Towards a knowledge base to support global change policy goals

ABSTRACT

In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.     

中国对全球地球观测系统的贡献

进一步认识地球、关注地球发展规律, 保护人类共同家园已成为世界各国政府的共识。共同发展地球观测技术, 提高对地观测能力成为新世纪世界各国的共同要求。2003年发起, 2005年由欧盟组织的地球观测部长级峰会上通过了全球综合地球观测系统(Global Earth Observation System of Systems, GEOSS)十年执行计划, 构成了世界范围内地球观测领域国际科技合作的主流。中国地球观测领域呈现出快速发展的趋势, 并提出了该领域的全球性发展战略, 预示着中国将在国际地球观测领域发挥越来越重要的作用。文章介绍了中国地球观测领域发展现状和趋势, 在分析中国参与全球地球观测领域国际合作现状及目前存在问题的基础上, 提出进一步促进中国参与该领域国际合作, 为中国乃至国际社会发展做出重要贡献的建议。     

A RESTful proxy and data model for linked sensor data

Abstract

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.     

Bringing GEOSS Services into Practice: A Capacity Building Resource on Spatial Data Infrastructures (SDI)

Data discoverability, accessibility, and integration are frequent barriers for scientists and a major obstacle for favorable results on environmental research. To tackle this issue, the Group on Earth Observations (GEO) is leading the development of the Global Earth Observation System of Systems (GEOSS), a voluntary effort that connects Earth Observation resources world‐wide, acting as a gateway between producers and users of environmental data. GEO recognizes the importance of capacity building and education to reach large adoption, acceptance and commitment on data sharing principles to increase the capacity to access and use Earth Observations data. This article presents “Bringing GEOSS services into practice” (BGSIP), an integrated set of teaching material and software to facilitate the publication and use of environmental data through standardized discovery, view, download, and processing services, further facilitating the registration of data into GEOSS. So far, 520 participants in 10 countries have been trained using this material, leading to numerous Spatial Data Infrastructure implementations and 1,000 tutorial downloads. This workshop lowers the entry barriers for both data providers and users, facilitates the development of technical skills, and empowers people.     

Progress and challenges in the architecture and service pattern of Earth Observation Sensor Web for Digital Earth

The Earth Observation (EO) Web is the data acquisition and processing network for digital Earth. The EO Web including Data Web and Sensor Web has become one of the most important aspects of the Digital Earth 2020. This paper summarised the history of the development and status quo of the major types of EO data web service systems, including architecture, service pattern and standards. The concepts, development and implementation of the EO Sensor Web were reviewed. Furthermore, we analysed the requirements on the architecture of the next-generation EO Sensor Web system, namely Spaceborne-Airborne-Ground integrated Intelligent EO Sensor Web system, and highlighted the virtualization, intelligent, pervasive and active development tendency of such system.     

Metadata Harvesting and Registration in a Geospatial Sensor Web Registry

A Sensor Web registry acts as a broker in a service‐oriented environment to publish and discover Sensor Web resources (e.g. sensors, sensor services, observations, and alerts). The Catalogue Service for the Web (CSW) developed by the Open Geospatial Consortium (OGC) defines a standard interface and protocol for publishing and discovering geospatial resources. This article adopts the CSW for the development of a Sensor Web registry. Metadata for Sensor Web resources are registered into the catalogue information model – the ebXML Registry Information Model (ebRIM). In the Sensor Web environment, sensor observations can be available in real‐time or near‐real‐time, and thus metadata registered in CSW need to be updated frequently. This article proposes an incremental harvesting approach, which can harvest the updated metadata efficiently. A prototypical implementation is provided to demonstrate the applicability of the approach.     

Coupling sensor observation services and web processing services for online geoprocessing in water dam monitoring

ABSTRACT

Novel sensor technologies are rapidly emerging. They enable a monitoring and modelling of our environment in a level of detail that was not possible a few years ago. However, while the raw data produced by these sensors are useful to get a first overview, it usually needs to be post-processed and integrated with other data or models in different applications. In this paper, we present an approach for integrating several geoprocessing components in the TaMIS water dam monitoring system developed with the Wupperverband, a regional waterbody authority in Germany. The approach relies upon the OGC Web Processing Service and is tightly coupled with Sensor Observation Service instances running at the Wupperverband. Besides implementing the standardized XML-based interface, lightweight REST APIs have been developed to ease the integration with thin Web clients and other Web-based components. Using this standards-based approach, new processing facilities can be easily integrated and coupled with different observation data sources.     

