WHICH PRINCIPAL COMPONENTS TO UTILIZE FOR PRINCIPAL COMPONENT REGRESSION

Principal components(PCs)for principal component regression(PCR)have historically been selectedfrom the top down for a reliable predictive model.That is,the PCs are arranged in a list starting withthe most informative(PC associated with the largest singular value)and proceeding to the leastinformative(PC associated with the smallest singular value).PCs are then chosen starting at the top ofthis list.This paper discusses an alternative procedure of treating PC selection as an optimization prob-lem.Specifically,without any regard to the ordering,the optimal subset of PCs for an acceptablepredictive model is desired.Five data sets are analyzed using the conventional and alternative approaches.Two data sets are spectroscopic in nature,two data sets deal with quantitative structure-activityrelationships(QSARs)and one data set is concerned with modeling.All five data sets confirm thatselection of a subset without consideration to order secures the best results with PCR.One data set isalso compared using partial least squares 1.     

Quantitative retrieval of soil salt content based on measured spectral data

Choosing the Minqin Oasis, located downstream of the Shiyang River in Northwest China, as the study area, we used field-measured hyperspectral data and laboratory-measured soil salt content data to analyze the characteristics of saline soil spectral reflectance and its transformation in the area, and elucidated the relations between the soil spectral reflectance, reflectance transformation, and soil salt content. In addition, we screened sensitive wavebands. Then, a multiple linear regression model was established to predict the soil salt content based on the measured spectral data, and the accuracy of the model was verified using field-measured salinity data. The results showed that the overall shapes of the spectral curves of soils with different degrees of salinity were consistent, and the reflectance in visible and near-infrared bands for salinized soil was higher than that for non-salinized soil. After differential transformation, the correlation coefficient between the spectral reflectance and soil salt content was obviously improved. The first-order differential transformation model based on the logarithm of the reciprocal of saline soil spectral reflectance produced the highest accuracy and stability in the bands at 462 and 636 nm; the determination coefficient was 0.603, and the root mean square error was 5.407. Thus, the proposed model provides a good reference for the quantitative extraction and monitoring of regional soil salinization.     

Seasonal nutritive value of native grasses of Argentine Calden Forest Range

This study assessed the nutritive value of the most important forage species of the Calden forest (central semi-arid La Pampa, Argentina), for samples collected in fall, winter and spring, under grazing conditions and during two successive years, for ranges of good and fair conditions. The crude protein concentration (CP) of short-winter grasses (Piptochaetium napostaense, Poa ligularis, Stipa clarazii and Hordeum stenostachys) was about 10%. Mid-winter grasses (S. tenuissima and S. gynerioides) never reached 6% CP. Summer grasses (Digitaria californica and Trichloris crinita) ranged from 7% to 9% CP. In vitro dry matter digestibility (IVDMD) was similar among short-winter and summer grasses (40–50%). Mid-winter grasses had the lowest IVDMD for all seasons (<40%). Effects of sampling year and range condition on CP were consistently significant only for short-winter grasses. Good condition ranges provide a more acceptable forage supply than fair condition ranges.     

MLR和PLSR的沙壤土盐分含量光谱检测对比研究

为了快速有效检测南疆地区典型土壤（沙壤土）的盐分含量变化,利用光谱仪和电导仪测得南疆阿拉尔市红枣种植区盐渍土近红外高光谱和电导率数据,基于7种不同光谱预处理方法和2种特征波长选择算法,分别建立多元线性回归（MLR）和偏最小二乘回归（PLSR）的土壤盐分监测模型。结果表明：7种预处理方法中,归一化,多元散射,变量标准化和一阶导数能够有效提高土壤盐分的预测模型精度。基于多元逐步回归（SMR）波长选择方法的多元线性回归（SMLR）模型的R_val²>0.948 9,RPD>6.294 9,RMSEP<0.435 6;基于连续投影算法（SPA）的多元线性回归（SPA-MLR）模型的R_val²>0.956 8,RPD>6.922 1,RMSEP<0.361 6,预测结果要优于偏最小二乘回归（PLSR）模型,其中基于归一化处理后的SMLR和SPA-MLR的预测精度最为理想,分别为R_val²=0.979 2,RPD=9.907 8,RMSEP=0.287 6和R_val²=0.980 5,RPD=10.50,RMSEP=0.278 3,而且筛选的特征波长较少。说明归一化是更有效的光谱预处理方法,多元线性回归（MLR）更适合建立南疆典型沙壤土盐分含量的预测模型。     

