Big Earth Data最新文献

{"title":"Publishing China satellite data on the GEOSS Platform","authors":"R. Roncella, Lianchong Zhang, E. Boldrini, M. Santoro, P. Mazzetti, S. Nativi","doi":"10.1080/20964471.2022.2107420","DOIUrl":"https://doi.org/10.1080/20964471.2022.2107420","url":null,"abstract":"ABSTRACT This paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS); it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration. The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform. The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"14 1","pages":"398 - 412"},"PeriodicalIF":4.0,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72399968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Think global, cube local: an Earth Observation Data Cube’s contribution to the Digital Earth vision","authors":"M. Sudmanns, H. Augustin, B. Killough, G. Giuliani, D. Tiede, A. Leith, F. Yuan, Adam Lewis","doi":"10.1080/20964471.2022.2099236","DOIUrl":"https://doi.org/10.1080/20964471.2022.2099236","url":null,"abstract":"ABSTRACT The technological landscape for managing big Earth observation (EO) data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations. EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales: local, national, regional, or global. Several EO data cubes with a geographic focus (“local EO data cubes”) have been implemented. However, their alignment with the Digital Earth (DE) vision and the benefits and trade-offs in creating and maintaining them ought to be further examined. We investigate local EO data cubes from five perspectives (science, business and industry, government and policy, education, communities and citizens) and illustrate four examples covering three continents at different geographic scales (Swiss Data Cube, semantic EO data cube for Austria, DE Africa, Virginia Data Cube). A local EO data cube can benefit many stakeholders and players but requires several technical developments. These developments include enabling local EO data cubes based on public, global, and cloud-native EO data streaming and interoperability between local EO data cubes. We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"44 1","pages":"831 - 859"},"PeriodicalIF":4.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86960535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An open compute and data federation as an alternative to monolithic infrastructures for big Earth data analytics","authors":"B. Backeberg, Z. Šustr, E. Fernández, G. Donchyts, A. Haag, J. B. R. Oonk, G. Venekamp, Benjamin Schumacher, Stefan Reimond, Charis Chatzikyriakou","doi":"10.1080/20964471.2022.2094953","DOIUrl":"https://doi.org/10.1080/20964471.2022.2094953","url":null,"abstract":"ABSTRACT An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe. This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data. Three critical aspects of the federation and the chosen technologies are elaborated upon: (1) federated data discovery, (2) federated access and (3) software distribution. With these technologies the open federation aims to provide homogenous access to resources, thereby enabling its users to generate meaningful results quickly and easily. This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration, including discovery of data across distributed archives, away from the end-users. Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures. The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp. Digital Twins vision for a high precision digital model of the Earth to model, monitor and simulate natural phenomena and related human activities.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"22 1","pages":"812 - 830"},"PeriodicalIF":4.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86335397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified representation method for interdisciplinary spatial earth data","authors":"Shuang Wang, Jian Wang, Qin Zhan, Lianchong Zhang, X. Yao, Guoqing Li","doi":"10.1080/20964471.2022.2091310","DOIUrl":"https://doi.org/10.1080/20964471.2022.2091310","url":null,"abstract":"ABSTRACT Unified representation of spatial earth data is an essential scientific issue. The analysis and mining of interdisciplinary spatial earth data resources can help discover hidden scientific knowledge, and even reveal the intrinsic relationship among different disciplines. However, the different description methods and inner structures among interdisciplinary spatial earth data bring significant challenges to unified data management and collaborative analysis in earth environment research. To address this issue, this paper proposes a unified representation method for interdisciplinary spatial earth data. First, this paper establishes a general metadata model and realizes the unified description of interdisciplinary data. Second, an entity data organization model is presented, which can realize the unified organization of entity data with different inner structures. Finally, we introduce the Spatial Earth Data Format (SEDF), a data format based on HDF5 for implementing the data organization model of interdisciplinary spatial earth data. Data representation experiments and validation are conducted to verify the availability and practicability of the proposed data representation method. The results suggest the powerful ability to represent spatial earth data efficiently and ensure data integrity, which is convenient for data management and application.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"34 1","pages":"126 - 145"},"PeriodicalIF":4.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83732947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Area and shape distortions in open-source discrete global grid systems","authors":"A. Kmoch, I. Vasilyev, Holger Virro, E. Uuemaa","doi":"10.1080/20964471.2022.2094926","DOIUrl":"https://doi.org/10.1080/20964471.2022.2094926","url":null,"abstract":"ABSTRACT 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.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"218 1","pages":"256 - 275"},"PeriodicalIF":4.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82686777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Reference Grids for Big Earth Data","authors":"R. Gibb, M. Purss, Z. Sabeur, P. Strobl, Tengteng Qu","doi":"10.1080/20964471.2022.2113037","DOIUrl":"https://doi.org/10.1080/20964471.2022.2113037","url":null,"abstract":"The emerging field of Discrete Global Grid Systems (DGGS) provides a way to organise, store and analyse spatio-temporal data at multiple resolutions and scales (from near global scales down to microns). DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones (or cells). Data integration, decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone. These identifiers are encodings of both the zone’s location and its resolution. As a result, complex multi-dimensional, multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations. DGGS are therefore particularly suited for efficient multi-source data processing, storage, discovery, transmis-sion, visualisation, computation, analysis, and modelling. DGGS are supported by both the Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO) TC211 standards (OGC Abstract Specification – Topic 21 1 , ISO 19170-1 2 ). These published specifications support 2D equal-area DGGS of the Earth’s surface. Current work led through both OGC and ISO/TC-211 is drafting standards to specify 3D (3D & equi-volume) 3 , 4D (spatio-temporal) 4 and axis-aligned 5 DGGS, as well as OGC API DGGS 6 , 7 interface encodings for DGGS infrastructures. The continued effort to develop international standards for DGGS will support the implementation of standardised interoper-able Global Reference Grid Infrastructures that can support efficient and scalable integration of Big Earth Data across multiple organisations around the world. we global","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"10 1","pages":"251 - 255"},"PeriodicalIF":4.