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| 1 | Big Earth data:A new frontier in Earth and information sciences显示文摘Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science. | Huadong Guo | 2017 | Big Earth Data2017，1，1： | 49 |
| 2 | Mapping landslide susceptibility and types using Random Forest显示文摘Landslides are one of the most destructive natural hazards;they can drastically alter landscape morphology,destroy man-made struc-tures,and endanger people’s life.Landslide susceptibility maps(LSMs),which show the spatial likelihood of landslide occurrence,are crucial for environmental management,urban planning,and minimizing economic losses.To date,the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide.This paper presents a data mining approach to producing LSM for a large,heterogeneous region that is susceptible tomultipletypesoflandslides.UsingacasestudyofPiedmont,Italy,a Random Forest algorithm is applied to produce both susceptibility maps and classification maps.These maps are combined to give a highly accurate(over 85%classification accuracy)LSM which con-tains a large amount of information and is easy to interpret.This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for alargeheterogeneousregion withouttheneed formultiple susceptibility assessments. | Khaled Taalab Tao Cheng Yang Zhang | 2018 | Big Earth Data2018，2，2： | 13 |
| 3 | Big data drives the development of Earth science显示文摘Big data is now a popular topic,becoming increasingly well known around the world,and yet the concept of big data and its implications are still novel.To discuss big data,it is appropriate to first talk about what is really meant by this term,and so to begin the first article in the inaugural issue of Big Earth Data,let us look at how data has become big data and why that is important. | Huadong Guo | 2017 | Big Earth Data2017，1，1： | 11 |
| 4 | Generation of ready to use (RTU) products over China based on Landsat series data显示文摘Earth observation community has entered into the era of big data.Family of Landsat sensors have collected massive medium resolution satellite images,which are valuable for long-term land surface monitoring.In order to significantly reduce the magnitude of data processing for remote sensing data users,Landsat-based Ready to Use(RTU)products have been produced.Main RTU products,including orthorectified products,land surface reflectance,land surface temperature,large-area mosaic image,and standard image map products,are described.The resulting Landsat RTU products are hosted on the RSGS earth observation data sharing web site for free download(http://ids.ceode.ac.cn/rtu/).These new products will provide consistent,standardized,multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.In the coming years,CASEarth DataBank system will be constructed,which is an intelligent data service platform for providing not only the RTU products from multi-source satellite data,but also big earth data analysis methods. | Guojin He Zhaoming Zhang Weili Jiao Tengfei Long Yan Peng Guizhou Wang Ranyu Yin Wei Wang Xiaomei Zhang Huichan Liu Bo Cheng Bo Xiang | 2018 | Big Earth Data2018，2，1： | 8 |
| 5 | Big Earth data facilitates sustainable development goals显示文摘The 2030 Agenda for Sustainable Development,comprising 17 Sustainable Development Goals(SDGs),was adopted in September 2015 by heads of state and government at the United Nations(UN)summit.The Agenda is a transformative plan of action for people,planet and prosperity that all countries and all stakeholders will implement to ensure that no one is left behind.The inception of the 2030 Agenda marks a milestone in the progress towards a sustainable society for all. | Huadong Guo | 2020 | Big Earth Data2020，4，1： | 7 |
| 6 | The challenges of a Big Data Earth显示文摘The potential of big data fused with the vision of a digital Earth offers powerful opportunities to deepen understanding of the whole Earth system and the management of a sustainable planet.It is important to stand back from often confusing detail to clarify what those opportunities are and how they might be seized.The essential scientific potential of data,big or small,is to reveal patterns,which have often been the fundamental first step in stimulating inquiry,leading to new questions,new perspectives and potentially to new answers.The digital revolution has created a“digital microscope”that permits us to see patterns that have not been seen before,and when coupled with machine learning technologies to analyse them in creating statistical predictions of the behaviour of both human and non-human systems.These potentials converge with the imperative to represent an Earth system with interacting non-human and human components,as a vital contribution to the understanding and actions required in working towards planetary sustainability.But a digital Earth is also capable of being represented mathematically as a digitally networked phenomenon,analogous to an analogue computer,and should be an important target for a Big Earth Data Journal.We should also return to Al Gore’s vision of an accessible digital Earth with wide usability.Pre-determining the separate functions of parallel digital Earths risks losing one of the great potentials of big data and learning algorithms,the identification and analysis of unanticipated relationships and processes. | Geoffrey Boulton | 2018 | Big Earth Data2018，2，1： | 7 |