基于高光谱数据的戈壁地表砾石粒径反演研究

戈壁地表砾石粒径组成特征反映戈壁形成过程信息,且在很大程度上决定戈壁改造利用的难易,是开展戈壁研究的基础和前提。结合高光谱数据的微分变换,遴选出砾石粒径的敏感波段与反演方程,进行戈壁地表砾石粒径反演研究。结果表明：微分变换后的砾石光谱反射率与粒径有较好相关性,相关性最好的波段为908nm、983nm和985nm。其中,对数倒数微分变换之后的反射率与粒径成正相关（R² =0.61）,而一阶微分、平方根微分、对数微分3种变换形式之后的反射率与粒径呈负相关,相关系数分别为-0.633、-0.646、-0.649。将一阶微分变换后的光谱数据与粒径进行回归分析,发现一元三次回归模型具有较好的拟合精度,其中对数微分在回归分析中表现最好（R² =0.851）,经过验证得出对数微分预测精度（75.27%）高于其他4种微分形式的精度,表明砾石光谱的对数微分变换之后的908nm波段可应用于戈壁地表砾石粒径的反演。     

新疆典型绿洲土壤电导率和pH值的光谱响应特征

土壤理化性质影响土壤质量,直接决定作物的产量,极易受到灌溉的影响。选择新疆典型绿洲——渭干河-库车河三角洲绿洲作为靶区,利用土壤光谱反射率预测土壤的电导率、pH值。首先,对土壤光谱反射率做变换,得到18种形式的反射率;其次,对18种形式的反射率与土壤电导率、pH值进行相关与回归分析,得到预测方程;最后,验证预测方程的精度,并确定最佳方程。结果显示:可以用土壤的光谱反射率预测土壤电导率、pH值,土壤电导率的预测方程为反射率的一阶导数微分形式,均方根误差为0.184;土壤pH值的预测方程为倒数的二阶导数微分形式,均方根误差为0.278。快速预测土壤电导率、pH值可以为土壤质量的评价提供数据基础,有利于正确有效地指导农业生产。     

利用树木车轮资料重建西藏中部过去气候的初步尝试

为较可靠地利用年轮气候学方法重建过去气候，本文依据西藏中部，四个经过合适取样和精确定年的树木年轮年表，首先确立了它们各自生长状况对气候要素的响应函数，进一步明确年轮宽度与气候变化的关系。接着，选择可被重建的气候因子，并建立包括前期生长在内的、经过正交变换的转换函数，达到重建过去气温和降水的目的。此外，还划出不同的校准期和验证期，采用统计方法和其它类型代用资料进行检验。     

反射光谱技术:研究南极企鹅古生态演化的一种新方法

为探求快速、廉价、无损和同步的光谱技术在南极生态环境研究中的可能性,本文利用南极阿德雷岛的四根企鹅粪土沉积柱样品的反射光谱,通过逐步多元线性回归和主成分回归两种数学运算方法,建立了反射光谱数据与企鹅粪九种标型元素浓度之间的关系,并探讨了南极企鹅粪土沉积物光谱数据的古生态意义。结果表明:南极粪土沉积样品反射率光谱与企鹅粪九种标型元素(P、Ca、Cu、F、Ba、S、Zn、Sr、Se)含量之间存在良好的相关性,预测值与实测值之间的相关系数R都达到了0.9以上,在深度剖面上预测浓度与实测浓度具有非常一致的变化趋势;南极粪土沉积物光谱数据包含有明确的古生态变化信息,可利用主成分分析快速恢复历史时期企鹅数量演化过程。本研究结果为在偏远的南极地区开展古生态环境研究提供了一种新的快捷方法和技术途径。     

