advantages of diffused diodes for broad and smooth spectral response and analog registers for low-noise readout. The device structure described is that of a high-speed low-blooming 100-by-100 diode array using bucket-brigade readout registers. Two of the mechanisms which are essential to successful operation of this combination structure are studied. These mechanisms are the charge transfer from sensing diode to the analog register and blooming suppression. It is found that the charge-transfer speed degrades sharply with reducing light level due to subthreshold leakage behavior of the MOS transfer gate. This degradation is eliminated by using a background charge supplied from the analog register. Experimental data confirms the validity of the concept.     

利用气象火箭探测资料估算戈壁地区高空折射率结构常数廓线

大气光学湍流严重影响光学系统的正常工作,大气折射率结构常数C_n²廓线的获取是计算大气湍流影响效应的基础,而目前C_n²廓线的实测与估算通常限于平流层底部以下,对于平流层中部以上的C_n²廓线则研究较少。为了研究更高高度的C_n²分布特征,利用戈壁地区气象火箭探测获取的常规气象参数,以30 km为界分两层估算了C_n²廓线,在30 km以上,探索性地尝试了基于Tatarskii公式的参数化方案。研究结果表明：在30 km以下,估算的C_n²数量级和变化趋势与历史数据较一致;在30 km以上,在假定满足均匀各向同性理论情况下,估算的C_n²随着高度增加整体呈减小趋势且变化平稳,平均数量级从10^-20减小到10^-23。由于目前关于湍流的理论基础还不够完善,因...     

Coordinate-descent super-resolution and registration for parametric global motion models

We present a method for simultaneously obtaining registration and super-resolution from a sequence of low resolution images, based on a coordinate-descent approach. The novelty of the algorithm resides on the registration step, which can be applied easily to any parametric global motion model. We prove the validity of the model with synthetic and real data experiments, being of special interest the good performance achieved in the difficult case of images registered under a projective transformation.     

Estimation of global motion parameters by complex linear regression

Global motion is very likely to occur in image sequences analysis. For example, it arises if the observer is moving during the sequence acquisition (ego-motion). Our aim is to get a simple method to estimate in a reliable may a set of parameters that can take into account the presence of a global motion component, using only local information. The novelty of our approach is in regarding spatial shift, change of scale, and rotation (corresponding to usual camera effects such as pan and zoom) as a two-dimensional (2-D) Doppler effect. The mathematical treatment is carried on in the complex plane, so that the results can be easily deduced as an extension of the one-dimensional (1-D) case; in this way, we obtain simple expressions, well suited for a practical realization of the estimate. The method has been experimentally validated by both real pictures with a synthetic motion and real image sequences.     

Estimation of capability index based on bootstrap method

A process capability index C_pmk is recently proposed to measure the degree of process capability of a system. C_pmk is a generalized version of the existing indices C_p, C_pk, and, C_pm, which have been studied extensively in the last few years. In this paper, we present the estimators of C_pmk and derive its asymptotic distribution. We also utilize the asymptotic variance of the estimator of C_pmk to derive the two-sided confidence interval for C_pmk based on a percentile-t bootstrap method. Our new confidence intervals are shown to outperform the ones based on the percentile and standard bootstrap method by simulation.     

Estimation and choice of neighbors in spatial-interaction models of images

Some aspects of statistical inference for a class of spatial-interaction models for finite images are presented: primarily the simultaneous autoregressive (SAR) models and conditional Markov (CM) models. Each of these models is characterized by a set of neighbors, a set of coefficients, and a noise sequence of specified characteristics. We are concerned with two problems: the estimation of the unknown parameters in both SAR and CM models and the choice of an appropriate model from a class of such competing models. Assuming Gaussian-distributed variables, we discuss maximum likelihood (ML) estimation methods. In general, the ML scheme leads to nonlinear optimization problems. To avoid excessive computation, an iterative scheme is given for SAR models, which gives approximate ML estimates in the Gaussian case and reasonably good estimates in some non-Gaussian situations as well. Likewise, for CM models, an easily computable consistent estimate is given. The asymptotic mean-squared error (mse) of this estimate for a four-neighbor CM model is shown tn be substantially less than the mse of the popular coding estimate. Asymptotically consistent decision rules are given for choosing an appropriate SAR or CM model. The usefulness of the estimation scheme and the decision rule for the choice of neighbors is illustrated by using synthetic patterns. Synthetic patterns obeying known SAR and CM models are generated, and the models corresponding to true and several competing neighbor sets are fitted. The estimation scheme yields estimates close to the parameters of the true models, and the decision rule for the choice of neighbors picks up the true model from the class of competing models.     

Telecommunications reforms: options, models, and global challenges

Telecommunications reforms are gaining momentum around the globe, spurred by technological change as well as a growing urgency to attract financial investment in a sector increasingly recognized as critical for economic growth. Particularly in developing countries, investment in the telecommunications infrastructure is considered a top priority to combat low telephone densities and poor service quality and to take advantage of modern technologies. In developed countries, pressures for reform continue to mount as a consequence of innovation and increased international trade in telecommunications services and equipment. Throughout the world, the continued internationalization of telecom providers and services will create new exigencies for all countries to make sometimes dramatic changes so as to position themselves to participate in a new global order     

Estimation of network reliability using graph evolution models

Monte Carlo techniques for estimating various network reliability characteristics, including terminal connectivity, are developed by assuming that edges are subject to failures with arbitrary probabilities and nodes are absolutely reliable. The core of the approach is introducing network time-evolution processes and using certain graph-theoretic machinery, resulting in a considerable increase in accuracy for Monte Carlo estimates, especially for highly reliable networks. Simulation strategies and numerical results are presented and discussed