| 7 | MODIS-based Daily Lake Ice Extent and Coverage dataset for Tibetan Plateau显示文摘The Tibetan Plateau houses numerous lakes,the phenology and duration of lake ice in this region are sensitive to regional and global climate change,and as such are used as key indicators in climate change research,particularly in environment change comparison studies for the Earth three poles.However,due to its harsh natural environment and sparse population,there is a lack of conventional in situ measurement on lake ice phenology.The Moderate Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Snow Index(NDSI)data,which can be traced back 20 years with a 500 m spatial resolution,were used to monitor lake ice for filling the observation gaps.Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm,and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps.Through time series analysis of every single lake with size greater than 3 km2 in size,308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset,including 216 lakes that can be further retrieved with four determinable lake ice parameters:Freeze-up Start(FUS),Freeze-up End(FUE),Break-up Start(BUS),and Break-up End(BUE),and 92 lakes with two parameters,FUS and BUE.Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing.The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations.The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744. | Yubao Qiu Pengfei Xie Matti Leppäranta Xingxing Wang Juha Lemmetyinen Hui Lin Lijuan Shi | 2019 | Big Earth Data2019，3，2： | 7 |
| 8 | Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)显示文摘Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube. | Gregory Giuliani Bruno Chatenoux Andrea De Bono Denisa Rodila Jean-Philippe Richard Karin Allenbach Hy Dao Pascal Peduzzi | 2017 | Big Earth Data2017，1，1： | 7 |
| 9 | Atmospheric heat source/sink dataset over the Tibetan Plateau based on satellite and routine meteorological observations显示文摘The Tibetan Plateau(TP),acting as a large elevated land surface and atmospheric heat source during spring and summer,has a substantial impact on regional and global weather and climate.To explore the multi-scale temporal variation in the thermal forcing effect of the TP,here we calculated the surface sensible heat and latent heat release based on 6-h routine observations at 80(32)meteorological stations during the period 1979–2016(1960–2016).Meanwhile,in situ air-column net radiation cooling during the period 1984–2015 was derived from satellite data.This new data-set provides continuous,robust,and the longest observational atmospheric heat source/sink data over the third pole,which will be helpful to better understand the spatial-temporal structure and multi-scale variation in TP diabatic heating and its influence on the earth’s climatic system. | Anmin Duan Senfeng Liu Yu Zhao Kailun Gao Wenting Hu | 2018 | Big Earth Data2018，2，2： | 7 |
| 10 | Innovative approaches to the Sustainable Development Goals using Big Earth Data显示文摘A persistent challenge for the Sustainable Development Goals(SDGs)has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to con-duct these assessments.Rapid developments in big data,however,are facilitating a global approach to the SDGs.Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indica-tors.Big Earth Data,a special class of big data,integrates multisource data within a geographic context,utilizing the principles and methodologies of the established literature on big data science,applied specifically to Earth system science.This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and mea-sure progress towards the SDGs.As an example,the Big Earth Data Science Engineering Program(CASEarth)of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs.Lastly,the paper proposes future priorities for developments in Big Earth Data,such as human resource capacity,digital infrastructure,interoperability,and envir-onmental considerations. | Huadong Guo Dong Liang Fang Chen Zeeshan Shirazi | 2021 | Big Earth Data2021，5，3： | 7 |