RESIDUAL BILINEARIZATION.PART 1:THEORY AND ALGORITHMS

When using hyphenated methods in analytical chemistry,the data obtained for each sample are given asa matrix.When a regression equation is set up between an unknown sample (a matrix) and a calibrationset (a stack of matrices),the residual is a matrix R.The regression equation is usually solved by minimizing the sum of squares of R.If the sample containssome constituent not calibrated for,this approach is not valid.In this paper an algorithm is presentedwhich partitions R into one matrix of low rank corresponding to the unknown constituents,and onerandom noise matrix to which the least squares restrictions are applied.Properties and possibleapplications of the algorithm are also discussed.In Part 2 of this work an example from HPLC with diode array detection is presented and the resultsare compared with generalized rank annihilation factor analysis (GRAFA).     

高光谱遥感土壤有机质信息提取研究

土壤反射光谱特征分析是反演土壤信息参量的基础资料。本文阐述了使用航空成像光谱仪OMIS- Ⅰ数据并 结合ASD FieldSpec FR(350~2500nm)便携式光谱仪获取野外光谱数据, 对山东省烟台市招远东良乡原状农用土有 机质含量进行反演, 从而实现有机质填图。通过对土壤原反射率对数一阶微分变换并确定其与SOM的相关性, 最 终建立相应的多元线性回归方程。分析认为土壤有机质的测定选用762nm、874nm 及1667nm 波段在本次研究中效 果最佳。该模型也可作为土壤有机质估测和评价的参考。     

基于高光谱数据的天山北坡积雪孔隙率反演研究

以新疆天山北坡中段典型流域季节性积雪为研究对象,基于高光谱遥感监测技术,分析了融雪期积雪孔隙率与光谱反射率的相关性。采用偏最小二乘法(PLS)对相关性较高的波段进行压缩,并提取贡献率最高的前四个主成分,以此用来确定神经网络的隐含节点数、输入层、输出层的初始权值,建立PLS-BP模型进行积雪孔隙率反演研究。结果表明:当隐含节点数为3,模型的线性确定相关系数(R²)较高为0.9159,RMSE为0.04,相对误差为0.23。与传统偏最小二乘回归(PLSR)、主成分回归(PCA)建模方法相比,精度较高,所建定量模型可用于高光谱遥感反演积雪孔隙率。     

塔里木河中游典型绿洲盐渍化土壤的反射光谱特征

研究盐渍化土壤的光谱特性是利用遥感技术实现在区域尺度上进行土壤盐渍化监测和评价的工作基础, 是建立地面数据和遥感数据关系的桥梁。本文以塔里木河中游典型绿洲--渭干河-库车河三角洲绿洲为研究对象, 采用光谱学技术以及多元统计相结合的方法, 研究干旱区典型绿洲盐渍化土壤的反射光谱特征。首先, 对光谱数据进行预处理(去噪、剔除水分吸收波段), 以便消除仪器本身噪声及外界条件的影响, 并且计算了部分盐渍地样本的光谱吸收特征参数, 说明相同程度的盐渍化土壤具有相似的吸收特征;其次, 研究盐渍化土壤的反射光谱与盐分因子(八大离子、电导率(EC)、含盐量(salt content)、pH、总溶解固体(TDS)等) 之间的关系, 并选择具有代表性的盐分因子与野外实测光谱数据建立定量回归模型, 通过多元线性回归分析得出含盐量、SO₄^2-、TDS、EC与原始光谱数据的相关性分别是0.746、0.908、0.798 和0.933, 达到了理想的效果。本研究对于干旱区典型绿洲盐渍土的光谱特征研究有着重要指示意义, 为发展和完善中国盐渍土理化特征的可见光-近红外反射光谱分析理论奠定科学积累, 并进一步为干旱区土壤盐渍化、沙漠化灾害等环境恶化问题的解决提供新的科学技术手段。     

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.     

