DISARMING NATURE: CONVERTING MILITARY LANDS TO WILDLIFE REFUGES*

Since 1988 the United States has closed nearly two dozen major military installations and reclassified them as national wildlife refuges. By presenting a case study of one site of military‐to‐wildlife conversion, this article examines the formation of these places and the implications of casting military practices and environmental conservation as compatible activities. As lands where military and environmental attributes can be perceived as inseparable, military‐to‐wildlife sites exemplify hybrid geographies that challenge dualistic notions of nature and society.     

GIS基础软件技术体系发展及展望

地理信息系统作为IT系统的重要组成部分,其技术的每一次进步都与最新IT技术的兴起息息相关。随着云计算、大数据、人工智能等技术的发展与应用,如今GIS基础软件已经形成五大技术体系：大数据GIS技术体系,增加了对空间大数据的存储管理、分析处理以及可视化的能力,丰富了空间数据的内涵;人工智能GIS技术体系,GIS通过结合人工智能相关算法,增强了GIS模型的分析预测能力,同时二者之间相互赋能,在增强GIS能力的同时,也让人工智能具备空间分析和可视化能力,拓展了其应用范围;新一代三维GIS技术体系,实现了二三维GIS一体化和多源异构数据的融合,推动了三维GIS从室外走向室内,从宏观走向微观;分布式GIS技术体系,突破了数据类型和容量的限制,数量级提升了GIS软件的性能,让高可用和高可信GIS应用成为可能;跨平台GIS技术体系,使得GIS软件可运行于不同类型的CPU架构和操作系统,满足日益丰富的多终端应用需求。五大技术相辅相成,进一步拓展了GIS基础软件的能力和应用场景。本文以SuperMap GIS为例,详细介绍了GIS五大技术体系的具体内容,阐述了每项技术的难点与创新点,并用光环曲线对五大技术体系的发展阶段进行了划分,探讨了未来GIS技术的发展趋势。     

子午环度盘对径改正测试的研究

评述了各种度盘对径改正的方法；用玫瑰系列法对DCMT进行N=180的抽样实验测试，估计DCMT度盘常规读数对径误差为±0．25"。通过对度盘所有对径测试（N=5400）的精度统计特性的分析，得出49°30'、46°40'、43°12'是最优化的玫瑰系列测试组合；此外还论证了当对精度的统计特性的随机性要求不很严格时，在DCMT上实施小玫瑰角系列是可行的，其最优化组合是0°10'、46°40'、50°24'角系列。     

基于大数据的强震临震前地震背景噪声异常分析——以2013年芦山7.0级地震为例

目前针对强震临震前震动信号的拾取和分析基本都是在有限的频带内开展,且分析的数据量时空范围有限。本文提出一种基于大数据的地震背景噪声计算和分析方法,对海量地震观测数据进行分布式噪声功率谱计算,数据采用2013年3—4月的四川地震台网记录的连续波形数据。通过对2013年四川芦山7.0级地震前四川地震台网记录的50Hz～200s范围内的多个频点时序变化进行分析,发现芦山地震距震中50km范围内的MDS台和BAX台在震前水平方向30～150s周期上噪声出现增强10～20dB且持续3～5天的情况,垂直向未发现明显变化,同时超出50km范围内的台站也未有明显变化。这种长周期异常变化很难通过传统的地震动信号拾取来发现,表明本文提出的强震临震异常分析方法可以用于发现和分析震前的长周期异常信号。     

Connected Geomatics in the big data era

The development of global informatization and its integration with industrialization symbolizes that human society has entered into the big data era. This article covers seven new characteristics of Geomatics (i.e. ubiquitous sensor, multi-dimensional dynamics, integration via networking, full automation in real time, from sensing to recognition, crowdsourcing and volunteered geographic information, and service-oriented science), and puts forward the corresponding critical technical challenges in the construction of integrated space–air–ground geospatial networks. Through the discussions outlined in this paper, we propose a new development stage of Geomatics entitled ‘Connected Geomatics,’ which is defined as a multi-disciplinary science and technology that uses systematic approaches and integrates methods of spatio-temporal data acquisition, information extraction, network management, knowledge discovery, and spatial sensing and recognition, as well as intelligent location-based services pertaining to any physical objects and human activities on the earth. It is envisioned that the advancement of Geomatics will make a great contribution to human sustainable development.     

