基于MapX和Oracle Spatial建立矿区土地复垦信息系统探讨

煤炭资源的开采破坏了大量的土地,加剧了人地矛盾,土地复垦是矿区可持续发展的重要内容之一.矿区土地复垦是一个边缘交叉学科,所需处理的数据涉及采矿、地质、地理、土壤、农林、生物、社会经济等众多领域,具有数据信息量大、相互关系复杂等特点.传统的数据处理方法已难以适应土地复垦发展的要求.主要研究了矿区土地复垦信息系统的主要功能,并分析了基于MapX和Oracle Spatial进行系统开发的关键技术.     

CWRF对青藏高原气温和降水模拟效果的综合评估

利用CWRF(Climate-Weather Research and Forecasting model)对中国区域气候的31 a多物理集合模拟试验,分析了该模式对青藏高原气温和降水的模拟效果及其对水平分辨率和物理过程参数化方案的敏感性.结果表明:1)CWRF降尺度全面改善了全球模式对高原气温和降水的模拟,使气温年循...     

Testing taxonomic resolution,data transformation and selection of species for monitoring macroalgae communities

The Water Framework and Habitats Directives require the evaluation of both the conservation and ecological status of macroalgae communities at water body or habitat level. However, assessments of macroalgal communities are highly time-consuming, both in terms of sampling effort and laboratory processing. These constraints have brought about their oversight in many marine monitoring programs, especially in subtidal environments. By using data from intertidal and subtidal macroalgae assemblages of Mouro Island (North coast of Spain) we wanted to identify possible cost-effective methods for monitoring this biological indicator, based on both high taxa levels and use of representative taxa. Multivariate analyses were applied using different data transformations. The results show that macroalgal communities are robust to aggregation to genus or even family level. Moreover, the outcomes show that transformation types introduce higher variation in the multivariate pattern of samples than the taxonomic level to which organisms are identified. Also, the study supports the use of representative taxa as a surrogate to overall community structure. Therefore, we conclude that a rapid-assessment by means of field evaluations, based on coverage of representative taxa, is a reliable alternative for the assessment of macroalgae status. In addition this procedure allows evaluation at a broader spatial scale (water body or habitat level) than traditional quantitative sampling procedure does.     

Developing a building policy for the erosion zone: Solutions to some key (Dutch) questions

In those coastal communities where the most seaward strip of mainland consists of dunes, these dunes often serve as a flexible sea defence. In addition, this strip offers large potential for housing and commercial enterprises. Unfortunately, due to severe storm surges part of this strip (the erosion zone) is subject to erosion, and as a result of which any buildings or infrastructure located here, are destroyed. Therefore, as we will illustrate in this paper, a building policy for this zone should reflect a compromise between two opposite interests: exploitation of the existing potential and, prevention of an unacceptable high risk due to erosion. Accordingly, the authors have developed a framework for such a building policy on the basis of which the desirability of various different types of investments and the location within the erosion zone of such investments can be determined. The examples that are used to illustrate this framework in this paper are limited to experiences in The Netherlands as relevant data and experiences are available and relatively easy accessible here. Nevertheless, the approach as is described is generic and applicable worldwide suggesting that the discovered unused potential for exploitation is not just limited to The Netherlands.     

面波频散反演的数值模拟

大量的数值模拟表明,SVD和LSQRD在面波频散网格反演两步法中的应用效果都很好.但SVD可以用分辨矩阵、信息矩阵和协方差矩阵对解估计进行数学上客观有效的评价;而对于大型稀疏方程组的求解,LSQRD确是一种内存需求小、计算速度快以及分辨抗噪能力都较强的算法 .在现有计算机运算速度较快、内存可以扩充较大的条件下,实测数据量不很大时,应采用SVD算法进行线性反演.     

A density-based approach for detecting network-constrained clusters in spatial point events

Existing spatial clustering methods primarily focus on points distributed in planar space. However, occurrence locations and background processes of most human mobility events within cities are constrained by the road network space. Here we describe a density-based clustering approach for objectively detecting clusters in network-constrained point events. First, the network-constrained Delaunay triangulation is constructed to facilitate the measurement of network distances between points. Then, a combination of network kernel density estimation and potential entropy is executed to determine the optimal neighbourhood size. Furthermore, all network-constrained events are tested under a null hypothesis to statistically identify core points with significantly high densities. Finally, spatial clusters can be formed by expanding from the identified core points. Experimental comparisons performed on the origin and destination points of taxis in Beijing demonstrate that the proposed method can ascertain network-constrained clusters precisely and significantly. The resulting time-dependent patterns of clusters will be informative for taxi route selections in the future.     

Two-stage permutation tests for determining homogeneity within a spatial cluster

The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications.     

浅水多波束测深仪高分辨率波束形成方法研究

常规波束形成技术以其稳健性好、计算量低等特点得到广泛应用,但其空间分辨率受阵元个数限制,不能突破瑞利限。因此,常规波束形成技术应用于高精度浅水多波束测深仪时,存在空间分辨率不足和旁瓣干扰等问题。对比研究了最小方差无失真(MVDR)及多重信号分类(MUSIC)波束形成在浅水多波束测深仪中的应用,给出了浅水多波束测深仪的常规波束形成、MVDR和MUSIC处理方法,并结合能量法和相位法两种底检测测深算法,处理了iBeam8120浅水多波束测深仪外场数据,验证了本文方法的性能。     

A comparative analysis of gridding systems for point-based land cover/use analysis

For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria.     

深度学习GoogleNet模型支持下的中分辨率遥感影像自动分类

提出了一种基于深度学习技术的遥感分类方法,它能有效解决中分辨率影像在分类过程中出现的像元混分问题。研究选用2016年5月12日武汉市Landsat 7 ETM+遥感影像,基于GoogleNet模型中的Inception V3网络结构,借助迁移学习方法,构建出遥感分类模型,实现了对武汉市主城区4类典型地物（不透水层、植被、水体和其他用地）的自动分类提取,并将分类结果与传统最大似然分类（ML）结果进行了对比分析。研究表明：基于深度学习方法的遥感影像总体分类精度高达88.33%,Kappa系数为0.834 2,明显优于传统ML方法总体分类精度83%和Kappa系数0.755 0,而且有效抑制了地物在分类过程中出现的像元混分现象。