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地形属性
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  “地形属性”译为未确定词的双语例句
     Digital Terrain Analysis (DTA) is a topographical information analysis technology that calculates and extracts terrain surface parameters and morphological features on Digital Elevation Model (DEM).
     数字地形分析(Digital Terrain Analysis,DTA)是在DEM上计算地形属性、提取地形特征的地形信息处理技术,其结果是地学建模的基本参数。
短句来源
     Digital Terrain Analysis (DTA) is a topographical information analysis technology to calculate and extract terrain surface parameters and morphological features on Digital Elevation Model(DEM), and the application of DEM is extended by DTA.
     数字地形分析(Digital Terrain Analysis,DTA)是在数字高程模型DEM上进行地形属性计算和特征提取的数字地形信息处理技术,是DEM应用范围的拓广和延伸。
短句来源
     Many factors, such as, DEM data structure、 DEM grid resolution、 DEM grid direction、 data accuracy、 algorithm、 analysis methods, etc. can affect the accuracy of the extracting and calculating of terrain surface parameters and morphological features on DEM.
     地形属性的计算精度直接影响地学及其相关学科模型的精度和实际应用的准确性。 基于DEM的地形信息提取受到DEM数据结构、DEM分辨率、格网方向、数据误差等结构特征、算法、分析方法等多种因素的影响。
短句来源
     Digital Terrain Model is a general designation of space topographical data set, and it is the digital description with the figure of space position and topographical attributive character.
     数字地形模型是空间地形数据集合的统称,是具有空间位置特征和地形属性特征的数字描述。
短句来源
  相似匹配句对
     Landform Architecure
     地形建筑
短句来源
     Overview of Terrain Visualization
     地形可视化
短句来源
     attribute set;
     属性集;
短句来源
     Study on the 3D Visualization and Relevant Attribution Inquiry of Road and Terrain
     道路地形动态三维可视化及相关属性查询研究
短句来源
     Study on Road and Terrain 3D Visualization and Relevant Attribution Inquiry
     道路地形动态三维可视化及相关属性查询研究
短句来源
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  terrain properties
It is shown that the ratio of standard deviation of lateral velocity to the friction velocity, ΣΝ/u*, and therefore wind direction fluctuations, are sensitive to mesoscale terrain properties.
      
The model built allows defining safe trajectories depending on the robot's capabilities and the terrain properties and will represent, in a topo-geometric way, the environment as local and global maps.
      
Intuitively the yield clusters are due to soil and terrain properties, but there is no clear criterion for the delineation.
      
Even comprehensive representations only account for the obstacle presence and disregard the terrain properties.
      
It should be noted that the coefficients of the impulse response function characterize the terrain properties for a given location.
      
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A method investigating the geological background of debris flows along the famous disaster making rivers--the Dong River and the Xi River in Southwest China is briefly introduced in this paper. Based on the elevation data collected from the remote sensing images directly by means of a analytical plotter, a digital terrain model is established first, then followed by visual interpretation quantitatively and semi--quantitatively of a series of factors of geological environment, such as the characteristics of river--system,...

A method investigating the geological background of debris flows along the famous disaster making rivers--the Dong River and the Xi River in Southwest China is briefly introduced in this paper. Based on the elevation data collected from the remote sensing images directly by means of a analytical plotter, a digital terrain model is established first, then followed by visual interpretation quantitatively and semi--quantitatively of a series of factors of geological environment, such as the characteristics of river--system, the drainage geomorphology,.the strata and rocks characters, the vegetation coverage and the disastreous geological phenomena etc.. Some useful conclusions and suggestions are made in this paper.

本文以西昌地区泥石流调查为例,介绍了利用解析测图仪直接从航片上采集高程数据,建立数 字高程模型(DEM)和提取有关地形属性数据的方法,并结合目视解译与泥石流形成有关的非地 形环境因子,通过计算、自动复合成图和统计运算,获得了大量西昌地区泥石流形成环境的定量、 半定量数据。并通过综合分析,对西昌地区泥石流形成环境有了比较确切的了解,为制定泥石流 治理规划提供了十分有用的资料。

A new deployment strategy is proposed under constraints of imprecise detections and terrain properties. Given the coverage of the sensor field which is divided into many grids,the proposed theory can be aimed at optimizing the number of sensors and determine their placements.Simulations in some representative situations are given in order to prove the proposed algorithm is better than the random placement.At the same time the placement of the sensors is also given.

提出一种在不精确探测和地形属性限制下的传感器部署方法,对给定的划分为栅格点的传感器区域,该方法不仅能求出所需的最小的传感器数目,还能给出这些传感器的位置。用一些典型情景下的仿真证明:提出的这种方法比随机部署有效地减少了使用传感器的数量,并给出了这些传感器的位置。

The spatial patterns of soil organic carbon (SOC) are closely related to changes in the global climate. In order to quantify spatial patterns of SOC density in Hebei Province, China, three different methods, i.e. multiple linear regression (MLR), universal kriging (UK) and regression-kriging (RK), coupled with auxiliary topographic factors extracted from a 1∶ 250 000 DEM (cell size is 100 m) were applied to predict spatial patterns of SOC density for Hebei Province. The results show that the sum squared error...

The spatial patterns of soil organic carbon (SOC) are closely related to changes in the global climate. In order to quantify spatial patterns of SOC density in Hebei Province, China, three different methods, i.e. multiple linear regression (MLR), universal kriging (UK) and regression-kriging (RK), coupled with auxiliary topographic factors extracted from a 1∶ 250 000 DEM (cell size is 100 m) were applied to predict spatial patterns of SOC density for Hebei Province. The results show that the sum squared error (SSE) of the MLR method was quite large with only 18.6% of the total variation explainable, the UK method lowered SSE but widened the range of SOC density as compared with the MLR method. However, it can explain 53% of the total variation and detect local variation of lower SOC density in southeast of Hebei Province. When the RK method was applied, the SSE decreased significantly. It not only explained 65% of the total variation, but also better reflected the relationship between SOC density and landform and local variation, indicating that it is the best one for predicting spatial patterns of SOC density.

运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布。多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%。而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异。三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差。

 
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