Nontectonic signals in reprocessed continuous GPS position time series of CMONOC

The nearly nine-year continuous GPS data collected since 1 March 1999 from the Crustal Motion Observation Network of China(CMONOC) were consistently analyzed.Most of the nonlinear movements in the cumulative position time series pro-duced by CMONOC data center disappeared;and more accurate vertical terms and tectonic signals were extracted.Displacements caused by atmospheric pressure loading,nontidal ocean loading,soil moisture mass loading,and snow cover mass loading using the National Centers for Environmental Prediction(NCEP) Reanalysis I/II models and Estimation of the Circulation and Climate of the Ocean(ECCO) data can explain most of the vertical annual terms at many stations,while only parts can be explained at Lhasa and southern coastal sites,indicating that there are some deformation mechanisms that are still unknown or not modeled accurately.The remarkable differences in vertical position time series for short-baseline sites reveal that GPS stations can be greatly affected by lo-cal factors;and attention should be paid when explaining observed GPS velocity vectors.     

城市地下管线信息化建设框架研究

目前,我国各城市相继展开地下管线普查、地下管线信息系统的建设工作,力求建立科学、准确、完整、动态的综合管线信息管理系统,但也都普遍存在一些问题。本文针对管线信息化过程中普遍存在的这些问题,提出站在城市的高度,统筹规划管线信息系统的建设工作。并通过对现代城市发展过程中城市管线信息需求的分析,给出了城市地下管线信息化建设的一种框架。     

差分层析SAR技术研究现状分析

差分层析SAR是在层析SAR基础上发展起来的一种四维信息反演技术。它不仅实现了对雷达目标的三维分辨能力,同时可以获取目标的形变速率信息,可实现对目标方位-距离-高度-时间四维成像,这对实现城市基础设施动态形变监测、古建筑风险评估、重要工程安全监测等应用具有重要实际意义和价值。本文基于差分层析SAR成像原理,分析了成像处理过程中存在的问题,总结了差分层析SAR成像算法研究现状和特点;最后列举了差分层析SAR技术的主要应用领域,并对其技术发展趋势进行了展望。     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

无人机倾斜摄影及自动三维建模技术在地质灾害应急测绘中的应用——以三明市妙元山为例

利用无人机获取多视角的倾斜影像,结合自动三维建模技术,模拟地质灾害受灾情况,可以对受灾程度进行定性、定量分析管理,能够快速准确地评估受灾情况,为救灾工作提供决策支持。     

不动产地籍和产权登记信息系统

介绍了一种涵盖地籍管理过程且在实施过程中简单易行而有效的信息系统,为土地资源的合理开发利用和地籍信息的动态管理了新的途径。     

基于模拟退火算法的自动地图注记配置研究

本文将求解大规模组合优化问题最优解的一种新型算法模拟退火算法应用于地图注记的自动配置。该算法将地图各要素注记的配置作为一个统一的整体来对待,从而避免了一般局部搜索法可能终止于一个局部最优解的缺点,这样可使自动注记配置的结果更趋合理化。用模拟退火算法进行注记自动配置,理论体系完整,机理简单,通用性强,编程容易,而且与一般局部搜索法相比,超常规注记配置出现的数目大大降低,其缺点在于配置速度较慢     

基于高通量测序方法分析荣成月湖潮间带趋磁细菌多样性

荣成月湖是一个典型天然潟湖,潮间带沉积物中存在多种形态趋磁细菌。通过Roche454高通量测序平台,对沉积物样品(B_S)和磁收样品(B_M)进行16SrRNA基因高通量测序,认识荣成潮间带沉积物的细菌群落结构,并了解趋磁细菌多样性及系统进化地位。研究结果表明沉积物样品中主要的细菌类群为δ-变形菌纲,占总细菌数的26.4%,其次是γ-变形菌纲和α-变形菌纲;而磁收样品中细菌多样性和种类明显降低,以α-变形菌纲占绝对优势,相对比例达72.6%。在磁收样品和沉积物样品分别发现了1 612条和186条reads与趋磁细菌相关,分别占细菌总数的5.76%和0.85%,磁收样品中趋磁细菌数是沉积物样品的6.8倍。对两个样品中获得的趋磁细菌序列进行系统进化分析,发现这些序列多数属于变形菌门的α-变形菌纲,以趋磁球菌占绝对优势,少数属于δ-变形菌纲,与多细胞趋磁原核生物亲缘关系最近。海洋趋磁螺菌属仅在B_M样品中检出,趋磁弧菌属在B_M样品优势度高于B_S样品,而多细胞趋磁原核生物和趋磁螺菌属在B_S样品中优势度更高。通过分析样品间的差异OTUs,认为荣成潮间带沉积物中可能存在大量未知的趋磁细菌新类群。研究结果为下一步培养和开发趋磁细菌这一功能菌群,发现趋磁细菌新类群及趋磁细菌生态功能提供了基础资料。     

Pollution trend in the tumen river and its influence on regional development

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Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.