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.     

Earth observations for sustainable development goals monitoring based on essential variables and driver-pressure-state-impact-response indicators

ABSTRACT

In recent years, researchers of different communities have increased their efforts in formalizing a set of measurements regularly collected for analysing changes in Drivers, States, Impacts and Responses of a given discipline. In some cases, different actors have converged in a minimum set of Essential Variables (EVs), such as for Climate, Biodiversity or Oceans. The definition of such EVs is an ongoing evolution and in extension (e.g. EVs for water) although some communities have not even started (e.g. agriculture and energy). This paper characterizes the Earth Observation (EO) networks and creates a graph representation of their relations. Secondly, this graph is enriched with the EVs produced by each network creating a knowledge base. Finally, an effort has been done to identify links between EVs and Sustainable Development Goals (SDG) indicators in a way that they indirectly connect the EO. An analysis to detect gaps in EO variables due to a lack of observational networks is performed. Several suggestions for improving SDG indicators framework by considering EVs are exposed, as well as proposing new necessary EVs and suggesting new EO based indicators. The complete graph is available in the ENEON website (http://www.eneon.net/graph-ev-sdg/).     

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.     

A goal-based approach to the identification of essential transformation variables in support of the implementation of the 2030 agenda for sustainable development

ABSTRACT

The United Nations' 2030 Agenda for Sustainable Development sets seventeen Sustainable Development Goals (SDGs) to be achieved by 2030. Earth observation are needed that can support the development and validation of transformation policies to make progress towards the SDGs. A participatory and inclusive goal-based approach (GBA) is introduced that links societal goals, targets and indicators to Essential Transformation Variables (ETVs) of the human and non-human environment. The GBA is complementary to the widely used expert-based approach. The GBA is applied to the SDGs at the goal, target and indicator levels. The high-level conceptual model used for the SDGs is humanity embedded in the Earth's life-support system (ELSS). At the goal level, very few of the SDGs are directly focusing on the ELSS and its physiology. Most of the SDG Targets focus on transformations in society and the built environment. Having targets that explicitly focus on the physiology of the ELSS would be important for sustainability. Most of the current indicator measure the built environment and the embedded social fabric. Sustainable development requires a functioning ELSS, and to ensure this, complementary indicators that bring environmental aspects to the monitoring of SDG targets are needed.     

Towards integrated essential variables for sustainability

ABSTRACT

Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions. These data allow characterizing any location on Earth, assessing the status of the environment at various scales (e.g. national, regional, global), understanding interactions between different systems (e.g. atmosphere, hydrosphere, biosphere, geosphere), and modeling future changes. The Group on Earth Observations (GEO) was established in 2005 in response to the need for coordinated, comprehensive, and sustained observations related to the state of the Earth. GEO’s global engagement priorities include supporting the UN 2030 Agenda for Sustainable Development, the Paris Agreement on Climate, and the Sendai Framework for Disaster Risk Reduction. A proposition is made for generalizing and integrating the concept of EVs across the Societal Benefit Areas of GEO and across the border between Socio-Economic and Earth systems EVs. The contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are introduced. Finally, the main aim of the 10 papers of the special issue is shortly presented and mapped according to the proposed typology of SBA-related EV classes.     

GEOCAB Portal: A gateway for discovering and accessing capacity building resources in Earth Observation

The discovery of and access to capacity building resources are often essential to conduct environmental projects based on Earth Observation (EO) resources, whether they are Earth Observation products, methodological tools, techniques, organizations that impart training in these techniques or even projects that have shown practical achievements. Recognizing this opportunity and need, the European Commission through two FP7 projects jointly with the Group on Earth Observations (GEO) teamed up with the Committee on Earth observation Satellites (CEOS). The Global Earth Observation CApacity Building (GEOCAB) portal aims at compiling all current capacity building efforts on the use of EO data for societal benefits into an easily updateable and user-friendly portal. GEOCAB offers a faceted search to improve user discovery experience with a fully interactive world map with all inventoried projects and activities. This paper focuses on the conceptual framework used to implement the underlying platform. An ISO19115 metadata model associated with a terminological repository are the core elements that provide a semantic search application and an interoperable discovery service. The organization and the contribution of different user communities to ensure the management and the update of the content of GEOCAB are addressed.     

