Methods for Setting up a Three-Dimensional Industrial Surveying System of “Building Blocks Type”

1　Methodsfororientatinginitialdirec tioninindustrialsurveyDifferentfromconventionalengineeringsurvey,thedistancebetweenthetwoangle measurementdevicesinindustrialsurveyisnomorethantenmeters.Theaccu racyforthemeasurementofobjectpointsisrequiredtobebetween± ( 0 .0 2～ 0 .0 5～ 0 .2 0 )mm ,sotheaccu racyfortheorientationoftheinitialdirectionoughttobehigh .Theexistingmethodsfororientatingtheinitialdirectionbetweenthetwoangle measurementdevicesincludemethodofusinginnertarget,collimatormethod,metho…     

无地面控制GPS辅助光束法区域网平差

介绍了在WGS-84坐标系中进行GPS辅助光束法区域网平差的基本原理以及利用大地水准面拟合方法实施大地高与正常高的变换方法。利用新疆库尔勒地区1：25000和1：50000实际航摄资料验证了理论与程序实现的正确性。试验结果表明，无地面控制GPS辅助光束法区域网平差可用于中小比例尺测图的加密，在WGS-84坐标系中可以获得较高的点定位精度，而在国家80坐标系下，利用大地水；隹面拟合前后的GPS摄站坐标进行GPS辅助光束法区域网平差，加密精度没有明显差异。     

导航卫星自主定轨的算法研究及模拟结果

讨论了利用卫星-卫星间的距离观测值建立导航卫星系统自主定轨的数学方法，并用GPS星座模拟星间观测值，采用滤波算法计算卫星的状态参数。模拟结果表明，在空间测距精度一定的情况下，自主定轨精度能高于GPS广播星历，因而能较精确地维持卫星系统的坐标框架。     

利用AMTIS数据反演植被和土壤温度

遥感陆面温度 (LST)是目前遥感界面临的重要课题 ,组分温度反演则为LST反演的主攻目标。如果混合像元组分间的温度及发射率存在明显差别 ,就可以从多角度热辐射亮度变化中分离出组分温度的信息。在热辐射矩阵模型的基础上 ,讨论了确定可反演参数和反演组分温度的方法 ,并以AMTIS多角度热红外图像为数据源 ,在进行几何和大气纠正后 ,尝试利用矩阵反演方法分解混合像元中的植被和土壤温度。误差分析表明土壤温度反演结果较好 ,但植被温度的反演精度不高     

基于GPS掩星技术反演大气参数模型的优化

准确掌握地球大气中的水气分布，了解水气变化趋势对天气现象、全球气候变化、数值预报具有理论研究及实用价值。以无线电掩星技术为基础，利用掩星数据反演大气参数剖面。对原反演模型的不足进行了论证，并给出了反演个例。详细地论述了通过引入MM5先验温度T再通过线性迭代的方法反演对流层下部水汽廓线原理，给出了优化后模型反演个例。并对模型优化后反演廓线中存在的问题进行了分析，提出了下一步优化方向。     

Stability and accuracy analysis of the central difference method for real‐time substructure testing

The central difference method (CDM) that is explicit for pseudo‐dynamic testing is also believed to be explicit for real‐time substructure testing (RST). However, to obtain the correct velocity dependent restoring force of the physical substructure being tested, the target velocity is required to be calculated as well as the displacement. The standard CDM provides only explicit target displacement but not explicit target velocity. This paper investigates the required modification of the standard central difference method when applied to RST and analyzes the stability and accuracy of the modified CDM for RST. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods*

Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over‐ and underpredicted observations based on local factors.     

Correcting the Smoothing Effect of Ordinary Kriging Estimates

The smoothing effect of ordinary kriging is a well-known dangerous effect associated with this estimation technique. Consequently kriging estimates do not reproduce both histogram and semivariogram model of sample data. A four-step procedure for correcting the smoothing effect of ordinary kriging estimates is shown to be efficient for the reproduction of histogram and semivariogram without loss of local accuracy. Furthermore, this procedure provides a unique map sharing both local and global accuracies. Ordinary kriging with a proper correction for smoothing effect can be revitalized as a reliable estimation method that allows a better use of the available information.