PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.     

A variable precision rough set approach to knowledge discovery in land cover classification

This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision methods. In particular, it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised classification. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set theory. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary regions. The paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover characterization. The rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms. A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network, naïve Bayesian and support vector machine methods.     

Re-conceptualizing the Anthropocene: A call for collaboration

Since it was first proposed in 2000, the concept of the Anthropocene has evolved in breadth and diversely. The concept encapsulates the new and unprecedented planetary-scale changes resulting from societal transformations and has brought to the fore the social drivers of global change. The concept has revealed tensions between generalized interpretations of humanity’s contribution to global change, and interpretations that are historically, politically and culturally situated. It motivates deep ethical questions about the politics and economics of global change, including diverse interpretations of past causes and future possibilities. As such, more than other concepts, the Anthropocene concept has brought front-and-center epistemological divides between and within the natural and social sciences, and the humanities. It has also brought new opportunities for collaboration. Here we explore the potential and challenges of the concept to encourage integrative understandings of global change and sustainability. Based on bibliometric analysis and literature review, we discuss the now wide acceptance of the term, its interpretive flexibility, the emerging narratives as well as the debates the concept has inspired. We argue that without truly collaborative and integrative research, many of the critical exchanges around the concept are likely to perpetuate fragmented research agendas and to reinforce disciplinary boundaries. This means appreciating the strengths and limitations of different knowledge domains, approaches and perspectives, with the concept of the Anthropocene serving as a bridge, which we encourage researchers and others to cross. This calls for institutional arrangements that facilitate collaborative research, training, and action, yet also depends on more robust and sustained funding for such activities. To illustrate, we briefly discuss three overarching global change problems where novel types of collaborative research could make a difference: (1) Emergent properties of socioecological systems; (2) Urbanization and resource nexus; and (3) Systemic risks and tipping points. Creative tensions around the Anthropocene concept can help the research community to move toward new conceptual syntheses and integrative action-oriented approaches that are needed to producing useful knowledge commensurable with the challenges of global change and sustainability.     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。     

运用社交网络分析淹水空间信息之关联性

随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。     

地球观测数据共享的发展和趋势

地球观测数据共享是地球科学和相关学科科研活动中非常重要的基础性工作,是对地观测信息生命周期中的重要环节。受到由资源提供者、资源消费者和资源加工者组成的社会生态系统发展变化的影响,共享模式经历了无共享、项目共享、部门共享、社会共享等渐进的4个发展阶段,并呈现出区域差异和阶段差异。地球观测数据共享的概念体系包含数据开放、数据共享、数据互联等不同层次的问题,并受到信息技术等使能技术的驱动。其中开放性代表数据在网络中可被访问的状态,共享性是对于数据重复使用的授权和模式,互联性则是强调可共享数据资源在科学含义上的相互理解。而地球观测数据共享的技术体系则包含数据开放技术、数据共享技术和数据出版与引用技术。目前地球观测领域的数据共享正在经历巨大的文化、政策、技术和应用变革,下一代的地球观测数据设施集中体现了数据的共享和协作,并将呈现国际化、多学科化、标准化、设施化、大数据化和公众社会化等新的技术特征,将对相关科学活动产生重大影响。     

SoFISHticated policy – social perspectives on the fish conflict in the Northeast Atlantic

Ecosystem changes currently question the traditional allocation of fishing rights and quotas in the fishery of Northeast Atlantic mackerel and Norwegian spring-spawning herring in the Northeast Atlantic. Variability in the distribution of these highly migratory species escalated in a political conflict between member states of the European Union, Iceland, the Faroe Islands and Norway, which is a driving force for unsustainable fishery. The aim of this paper is to investigate this conflict by outlining the social understandings of diverse stakeholders by using the Q methodology. The method reduced the complexity of numerous opinions, detected four distinct perspectives and simultaneously categorised the participating stakeholders. Although the perspectives differ in various elements, the protection of economic interests seems to dominate over the quest for sustainability. The call of all stakeholders in this study to clarify the fishing rights in the Northeast Atlantic reveals a clear deficiency of the current international fishery management in handling abrupt ecological changes and the necessity to acknowledge this as a complex adaptive system.     

Engaging in the boycott,divestment, and sanctions (bds) debate

The goal of this forum is to garner attention to the issues surrounding the Boycott, Divestment, and Sanctions (BDS) campaign against Israel, to help a broad audience better understand the complexity of BDS, and to engage geographers in the BDS debate. Including the editorial introduction, seven different experts on the issues of Palestine/ Israel have written six essays on BDS for this forum. The six essays represent a wide range of opinions while also echoing some similar themes.     

基于神经网络的页岩微纳米孔隙微结构分析的正则化和最优化方法

页岩气成藏机理与页岩内部孔隙结构紧密相关,对页岩孔隙结构的研究成为页岩气勘探开发技术中至关重要的一环。页岩内部不同结构体组分对X-射线的吸收能谱不一样,这样就导致观测数据是由不同页岩组分衰减不同波段的X-射线构成的。经过对CT图像分割,能够获得页岩微孔结构的图像,尤其是获得有机质中孔隙类别、形状、尺寸、空间分布、连通特性。本文利用同步辐射X射线扫描重构的页岩CT数据,研究并设计基于多能CT图像的神经网络图像分割技术和算法,以期得到页岩体三维结构特征及空间分布,可以为建立有机质种类和无机矿物组成与微纳孔隙特征的联系以及最终实现页岩气的资源储量评估和勘探开发提供技术支持。