一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。     

长距离单历元单频非差北斗网络差分方法

针对北斗卫星导航系统现有的网络差分方法多是基于GPS系统并采用双差定位模型,而双差定位模型的采用使得网络差分方法存在一些缺陷的问题,该文提出一种长距离单历元单频非差北斗网络差分方法;首先长距离北斗基准站网向覆盖区域内的北斗用户提供非差误差改正数;然后用户内插计算出自己所需的非差误差改正数,并改正北斗单频伪距观测值的误差;最后进行单历元单频网络差分定位。实验表明,该方法能够使用单历元单频伪距观测数据实现长距离北斗网络差分定位。     

港珠澳大桥沉管隧道贯通误差预计

针对港珠澳大桥沉管隧道的安全贯通问题,该文从隧道外GPS网的布设及施测方法出发,通过多期复测数据评价GPS网具有可靠的测量精度;通过对GPS网点的稳定性分析,阐明了沉管隧道地面控制点位无法长期保留、稳定性差的特点;最后就GPS网引起的贯通误差影响值进行了估算。     

A hybrid approach to modeling territorial control in violent armed conflicts

Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time.     

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.     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

Approach to calculating spatial similarity degrees of the same river basin networks on multi-scale maps

Obtaining spatial similarity degrees among the same objects on multi-scale maps is of importance in map generalization. This paper firstly defines the concepts of ‘map scale change’ and ‘spatial similarity degree’; then it proposes a model for calculating the spatial similarity degree between a river basin network at one scale and its generalized version at another scale. After this, it validates the new model and gets 16 points in the model validation process. The x-coordinate and y-coordinate of each point are map scale change and spatial similarity degree, respectively. Last, a formula for calculating spatial similarity degree taking map scale change as the only variable is obtained by the curve fitting method. The formula along with the model can be used to automate the algorithms for simplifying river basin networks.     

四川观音梁子锰矿床地质特征及找矿意义

观音梁子锰矿赋存于寒武系邱家河组中,是龙门山中段在邱家河组中新发现的工业锰矿床。通过矿区勘探工作及室内观察、测试等手段,对矿区成矿地质背景、含锰岩系、矿体及矿石矿物特征、矿床成因、找矿前景等方面进行了初步总结分析。研究发现,观音梁子锰矿为典型的沉积型锰矿床,矿体呈层状产于邱家河组三段硅质白云岩中,延伸较稳定,严格受层位的控制;矿石类型为菱锰矿型,主要由菱锰矿、硫锰矿、水锰矿等组成。早寒武世,龙门山地区由于地壳拉张裂陷形成深海盆地,在还原环境下,锰元素与碳酸根直接结合形成碳酸锰矿物,稳定的物源、深海还原环境为锰矿富集提供了有利的成矿环境。观音梁子锰矿的发现,证实了龙门山北段邱家河组含锰层位的存在,为龙门山中段的锰矿找矿提供了契机,同时显示了寒武系邱家河组具有良好的找锰远景。     

云南富宁县水合口矿化蚀变信息提取和找矿预测

云南富宁水合口金矿位于滇东南文山―富宁成矿带,属于著名的滇黔桂"金三角"地区。利用landsat 8OLI遥感影像对研究区进行1∶100 000矿化蚀变信息提取,结合区域地质和化探异常等多元信息进行找矿预测,表明矿化蚀变、断裂构造交汇处和地球化学异常空间叠加部位为有利找矿地区,圈定了5个找矿远景区,为研究区开展下一步找矿提供了科学依据。     

河南萑香洼金矿地球化学分带模型研究

河南萑香洼金矿床作为典型的构造蚀变岩型金矿床,通过研究分析该矿床F985矿化带的Ⅰ号矿体各类元素组分在不同地质体的含量变化特征,原生晕异常分带特征和地球化学轴向分带特征,建立了该矿床完整的地球化学异常分带理想模型,得出矿床地球化学异常轴向分带序列确定为:烃类、Sb(前缘晕)→As、Hg(矿头)→(矿中晕)Au、Ag、Cu、Pb、Zn、W、Mn→(矿尾晕)Mo、Co、Ni、Sn,并总结出了找矿预测标志,为该矿区深部找矿提供了一定的科学理论依据。