基于冠层反射和植被指数的华东地区叶面指数反演

1 IntroductionLeaf A rea Index (LA I), defined as half the all-sided leaf or needle surface per unit groundsurface (Chen and Black,1992),is an im portantparam eter to quantify leaf density and m onitorvegetation change.A tthe sam e tim e,LA I is also an i…     

基于FTIR和MODIS数据估算新疆沙漠宽波段地表比辐射率

基于FTIR和MODIS数据,建立了新疆沙漠宽波段（8～13.5 μm）地表比辐射率的最优估算模型。首先,利用傅立叶变换热红外光谱仪观测的塔克拉玛干沙漠地表比辐射率光谱数据,结合同期MODIS温度/比辐射率产品MOD11A1的29、31和32波段比辐射率值和MOD09A1的第7波段反射率值,建立宽波段地表比辐射率估算模型,并分别采用观测数据和光谱库数据验证了模型的精度,估算结果的均方根误差分别为0.0041和0.0081。其次,选择最优估算模型,利用MODIS数据,估算了新疆4个沙漠的宽波段地表比辐射率,得到了沙漠地表比辐射率的空间分布特征。结果表明：塔克拉玛干沙漠和库鲁克库姆沙漠气候干燥稳定,地表比辐射率分布较为均匀,范围为0.850～0.915;古尔班通古特沙漠受到植被和地表水分的影响,比辐射率空间分布不均匀,范围为0.890～0.915;库木塔格沙漠的地表比辐射率分布与其羽状地表类似,范围为0.860～0.910。     

STEPWISE CANONICAL DISCRIMINANT ANALYSIS OF CONTINUOUS DIGITALIZED SIGNALS:APPLICATION TO CHROMATOGRAMS OF WHEAT PROTEINS

Continuous digitalized signals such as spectra,electrophoregrams or chromatograms generally have alarge number of data points and contain redundant information.It is therefore troublesome performingdiscriminant analysis without any preliminary selection of variables.A procedure for the application ofcanonical discriminant analysis(CDA)on this kind of data is studied.CDA can be presented as asuccession of two principal component analyses(PCAs).The first is performed directly on the raw dataand gives PC scores.The second is applied on the gravity centres of each qualitative group assessed onthe normalized PC scores.A stepwise procedure for selection of the relevant PC scores is presented.Themethod has been tested on an illustrative collection of 165 size-exclusion high-performance(SE-HPLC)chromatograms of proteins of wheat belonging to 55 genotypes and grown in three locations.Thediscrimination of the growing locations was performed using seven to nine PC scores and gave more than86% accurate classifications of the samples both in the training sets and the verification sets.Thegenotypes were also rather well identified,with more than 85% of the samples correctly classified.Thestudied method gives a way of assessing relevant mathematical distances between digitalized signalsaccording to qualitative knowledge of the samples.     

黄柏山树轮生长对气候因子的响应及模拟

利用大别山西部的3个黄山松（Pinus taiwanensis Hayata）树木年轮采样点的样本,建立3个标准年表,其特征值都表明黄山松树木年轮宽度中含有较高的环境信息;年表间相关密切,区域主要受气候因子影响、南北差异显著;年表与气候因素的相关也显示不同环境生长的黄山松差异性要大于共性;同样多元线性回归也确定该研究区影响树木生长的主导因子为前一年10月的均温,而前一年8月的降水同样起着非常重要的作用。不同要素的模拟结果有较好的一致性,但极端气候条件下树木的生长差异显著。     

