武汉城市圈城乡关联度分析

城乡关系是以流为载体的社会大系统中的两个子系统之间的相互关系,反映了城市与乡村区域的动态演变过程,是影响区域发展的重要因子;城乡关联是城市与乡村地域相互作用的表现形式,对区域发展具有制约性影响,探讨城乡关联度对促进区域一体化与区域协调发展具有重要战略意义。在现状分析的基础上,用主成分分析法分析了武汉城市圈城乡关联度,从产业转移与产业链构建、现代农业发展、区域公共服务体系、基础设施一体化等方面提出了促进武汉城市圈城乡关联与协调发展的主要对策。     

多相位水平集高分辨率遥感影像非监督分类

高空间分辨率遥感影像(特别是城区场景)常表现出高的光谱异质性、地物细节丰富且地物拓扑关系复杂等特点,这对影像分类产生较大干扰,导致分类精度不高。针对以上情况提出一种新的非监督多相位水平集分类方法:该方法以基于区域竞争思想及Bayes准则的多相位水平集分类框架作为高分辨率遥感影像分类的基础,通过采用Parzen窗非参数密度估计方法来改善复杂场景下样本概率密度估计的准确性,从而增加分类模型的抗干扰能力;此外,为提高多水平集模型演化效率,在模型求解中提出一种简单而有效的水平集函数重初始化方案。通过与前人工作的试验对比及分析,验证了提出的模型在复杂场景及存在上述干扰的情况下表现出好的分类效果。     

基于人口集聚度的中国人口集疏格局

人口集聚和疏散是人口空间分布格局最直观和最集中的体现,研究一个地区人口集疏的空间格局,不仅可以揭不该地区人口集疏的内在规律,也有利于深入研究该地区的整体人口分布的态势和规律,更能够体现出区域内经济格局以及城市化格局的发展.基于人口集聚度的中国人口集疏空间格局的研究,有助于准确把握中国人口空间分布的基本脉络,具有一定的学术价值和实践意义.本研究采用了人口集聚度分级评价的方法,依据人口集聚度的不同,将各个地区划分为不同等级的人口集聚区,并对其分别讨论.在此基础上,结合中国人口分布格局、自然条件空间格局、人居环境自然适宜性评价结果以及经济发展格局和城市化格局,对中国的人口集疏的空间格局进行了归纳,得出了中国人口分布"西疏东密"的传统空间格局突出,人口集聚以平原地区为依托并呈现"沿海、沿江、沿线"高度集聚的特征的结论.     

福州市农业土壤多环芳烃的含量、来源及生态风险

研究了福州市农业表层土壤（0-10cm）。中美国环境保护署（USEPA）优控的16种多环芳烃（PAHs）含量,并对其来源和生态风险进行了分析。结果表明,供试土样中16种PAHs检出率达到100％,其总量的含量范围为100.2-1215.1μgkg^-1,且与土壤总有机碳的含量呈显著正相关;土壤中PAHs主要源于生物燃烧和石油。利用毒性当量因子（TEF）计算了供试土样中PAHs单体相对于苯并[a]芘的毒性当量（Bapeq）,土样中总Bap。值的范围为12.50-147.95μgkg^-1,其中50％土样总Bapeq的值超过荷兰规定目标值（总Bapeq=32.96μgkg^-1）,表明福州市部分农业土壤存在一定的潜在生态风险。     

Data assimilation in a system with two scales—combining two initialization techniques

An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observations.     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.     

数字地质填图研究现状与发展趋势

数字地质填图是指区域地质调查数据的野外获取及其成果的数字化统一性再现.当前数字地质填图现状和发展趋势已基本上实现了数字填图系统的采集、存储、管理、描述、分析和再现地质实体在地球表面空间分布有关的数据信息.在计算机辅助下, 通过野外观测路线的调查, 对地质、地理、地球物理、地球化学和遥感等多源地学数据进行综合分析和地质制图.在掌上机WindowsCE平台上, 实现了数字填图所需的基本GIS基本功能(GPS定位路线采集、素描), 实现了遥感系统与数字填图系统的一体化整合.      

Estimating overland flow erosion capacity using unit stream power

Soil erosion caused by water flow is a complex problem. Both empirical and physically based approaches were used for the estimation of surface erosion rates. Their applications are mainly limited to experimental areas or laboratory studies. The maximum sediment concentration overland flow can carry is not considered in most of the existing surface erosion models. The lack of erosion capacity limitation may cause over estimations of sediment concentration. A correlation analysis is used in this study to determine significant factors that impact surface erosion capacity. The result shows that the unit stream power is the most dominant factor for overland flow erosion which is consistent with experimental data. A bounded regression formula is used to reflect the limits that sediment concentration cannot be less than zero nor greater than a maximum value. The coefficients used in the model are calibrated using published laboratory data. The computed results agree with laboratory data very well. A one dimensional overland flow diffusive wave model is used in conjunction with the developed soil erosion equation to simulate field experimental results. This study concludes that the non-linear regression method using unit stream power as the dominant factor performs well for estimating overland flow erosion capacity.     

胶州--莱阳盆地白垩纪莱阳群中的震积岩及构造意义讨论

通过对胶州-莱阳盆地沉积层的野外考察和岩心观察,系统分析了莱阳群的沉积特征及其变化规律,鉴别出一些较典型的震积岩类型;较详细描述了其中的粉砂质液化脉、震裂液化角砾岩、震褶岩和砂枕构造等地震液化特征。最后从盆地的区域地质背景出发,初步认为白垩纪早期胶州-莱阳盆地的形成与其南部苏鲁造山带的后期隆升同时,造成研究区总体处于伸展-裂解的构造环境之中。强烈的同沉积构造活动伴随频繁的地震发生,致使盆地内早期沉积的莱阳群以普遍发育震积岩为特征。     

Characteristics of Late Permian Deep-Water Sedimentary Environments： A Case Study of Shaiwa Section, Ziyun County, Guizhou Province, Southwestern China

INTRODUCTIONThe Shaiwa Section is located in the town ofSidazhai, Ziyun County, Guizhou Province, south western China ( Fig. 1 ). This area belongs to theNanpanjiang basin of the Yangtze platform. A greatdeal of work purely on the subdivision of the stratahas been carried out in this region (Guizhou Bureauof Geology and Mineral Resources, 1987). However,studies on recoveries of the paleoenvironments of thisarea are rarely reported. Due to rifting and su…