岩石视反射率研究

根据对净月潭地区自然条件状况的分析和样品采集及数据预处理情况,采用线性回归方法对岩石视反射率进行研究与探讨。     

遥感图像的位置校正算法

通过对遥感图像解译中传统的几何位置校正方法的研究,提出了一种曲面样条函数算法,从而能解决传统的方法校正不完全的缺陷。     

Preparation of volume table of SAL (Shorea robusta) - an approach using satellite data

Sal (Shorea robusta) is an important forest tree species in north and north-eastern India. Large-scale plantations of this species have been raised there under taungya and coppice system of management. The conventional volume table prepared for high sal forest is referred to infer the volume of production of this species. Earlier workers have used aerial remote sensing data to develop volume tables of this species. In the present study a volume table for sal is developed based on remotely sensed satellite data using a regression technique. A two-step method was developed to estimate mean tree volume from satellite data. In step 1, mean crown diameter — an intermediate variable - was estimated from satellite data. In step 2, the estimated mean crown diameter was used to estimate the mean tree volume. Addition of age of the crop as an independent variable improved the predictive ability of the regression equation.     

南沙群岛海域浅海水深提取及影像海图制作技术

研究了南沙群岛海域TM图像提取浅海水深的模型和方法以及影像海图制作技术。在传统的多波段线性回归模型的基础上,引入数据分组平均预处理、潮汐改正、分段线性回归和数据归一化等技术,使改进后的模型更加合理并具有较高的精度,从而适用于大的海域。在此基础上,提出了一整套遥感影像海图制作的技术和工艺流程。     

最优子集的神经网络预报建模研究

作者尝试用最优子集方法进行神经网络长期预报模型的建模方法研究。结果表明,在很多情况下,由于最优子集方法比逐步回归方法能选取更好的预报因子,因此所构造的神经网络预报模型具有更好的拟合和预报效果,这为神经网络在长期预报的应用研究提供了新的思路和方法。     

A GENERALIZED CANONICAL MIXED REGRESSION MODEL FOR ENSO PREDICTION WITH ITS EXPELMENT

A scheme is proposed for predicting NINO-region SST in terms of a generalized canonical mixedregression model based on principal component canonical correlation analysis(PC-CCA). and into the scheme are introduced such techniques as EEOF, PRESS criterion and consensus prediction. By optimizing physicalfactors and selecting optimal model parameters, experiments were made successful in predicting the LINO SST index for 1 to 4 seasons to follow. The scheme is shown to be stable in operation and its total technical level compares well with that of the model published in NOAA/NWS/NCEP CPC Climate Diagnostics Bulletin. butthe number of factors needed in our scheme is much fewer than that for the CPC's model in dealing with the sameproblems. This makes it possible to establish an operational ENSO monitoring system in China.     

Comparison of trends in stream water quality

A method is described for assessing the strength of evidence for differences in the trends in chemical concentrations in stream water between catchments. A smoothing spline technique is used to model changes in water quality as a result of changes in flow rates, seasonal effects and an underlying trend. The method involves fitting the model twice, once with the underlying trends constrained to be identical for each river and once with the trends unconstrained. Statistical properties are assessed by simulation methods that allow for the autocorrelation in the residuals from the unconstrained fit. The method is applied to data from two streams in the Balquhidder catchments in the Highlands of Scotland and to data from the Dee, Don and Ythan rivers in the north‐east of Scotland. Copyright © 2003 John Wiley & Sons, Ltd.     

GPS坐标向国家坐标转换的三维分离回归法

三维分离回归法是一种简单、有效、实用的转换方法,该方法可有效地克服病态矩阵问题。同时,为切实、有效地提高三维分离回归法的转换精度,计算时引入了三向尺度因子,使得三维转换参数由传统的7个变为现在的9个。详细介绍了三维分离回归法的基本原理、转换过程及转换实例。     

Water temperature modelling: comparison between the generalized additive model,logistic, residuals regression and linear regression models

Water temperature has a significant influence on aquatic organisms, including stenotherm fish such as salmonids. It is thus of prime importance to build reliable tools to forecast water temperature. This study evaluated a statistical scheme to model average water temperature based on daily average air temperature and average discharge at the Sainte-Marguerite River, Northern Canada. The aim was to test a non-parametric water temperature generalized additive model (GAM) and to compare its performance to three previously developed approaches: the logistic, residuals regression and linear regression models. Due to its flexibility, the GAM was able to capture some of the nonlinear response between water temperature and the two explanatory variables (air temperature and flow). The shape of these effects was determined by the trends shown in the collected data. The four models were evaluated annually using a cross-validation technique. Three comparison criteria were calculated: the root mean square error (RMSE), the bias error and the Nash-Sutcliffe coefficient of efficiency (NSC). The goodness of fit of the four models was also compared graphically. The GAM was the best among the four models (RMSE = 1.44°C, bias = ?0.04 and NSC = 0.94).     

Trend analysis of nutrient loadings in a large prairie catchment

Nutrient loadings in many river catchments continue to increase due to rapid expansion of agriculture, urban and industrial development, and population growth. Nutrient enrichment of water bodies has intensified eutrophication which degrades water quality and ecosystem health. In this study, we carried out a trend analysis of total phosphorus and total nitrogen loads in the South Saskatchewan River (SSR) catchment using a novel approach to analyse nutrient time series. Seasonal analysis of trends at each of the water quality stations was performed to determine the relationships between annual flow regimes and nutrient loads in the catchment, in particular, the influence of the high spring runoff on nutrient export. Decadal analysis was also performed to determine the long-term relationships of nutrients with anthropogenic changes in the catchment. Although it was found that seasonal and historical variability of nutrient load trends is mainly determined by streamflow regime changes, there is evidence that increases in nitrogen concentration can also be attributed to anthropogenic changes.