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

ABSTRACT

Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user.     

Development of GIS-based environmental information system: an Indian scenario

Abstract

Ideally, scientists should be able to format, explore, analyse, and visualise data in a simple, powerful and fast application that would seamlessly integrate georeferenced data from a variety of data sources into an intuitive visualisation. The focus of an Environmental Information System is providing environmental information to decision makers, policy planners, scientists and engineers, research workers, etc. which ensures integration of data collection, collation, storage, retrieval and dissemination to all concerned. All such queries should be responded to supplying substantive information in the form of reports. The paper presents an innovative way to utilise the geographic information associated with the environmental data. The stand-alone application is the integration of using ArcObjects Environmental System Research Institute ArcGIS Engine 9.1 and VB.Net. The geographic information system (GIS)-based application, a framework of digital earth in terms of environmental information system provides a user-friendly query interface, which gives information about various environmental parameters in tabular as well as on map display. It also provides the visual interpretation to make further analysis and future decisions at multiple scales, locations and extents. The facility for modifying the map attributes and corresponding databases is integrated to update the information system. Output spatial data are produced in the form of reports using selected fields with display on map.     

An asynchronous Geoprocessing Workflow and its application to an Antarctic ozone monitoring and mapping service

The integration of Sensor Web Enablement services with other Open Geospatial Consortium (OGC) Web Services as Geospatial Processing Workflows (GPW) is essential for future Sensor Web application scenarios. With the help of GPW technology, distributed and heterogeneous OGC Web Services can be organized and integrated as compound Web Service applications that can direct complicated earth observation tasks. Under the Sensor Web environment, asynchronous communications between Sensor Web Services are common. We have proposed an asynchronous GPW architecture for the integration of Sensor Web Services into a Web Service Business Process Execution Language workflow technology. We designed a Sensor Information Accessing and Processing workflow, an asynchronous GPW instance, to take an experiment of observing and mapping ozone over Antarctica. Based on our results, our proposed asynchronous workflow method shows the advantages of taking environmental monitoring and mapping tasks.     

Addressing administrative units in international tsunami early warning systems: shortcomings in international geocode standards

Abstract

Administrative units reflect the territorial hierarchies established within all countries of the world. The units are addressable with geocodes that provide a bijective mapping between territories and unique identification codes. Early warning systems for natural or man-made hazards often map affected or threatened areas to administrative units to establish a spatial reference that is comprehensible to all parts of the population. Addressing these territories in an international context has several requirements, such as worldwide coverage, completeness and topicality, which must be met by geocode standards. In this paper, the practicability and suitability of international geocode standards are examined in the context of the requirements of large-scale early warning systems. This paper exposes the insufficiencies and limitations of existing geocode standards International Organization for Standardization (ISO)-3166, Second Administrative Level Boundaries data set project (SALB) and Nomenclature of the Territorial Units for Statistics (NUTS) and emphasises the suitability of the non-official hierarchical administrative subdivision codes (HASC). The analysis is framed in the context of addressing affected areas for an Indian Ocean tsunami early warning system. This system was developed within the Distant Early Warning Systems project according to the requirements of the United Nations Educational, Scientific and Cultural Organization (UNESCO) Intergovernmental Oceanic Commission for Regional Tsunami Watch Providers (RTWPs).     

Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation

Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Landmark Assessment Report highlighted that human activities are considerably degrading land and threating the well-being of approximately 3.2 billion people.In order to reduce and ideally reverse this prevailing situation, national capacities should be strengthened to enable effective assessments and mapping of their degraded lands as recommended by the United Nations Sustainable Development Goals (SDGs). The indicator 15.3.1 (“proportion of land that is degraded over total land area”) requires regular data production by countries to inform and assess it through space and time. Earth Observations (EO) can play an important role both for generating the indicator in countries where it is missing, as well complementing or enhancing national official data sources.In response to this issue, this paper presents an innovative, scalable and flexible approach to monitor land degradation at various scales (e.g., national, regional, global) using various components of the Global Earth Observation System of Systems (GEOSS) platform to leverage EO resources for informing SDG 15.3.1. The proposed approach follows the Data-Information-Knowledge pattern using the Trends.Earth model (http://trends.earth) and various data sources to generate the indicator. It also implements additional components for model execution and orchestration, knowledge management, and visualization.The proposed approach has been successfully applied at global, regional and national scales and advances the vision of (1) establishing data analytics platforms that can potentially support countries to discover, access and use the necessary datasets to assess land degradation; and (2) developing new capacities to effectively and efficiently use EO-based resources.     