A general-purpose framework for parallel processing of large-scale LiDAR data

ABSTRACT

Light detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle ‘big’ LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework.     

地球化学场及其在隐伏矿体三维预测中的作用

本文从物理学"场论"的角度介绍了地球化学场的概念,并从场源、场作用及地球化学指标分布三个基本要素出发阐述了地球化学场的扩散、对流-扩散的动力学机制。首先,按照采样介质,将地球化学场分为原生的岩石地球化学场,以及次生的土壤、水系沉积物、水文和气体地球化学场,描述了组成各类地球化学场的要素。其次,将地球化学场的分析方法概括为静态的空间结构分析和动态的时空结构分析两种,提出要以场的动力学机制为基础,利用地球物理学中的正、反演理论来研究四维时空中地球化学场的发展和演化。最后,探讨了地球化学场时空结构分析与三维地学模拟两者之间彼此补充和相互验证的关系,三维地学模拟构建的场源及空间介质模型,为地球化学场的正、反演提供了初始条件,地球化学场反演的结果又可用来修正三维地质模型;探讨了地球化学场与大数据分析技术间的关系,即采用大数据的"数据驱动"的思路来挖掘其与多元地学数据之间的隐性联系,探索其与成矿过程的关联性。地球化学场与三维地学模拟、大数据分析技术的结合将为隐伏矿体三维预测中地下成矿物质的分布和演化提供依据。     

大数据环境下卫星对地观测数据集成系统的关键技术

建立卫星对地观测数据集成系统是遥感卫星数据信息资源有效管理与应用的重要手段。从我国对地观测重大需求以及前沿科学问题入手,提出大数据环境下卫星对地观测数据集成系统建立中亟待解决的关键技术,包括大容量异构对地观测数据集成的语义技术、基于网格的遥感图像快速处理技术、遥感大数据深度分析技术、多数据中心协同处理及云平台技术,为实现集成卫星图像、地面观测数据和模拟模型的元数据管理、几何精度纠正和卫星数据质量评价、海量卫星图像数据的空间分析与知识发现、分布式高性能卫星图像数据管理和归档等基本功能,为解决海量卫星数据分布式存储与计算、数据集成与互操作、空间数据分析与地学知识发现提供新思路、新技术与新方法。     

Research challenges and opportunities in mapping social media and Big Data

Social media and Big Data have transformed our world into interconnected cyberspace and realspace. Cartographers can now trace, monitor, and map the spread of social movements, disease outbreaks, nature hazards, and popular events by digitally collecting social media and Big Data with locational contents, such as global positioning system tags and user location profiles. The dynamic characteristics of social media and Big Data provide a great research opportunity for cartographers to map and analyze human behaviors, communications, and movements. However, there are many challenges and pitfalls in cartographic research associated with spatiotemporal analysis of social media contents and Big Data. This short paper will address important research challenges and major opportunities for cartographers to process and visualize Big Data and social media.     

Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS

New sources of geolocated information, associated with big data and social networks, show great promise for geographical research, especially in the field of tourism geography. Photo-sharing services comprise one of these sources. The aim of this article is to demonstrate the potential of photo-sharing services for identifying and analyzing the main tourist attractions in eight major European cities: Athens, Barcelona, Berlin, London, Madrid, Paris, Rome and Rotterdam. Geotagged photographs on Panoramio were differentiated according to whether they had been taken by tourists or local residents, and their spatial distribution patterns were analyzed using spatial statistical techniques in a GIS. The results indicated the concentration and dispersion of photographs in each city and their main hot spots, and revealed marked differences between tourists' and residents' photographs, since the former showed higher spatial concentrations. In addition, differences were observed between cities; Barcelona and Rome presented a strong spatial concentration compared with London or Paris, which showed much greater dispersion.