| 11 | Exploring the depths of the global earth observation system of systems显示文摘This paper explores for the first time the contents,structure and relationships across institutions and disciplines of a global Big Earth Data cyber-infrastructure:the Global Earth Observation System of System(GEOSS).The analysis builds on 1.8 million metadata records harvested in GEOSS.Because this set includes almost all the major large data collections in GEOSS,the analysis represents more than 80%of all the data made available through this global system.We explore two major aspects:the collaborative networks and the thematic coverage in GEOSS.The first connects the contributing organisations through the more than 200,000 keywords used in the systems,and then explores who is citing whom,a proxy for of institutional thickness.The thematic coverage is analysed through neural network algorithms,first on the keywords,and then on the corpus of 653 million lemmatised lower case words built from the titles and abstracts of all 1.8 million metadata records.The findings not only give a good overview of the GEOSS data universe,but offer immediate priorities on how to increase the usability of GEOSS through improved data management,and the opportunity to augment the metadata with high level concept that synthetise well the contents of the data-set. | Max Craglia Jiri Hradec Stefano Nativi Mattia Santoro | 2017 | Big Earth Data2017，1，1： | 5 |
| 12 | A global land cover map produced through integrating multi-source datasets显示文摘In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets. | Min Feng Yan Bai | 2019 | Big Earth Data2019，3，3： | 5 |
| 13 | Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15-exploring the potential of data cube显示文摘Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth.To halt and reverse the current trends in land degradation,there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands,as required by the Sustainable Development Goals(SDGs),in particular,the SDG indicator 15.3.1(“proportion of land that is degraded over total land area”).Earth Observations(EO)can play an important role both for generating this indicator as well as complementing or enhancing national official data sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat or Sentinels).In response to this issue,this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators. | Gregory Giuliani Bruno Chatenoux Antonio Benvenuti Pierre Lacroix Mattia Santoro Paolo Mazzetti | 2020 | Big Earth Data2020，4，1： | 4 |
| 14 | Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets显示文摘Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650. | Christopher T.Lloyd Heather Chamberlain David Kerr Greg Yetman Linda Pistolesi Forrest R.Stevens Andrea E.Gaughan Jeremiah J.Nieves Graeme Hornby Kytt MacManus Parmanand Sinha Maksym Bondarenko Alessandro Sorichetta Andrew J.Tatem | 2019 | Big Earth Data2019，3，2： | 4 |
| 15 | GIScience research challenges for realizing discrete global grid systems as a Digital Earth显示文摘Increasing data resources are available for documenting and detecting changes in environmental,ecological,and socioeconomic processes.Currently,data are distributed across a wide variety of sources(e.g.data silos)and published in a variety of formats,scales,and semantic representations.A key issue,therefore,in building systems that can realize a vision of earth system monitoring remains data integration.Discrete global grid systems(DGGSs)have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring.However,DGGSs remain in their infancy with many technical,conceptual,and operational challenges.With renewed interest in DGGS brought on by a recently proposed standard,the demands of big data,and growing needs for monitoring environmental changes across a variety of scales,we seek to highlight current challenges that we see as central to moving the field(s)and technologies of DGGS forward.For each of the identified challenges,we illustrate the issue and provide a potential solution using a reference DGGS implementation.Through articulation of these challenges,we hope to identify a clear research agenda,expand the DGGS research footprint,and provide some ideas for moving forward towards a scaleable Digital Earth vision.Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS. | Majid Hojati Colin Robertson Steven Roberts Chiranjib Chaudhuri | 2022 | Big Earth Data2022，6，3： | 4 |
| 16 | New discrimination diagrams for basalts based on big data research显示文摘In recent days,discrimination diagrams have been widely used for tracing the tectonic settings and origins of basalts from orogenic belts.However,conventional discrimination diagrams are not accurate enough.Here,we reported six new discrimination diagrams obtained from the global database using data mining methods.For most individual diagrams,island arc basalt can be nearly 100%was identified,whereas ocean island basalt and midocean ridge basalt can be discriminated from each other with less than 10%of overlap,under a confidence coefficient of 85%.Using the six new discrimination diagrams together,basalts of different origins can be efficiently identified. | Qi Zhang Weidong Sun Yong Zhao Fanglin Yuan Shoutao Jiao Wanfeng Chen | 2019 | Big Earth Data2019，3，1： | 4 |