A Task Ontology Driven Approach for Live Geoprocessing in a Service‐Oriented Environment

In a service‐oriented environment, Web geoprocessing services can provide geoprocessing functions for a variety of applications including Sensor Web. Connecting Sensor Web and geoprocessing services together shows great potentail to support live geoprocessing using real‐time data inputs. This article proposes a task ontology driven approach to live geoprocessing. The task in the ontology contains five aspects: task type, task priority, task constraints, task model, and task process. The use of the task ontology in driving live geoprocessing includes the following steps: (1) Task model generation, which generates a concrete process model to fulfill user demands; (2) Process model instantiation, which transforms the process model into an executable workflow; (3) Workflow execution: the workflow engine executes the workflow to generate value‐added data products using Sensor Web data as inputs. The approach not only helps create semantically correct connections between Sensor Web and Web geoprocessing services, but also provides sharable problem solving knowledge using process models. A prototype system, which leverages Web 2.0, Sensor Web, Semantic Web, and geoprocessing services, is developed to demonstrate the applicability of the approach.     

Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass

ABSTRACT

Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses.     

Colour – Difficult to Both Choose and Use in Practice

Abstract

Colour is a very important visual variable and is often used for the communication of information in maps, graphics and diagrams. In cartography, the use of colour, in both a traditional and an electronic medium, has been widely discussed. With the growing use of a range of computer software (cartographic, remote sensing, Geographical Information Systems (GIS), and more recently the Internet or World Wide Web) as the main environment in which to design and create maps, and the availability of more sophisticated hardware, there is now an even greater opportunity and freedom of choice to make use of colour as a vital component in the communication of spatial information. The question is how to handle all these colours. This paper explores the importance of both the choice and use of colour in a paper and an electronic medium, and examines some of the ways in which more careful choice of colour can be encouraged in practice to ensure that the growing freedom of use considers the correct use of colour.     

Big Earth data: disruptive changes in Earth observation data management and analysis?

ABSTRACT

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.     

Big Data and cloud computing: innovation opportunities and challenges

ABSTRACT

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.     

Engaging indigenous people as geo-crowdsourcing sensors for ecotourism mapping via mobile data collection: a case study of the Royal Belum State Park

ABSTRACT

Web 2.0 and the proliferation of built-in Global Positioning System (GPS) on smartphones have influenced the increase of geo-crowdsourcing activities in a number of different contexts. The aim of this paper is to evaluate the performance of indigenous people’s use of mobile collection applications that are embedded in a smartphone to facilitate ecotourism asset mapping. In order to achieve this, field usability testing was conducted where structured observational method was used to assess the performance. The findings indicate majority of them can complete the data entry tasks using mobile data collection. The performance of data entries using radio button, icons, camera and audio methods were identified as better than free text and drop-down list methods. There was a correlation between the level of education with the ability of using radio button, drop-down list and image icon as data entry methods. The paper also discusses the extent of local knowledge relating to ecotourism within the community. The findings should be useful in the understanding of the design of mobile geo-crowdsourcing tools for use within other contexts that focus on data collection by semiliterate and indigenous groups.     

Enabling Digital Earth simulation models using cloud computing or grid computing – two approaches supporting high-performance GIS simulation frameworks

Abstract

Geospatial simulation models can help us understand the dynamic aspects of Digital Earth. To implement high-performance simulation models for complex geospatial problems, grid computing and cloud computing are two promising computational frameworks. This research compares the benefits and drawbacks of both in Web-based frameworks by testing a parallel Geographic Information System (GIS) simulation model (Schelling's residential segregation model). The parallel GIS simulation model was tested on XSEDE (a representative grid computing platform) and Amazon EC2 (a representative cloud computing platform). The test results demonstrate that cloud computing platforms can provide almost the same parallel computing capability as high-end grid computing frameworks. However, cloud computing resources are more accessible to individual scientists, easier to request and set up, and have more scalable software architecture for on-demand and dedicated Web services. These advantages may attract more geospatial scientists to utilize cloud computing for the development of Digital Earth simulation models in the future.