基于GIS与SDM技术的可视化空间数据分类研究

提出将GIS与可视化空间数据挖掘技术之集成的基本框架。在此基础上,基于VisualC++6.0和Ma-pObject2.0组件技术设计和开发了一个可视化交互空间数据挖掘分类系统,系统采用决策树方法和贝叶斯网络作为数据挖掘方法的基本算法,采用训练与学习相结合实现空间数据的分类。文中用实例数据对系统性能、算法和规则有效性进行了验证。结果表明,该系统是一个适用的、可扩展的可视化交互空间数据挖掘工具,系统能够实现数据挖掘实时动态的交互控制,实现了数据挖掘过程的可视化、挖掘模型的可视化和结果的可视化显示、可视化思考、可视化分析与评价。     

GPS测量在巴郎沟水电站施工控制网中的应用

介绍了巴郎沟水电站GPS施工控制网的布设、基线处理及平差方法,分析了GPS施工控制网精度及基线粗差,实现了在隐蔽地区较高精度GPS施工测量的应用.     

基于Quaternary编码的球面三角格网邻近搜索算法

分析了球面Quaternary编码的特点,给出了三角格网方向判断的规则,并对该算法与Bartholdi邻近搜索算法的时间复杂度进行了对比。结果表明,分解算法的平均时间耗时量约是Bartholdi邻近搜索算法的23.66%。     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

全站仪三角高程新方法及精度估算

利用传统三角高程原理和方法推导一种全站仪三角高程测量新方法计算公式。该方法的优点是在观测的过程中不需要量测仪器和棱镜高,从而达到提高精度的目的;并利用误差传播定律进行精度估算和分析,通过与《工程测量规范》中相应的水准闭合差比较,得出在一定条件下该方法可以代替三、四等水准测量。此方法用在施工测量中可以大大地提高工作效率和缩短工程工期。     

基于等高线构建TIN模型的平三角形修正算法

设计了一种平三角形修正算法来修正TIN模型中可能出现的平三角形区域。该算法通过对由平三角形组成的区域进行分类,并针对不同的平区域采用交换边、增加点甚至不处理的修正方法,同时对由于等高线数据错误产生的一些特殊情况进行了分析处理。实验结果表明,该算法能够去除约束型狄洛尼三角网中所有应修正的平三角形,并生成对地形描述更为精确的三角网。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.     

Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal,South Africa using field spectra resampled to the Sumbandila Satellite Sensor

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non-destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. In the context of South Africa, due to the cost and availability of imagery, studies focusing on the estimation of plant water content using remote sensing data have been limited. With the scheduled launch of the South African satellite SumbandilaSat evident in 2009, it is imperative to test the utility of this satellite in estimating plant water content. This study resamples field spectral data measured from a field spectrometer to the band settings of the SumbandilaSat in order to test its potential in estimating plant water content in a Eucalyptus plantation. The resampled SumbandilaSat wavebands were input into a neural network due to its ability to model non-linearity in a dataset and its inherent ability to perform better than conventional linear models. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41% on an independent test dataset outperforming the traditional multiple regression method of estimation. The best-trained neural network algorithm that was chosen for assessing the relationship between plant water content and the SumbandilaSat bands was based on a few points only and more research is required to test the robustness and effectiveness of this sensor in estimating plant water content across different species and seasons. This is critical for monitoring plantation health in South Africa using a cheaply available local sensor containing key vegetation wavelengths.     

联合Delaunay三角网的等高线群综合研究

采用联合Delaunay三角网来提取成组等高线的弯曲,在弯曲处利用平三角形连接法进行局部地形结构线的连接,并建立起地形结构线的树状结构。运用方根模型确定谷地选取的数量指标,并提出了一种新的数学模型以确定谷地选取的质量指标,完成等高线群的自动综合。研究和实验表明,该方法充分顾及了地貌形态特征,谷地选取合理,概括程度适当,并完全避免了相邻等高线相交,综合效果较好。     

一种QoS保证的容错拓扑控制算法

提出了一种QoS保证的容错拓扑控制算法,该算法能够构建干扰最小的K连通拓扑结构,并使任意节点间的最短(最小跳数)路径的干扰最小。仿真结果表明,在保持相同强度抗毁能力的同时,新的算法降低了网络中的干扰,更好地改善了网络的传输性能。