| 17 | A generalized supervised classification scheme to produce provincial wetland inventory maps:an application of Google Earth Engine for big geo data processing显示文摘Wetlands are important natural resources due to their numerous ecological services.Consequently,identifying their locations and extents is imperative.The stability,repeatability,cost-effectiveness,multi-scale coverage,and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications,such as provincial wetland mapping.To do so,it is required to(1)process and classify big geo data(i.e.a large amount of satellite datasets)in a time-and computationally-efficient approach and(2)collect a large amount of field samples.In this study,Google Earth Engine(GEE)and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba,Quebec,and Newfoundland and Labrador(NL).Additionally,using GEE,a generalized supervised classification method is proposed to produce a regional wetland map from a large area(e.g.,a province)when lacking field samples.In fact,using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics,the wetland maps were generated for the other two provinces.The overall classification accuracies for Manitoba,Quebec,and NL were 84%,78%,and 82%,respectively,indicating the high potential of the proposed method for aiding provincial wetland inventory systems. | Meisam Amani Brian Brisco Majid Afshar S.Mohammad Mirmazloumi Sahel Mahdavi Sayyed Mohammad Javad Mirzadeh Weimin Huang Jean Granger | 2019 | Big Earth Data2019，3，4： | 4 |
| 18 | Mapping essential urban land use categories(EULUC)using geospatial big data:Progress,challenges,and opportunities显示文摘Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning,land-scape design,environmental management,health promotion,and biodiversity conservation.Land-use maps outlining the distribution,pattern,and composition of essential urban land use categories(EULUC)have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies.New and improved Earth observations,algorithms,and advanced products for extracting thematic urban information,in association with emer-ging social sensing big data and auxiliary crowdsourcing datasets,all together offer great potentials to mapping fine-resolution EULUC from regional to global scales.Here we review the advances of EULUC mapping research and practices in terms of their data,methods,and applications.Based on the historical retrospect,we summarize the challenges and limitations of current EULUC studies regarding sample collection,mixed land use problem,data and model generalization,and large-scale mapping efforts.Finally,we propose and discuss future opportunities,including cross-scale mapping,optimal integration of multi-source features,global sam-ple libraries from crowdsourcing approaches,advanced machine learning and ensembled classification strategy,open portals for data visualization and sharing,multi-temporal mapping of EULUC change,and implications in urban environmental studies,to facil-itate multi-scale fine-resolution EULUC mapping research. | Bin Chen Bing Xu Peng Gong | 2021 | Big Earth Data2021，5，3： | 4 |
| 19 | Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping显示文摘Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source,multi-temporal,and multi-scale earth observation data.In this paper,the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer(GHSL)data are presented.Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform.A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope.The paper presents the processing workflows and the results of the two main experiments,giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets,and the lessons learnt in terms of handling and processing big earth observation data. | Christina Corbane Martino Pesaresi Panagiotis Politis Vasileios Syrris Aneta JFlorczyk Pierre Soille Luca Maffenini Armin Burger Veselin Vasilev Dario Rodriguez Filip Sabo Lewis Dijkstra Thomas Kemper | 2017 | Big Earth Data2017，1，1： | 3 |
| 20 | Area and shape distortions in open-source discrete global grid systems显示文摘A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube construction.This facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial statistics.We calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data cube.We conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the globe.Uber H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell area.However,they provide more mature software library functionalities.DGGRID provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction. | Alexander Kmoch Ivan Vasilyev Holger Virro Evelyn Uuemaa | 2022 | Big Earth Data2022，6，3： | 3 |
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