A digital earth platform for sustainability

ABSTRACT

Based on the experience of the International Society for Digital Earth (ISDE), this paper describes some challenges foreseen in order to develop a Digital Earth platform that can support the implementation of the Sustainable Development Goals. The use of ready-to-use derived geospatial information is essential. Future Earth’s methodology of ‘co-design’ aims to bring together natural, social scientists and decision makers to plan and carry out research for sustainability. Sustainability implies transdisciplinary research, but in order for scientists of different disciplines to work together, they will need to be able to share, access and use common data. This is by far not simple! While the good will to share data might exist, the associated technological, ethical and privacy issues are difficult to solve. An adequate e-infrastructure will be required. ISDE could consider to use the SDGs is the basis to develop the desired Digital Earth platform. This paper, by no means, covers everything for a Digital Earth platform, it aims to trigger research discussions and to have a good view about a starting point.     

“一带一路”区域可持续发展生态环境遥感监测

2013年9月和10月,习近平主席在出访中亚和东南亚国家期间,先后提出了共建"丝绸之路经济带"和"21世纪海上丝绸之路"(简称"一带一路")的重大倡议。要全面保护"一带一路"区域生态环境,实现2030年可持续发展目标,是一个具有挑战性的问题。遥感技术对生态环境监测与评价发挥着十分重要的作用。本研究利用多尺度、多源遥感数据,对2015年"一带一路"区域的生态环境状况进行监测和分析,旨在提供可持续发展目标生态环境遥感监测的本底。本文选取了几个重要的生态环境方面开展监测与分析,主要包括宏观生态系统结构和植被状况、太阳能资源分布、水资源平衡、主要生态环境限制因素对经济走廊建设的影响、主要城市生态环境质量等。监测区域覆盖亚洲、非洲、欧洲和大洋洲的陆上区域。研究结果为生态环境评价与保护提供了有效的决策依据,有助于"一带一路"建设积极推进。     

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

ABSTRACT

For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.     

The Sensor Web: systems of sensor systems

Abstract

Global Earth Observing System of Systems (GEOSS) presents a great challenge of System of Systems integration across organisational and political boundaries. One existing paradigm that can address the scale of the challenge is that of the Sensor Web. In this paradigm, the internet is evolving into an active, macro sensing instrument, capable of drawing sensory data from around the globe to the fingertips of individuals. The Sensor Web will support scientific research and facilitate transparent political decision making. This article presents some of the technologies explored and activities engaged in by the GEOSS Sensor Web community, towards achieving GEOSS goals.     

顾及地理空间视角的区域SDGs综合评估方法与示范

目前世界各国正积极落实联合国《2030年可持续发展议程》及其17项可持续发展目标(Sustainable Development Goals,英文缩写为SDGs),重要举措之一是利用统计和地理信息进行SDGs进展评估监测。就总体而言,国内外这方面研究尚处于概念设计、方法探讨和单指标、小范围试点阶段。究其原因,主要是涉及因素众多、技术过程复杂,既面临全球指标体系的科学理解、海量时空数据的融合处理、顾及地理视角的指标计算、基于事实的SDGs分析评估等诸多技术难题,还要实现跨学科的综合分析、多机构的沟通协调等。针对这一国际前沿课题,笔者研究提出了统计和地理信息相结合的综合评估方法,完成了浙江省德清县践行2030议程情况的定量综合评估。既为总结当地践行SDGs经验、发现存在问题、制定改进方案提供了重要科学依据,也为国内外其他区域开展SDGs定量评估监测提供了可借鉴的方法与范例。     

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。     

生物多样性监测网络建设进展

生物多样性是生物及其与环境形成的生态复合体以及与此相关的各种生态过程的总和。由于气候变化、人类活动的加剧,生物多样性正在经受前所未有的快速变化,各国政府和相关国际组织已经积极投入到生物多样性监测和保护中。为了解生物多样性的现状和变化规律,全球性、区域性及国家性生物多样性监测网络陆续建立。地球观测组织—生物多样性监测网络(GEO BON)作为全球性网络,目的是建立和完善生物多样性监测核心指标EBV(Essential Biodiversity Variables),推动监测指标的标准化和全球化,为数据共享和大尺度生物多样性变化评估奠定基础。在区域尺度上,欧盟成立了EU BON,亚太地区成立了AP-BON。在国家尺度上,瑞士、英国、日本等均建立了监测网络。中国科学院在"十二五"期间成立了中国生物多样性监测与研究网络(Sino BON),对中国生物多样性的变化开展长期的监测与研究。生物多样性监测依赖于传统调查方法与先进技术结合,如红外相机、基因技术、无人机技术等。遥感能够提供大范围、全覆盖的生物多样性信息,是未来大尺度生物多样性监测的重要手段之一。为此,GEO BON成立了"生态系统结构"组主要研究如何建立基于遥感数据的EBV。Sino BON也引入了无人机近地面遥感技术探讨更大区域的生物多样性监测。未来随着中国综合地球观测系统的完善,Sino BON的地面观测将更好地与卫星数据结合,实现生物多样性天地一体化监测,服务于中国生物多样性保护与评估。     

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.     