乡村旅游的乡村性测评模型——以江西婺源为例

乡村性是乡村旅游的本质特性,对乡村性进行测评是乡村旅游可持续发展研究的基础,是指导乡村旅游开发、经营和管理的重要依据。本文采用定性与定量相结合的方法,通过理论分析选取了5个潜在因素和17个观测因子构建了乡村性测评的指标体系,运用结构方程原理,建立了乡村性CFA测评模型,并以江西婺源为例进行了实证研究。通过对婺源乡村旅游典型地区的抽样调查,获取了相关数据,建立了多元回归方程,运用SPSS软件进行回归分析,获得模型的路径系数和随机误差,并对每个回归方程进行了F检验和拟合度检验,结果显示回归方程具有较高的可靠性。对模型中的路径系数进行了t检验,t值在2.319~86.895之间,其显著性概率P小于0.01或0.05,模型中的因果关系显著,与研究的假设条件相符合,模型可以接受。     

Constraining basin thermal history and petroleum generation using palaeoclimate data in the Piceance Basin,Colorado

Careful assessment of basin thermal history is critical to modelling petroleum generation in sedimentary basins. In this paper, we propose a novel approach to constraining basin thermal history using palaeoclimate temperature reconstructions and study its impact on estimating source rock maturation and hydrocarbon generation in a terrestrial sedimentary basin. We compile mean annual temperature (MAT) estimates from macroflora assemblage data to capture past surface temperature variation for the Piceance Basin, a high‐elevation, intermontane, sedimentary basin in Colorado, USA. We use macroflora assemblage data to constrain the temporal evolution of the upper thermal boundary condition and to capture the temperature change with basin uplift. We compare these results with the case where the upper thermal boundary condition is based solely upon a simplified latitudinal temperature estimate with no elevation effect. For illustrative purposes, 2 one‐dimensional (1‐D) basin models are constructed using these two different upper thermal boundary condition scenarios and additional geological and geochemical input data in order to investigate the impact of the upper thermal boundary condition on petroleum source rock maturation and kerogen transformation processes. The basin model predictions indicate that the source rock maturation is very sensitive to the upper thermal boundary condition for terrestrial basins with variable elevation histories. The models show substantial differences in source rock maturation histories and kerogen transformation ratio over geologic time. Vitrinite reflectance decreases by 0.21%Ro, source rock transformation ratio decreases 10.5% and hydrocarbon mass generation decreases by 16% using the macroflora assemblage data. In addition, we find that by using the macroflora assemblage data, the modelled depth profiles of vitrinite reflectance better matches present‐day measurements. These differences demonstrate the importance of constraining thermal boundary conditions, which can be addressed by palaeotemperature reconstructions from palaeoclimate and palaeo‐elevation data for many terrestrial basins. Although the palaeotemperature reconstruction compiled for this study is region specific, the approach presented here is generally applicable for other terrestrial basin settings, particularly basins which have undergone substantial subaerial elevation change over time.     

基于景观格局和水土流失敏感性的大理市生态脆弱性分析

以大理市为研究对象,在2005年大理市土地利用现状数据的基础上,从景观格局与水土流失敏感性相结合角度探讨了大理区域生态环境脆弱性问题。首先,选择景观分离度（DIVISION）、周长面积比分维数（PAF-RAC）的倒数、斑块密度（PD）3个景观格局指数,分析了景观类型脆弱度;并在此基础上,以1：5万的土地利用类型数据和DEM数据为基础,以ARC／INF09．2为工具,依据通用水土流失方程,选择降水侵蚀力、土壤质地、地形起伏度和地表覆盖等自然因子作为水土流失评价指标,得出水土流失敏感性指数。通过对景观格局指数和水土流失敏感性修正指数的数值分析和空间分析,得出了大理市不敏感、轻度敏感、中度敏感、高度敏感和极度敏感等5个生态脆弱性级别分区及其空间分布特征。