Agricultural growing season calendars derived from MODIS surface reflectance

In order to secure the necessary image acquisitions for global agricultural monitoring applications, we must first articulate Earth observation (EO) requirements for diverse agricultural landscapes and cropping systems. Crucial to this task is the identification of agricultural growing season timing at a meaningful spatial scale, so as to better define the necessary periods of image acquisition. To this end, 10 years of MODIS Terra Surface Reflectance imagery have been used to determine phenological transition dates including start of season, peak period, and end of season at 0.5° globally. This is the first set of global, satellite-derived, cropland-specific calendar dates for major field crops within a 0.5°, herein called agricultural growing season calendars Preliminary comparison against ground-based crop-specific calendars is performed, highlighting the utility of this approach for articulating growing season timing and its interannual and within-region variability. This research provides critical inputs for defining the EO requirements for the Global Agricultural Monitoring initiative (GEOGLAM), an effort by the Group on Earth Observations (GEO) to synergize existing national and regional observation systems for improved agricultural production and food security monitoring.     

Data poverty: A global evaluation for 2009 to 2013 - implications for sustainable development and disaster risk reduction

The article presents a time series (2009–2013) analysis for a new version of the “Digital Divide” concept that developed in the 1990s. Digital information technologies, such as the Internet, mobile phones and social media, provide vast amounts of data for decision-making and resource management. The Data Poverty Index (DPI) provides an open-source means of annually evaluating global access to data and information. The DPI can be used to monitor aspects of data and information availability at global and national levels, with potential application at local (district) levels. Access to data and information is a major factor in disaster risk reduction, increased resilience to disaster and improved adaptation to climate change. In that context, the DPI could be a useful tool for monitoring the Sustainable Development Goals of the Sendai Framework for Disaster Risk Reduction (2015–2030). The effects of severe data poverty, particularly limited access to geoinformatic data, free software and online training materials, are discussed in the context of sustainable development and disaster risk reduction. Unlike many other indices, the DPI is underpinned by datasets that are consistently provided annually for almost all the countries of the world and can be downloaded without restriction or cost.     

GeoCube： 面向大规模分析的多源对地观测时空立方体

随着对地立体观测体系的建立,遥感大数据不断累积。传统基于文件、景/幅式的影像组织方式,时空基准不够统一,集中式存储不利于大规模并行分析。对地观测大数据分析仍缺乏一套统一的数据模型与基础设施理论。近年来,数据立方体的研究为对地观测领域大数据分析基础设施提供了前景。基于统一的分析就绪型多维数据模型和集成对地观测数据分析功能,可构建一个基于数据立方的对地观测大数据分析基础设施。因此,本文提出了一个面向大规模分析的多源对地观测时空立方体,相较于现有的数据立方体方法,强调多源数据的统一组织、基于云计算的立方体处理模式以及基于人工智能优化的立方体计算。研究有助于构建时空大数据分析的新框架,同时建立与商业智能领域的数据立方体关联,为时空大数据建立统一的时空组织模型,支持大范围、长时序的快速大规模对地观测数据分析。本文在性能上与开源数据立方做了对比,结果证明提出的多源对地观测时空立方体在处理性能上具有明显优势。     

“全球生态环境遥感监测年度报告”回顾：2012—2021

持续开展生态环境遥感监测对于认识和评估全球生态系统可持续性和人类生存环境状况具有重要的科学意义,是面向“推进生态文明建设”和“构建地球生命共同体”等目标和愿景,提升综合地球观测能力和应对全球生态环境挑战的务实行动。为此,中华人民共和国科学技术部国家遥感中心聚焦可持续发展、气候变化、防灾减灾和韧性城市等优先事项,通过政府部门、科研机构、国际组织和社会公众的协同,连续十年（2012年—2021年）发布和共享了“全球生态环境遥感监测年度报告”共29个专题报告和100余个遥感数据集。此项工作取得的成果包括：在遥感技术方法创新方面,基于国产高分辨率卫星和多源遥感影像,针对生态系统状况和人类活动痕迹的高精度遥感监测取得了一批具有自主知识产权的算法模型和数据产品;在人类生存环境认知方面,显著提升了公众对粮食安全、气候变化、城市扩展、土地退化和自然灾害风险等全球生态环境热点问题的科学认知;在生态环境重点区域方面,对“一带一路”、南极、非洲、东盟等典型区域开展精细化监测和评估。该项工作是中国作为地球观测组织GEO (Group on Earth Observations)联合主席国对国际社会的实质贡献,...     

Rapid,high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube

The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations – the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25?m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.