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81581235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production","authors":"A. Markos, N. Sims, G. Giuliani","doi":"10.1080/20964471.2022.2076375","DOIUrl":"https://doi.org/10.1080/20964471.2022.2076375","url":null,"abstract":"ABSTRACT Monitoring changes in Annual Net Primary Productivity (ANPP) is required for reporting on UN Sustainable Development Goal (SDG) Indicator 15.3.1: the proportion of land that is degraded over the total land area. Calibrating time-series observations of ANPP to derive Water Use Efficiency (WUE; a measure of ANPP per unit of evapotranspiration) can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes. Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation, which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification (UNCCD). This paper presents an algorithm for the Google Earth Engine (freely and openly available upon request – http://doi.org/10.5281/zenodo.4429773) to calculate and compare ANPP and WUE time series for Santa Cruz, Bolivia, which has recently experienced an intensification in its land use. This code builds on the Good Practice Guidance document (version 1) for monitoring SDG Indicator 15.3.1. We use the MODIS 16-day average, 250 m resolution to demonstrate that the Enhanced Vegetation Index (EVI) responds faster to changes in water availability than the Normalized Difference Vegetation Index (NDVI). We also consider the relationships between ANPP and WUE. Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative. The sign and significance of the correlation between ANPP and WUE may also diverge over time. With further analysis, it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"10 1","pages":"59 - 80"},"PeriodicalIF":4.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87181627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remotely sensed big data for the oceans and polar regions","authors":"Xiaomei Li","doi":"10.1080/20964471.2022.2075100","DOIUrl":"https://doi.org/10.1080/20964471.2022.2075100","url":null,"abstract":"The oceans, which account for 71% of the Earth’s area, and the polar regions, the largest cold source on Earth, jointly play crucial roles in energy exchange and circulation, and in climate change (e.g. McGuire, Chapin, Walsh, & Wirth, 2006). In particular, against the background of global climate change, both the Arctic and Antarctic are experiencing profound changes (Bracegirdle, Connolley, & Turner, 2008; Jeffries, Overland, & Perovich, 2013), and the interactions between the oceans, polar regions and the atmosphere are closer than ever. Remote sensing has become one of the main research tools in ocean and polar studies (Lubin, Ayres, & Hart, 2009). At the same time, the amount of acquired data is undergoing explosive growth (Ma et al., 2015), thus leading oceanography into the era of “big data”. Although there is no agreed definition of big data, the “5 V” characteristics of volume, velocity, variety, veracity and value are commonly used to distinguish big data from other types of data. Satellite and airborne remote sensing data can be considered representative of big data and, along with the development of modern information techniques such as machine learning and cloud computing, have been associated with many advances in Earth observation. Using remote sensing data, particularly big data, the past, present and future of the oceans and polar regions can be better understood. To support the development of remote sensing big data, this Special Issue, “Remotely Sensed Big Data for Ocean and Polar Regions”, contains relevant research, review and data articles aimed at highlighting the recent progress made in the field of remote sensing big data as applied to the ocean and polar regions. Sea surface wind and waves are two important parameters related to the air‒sea interface and play a crucial role in the interactions between sea ice and ocean dynamic processes in the Arctic Ocean. To provide high-resolution ocean wind and wave data that have wide coverage, Li, Wu, and Huang (2021) developed an ocean wind and wave dataset based on Sentinel-1 synthetic aperture radar (SAR) that covered the pan-Arctic Ocean. This dataset, which covers the regions above 60°N, has a spatial resolution of around 2 km and covers the period from January 2017 to May 2021. Based on comparisons with scatterometer data, the SAR-retrieved wind data were found to have an accuracy of 1.23 m s‒1 and the SAR-retrieved significant wave height was found to have an RMSE of 0.66 m from a comparison with altimeter data. The development of this dataset will support offshore construction as well as shipping safety and security in the Arctic and further contribute to studies of the changing Arctic. Sea ice research is an essential component of studies of climate change in the Arctic, and the sea ice concentration (SIC) is one of the basic parameters used to describe the distribution of sea ice. Chen, Zhao, Pang, and Ji (2021) proposed a daily SIC product for the Arctic based on FY-3D M","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"12 1","pages":"141 - 143"},"PeriodicalIF":4.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73755253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes","authors":"Thapthai Chaithong","doi":"10.1080/20964471.2022.2046306","DOIUrl":"https://doi.org/10.1080/20964471.2022.2046306","url":null,"abstract":"ABSTRACT Many landslides triggered by intense rainfall have occurred in mountainous areas in Thailand, causing major economic losses and infrastructure damage. Extreme daily rainfall is a significant trigger for hillslope instability. Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides. Thus, in this context, changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed. Extreme rainfall is modeled using a generalized extreme value distribution and estimated for various return periods. A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions. The analysis period is divided into two periods of 34 years: 1952 to 1985 and 1986 to 2019. According to the analysis results, the distribution of extreme daily rainfall changes in terms of location. The average annual daily maximum rainfall increased by approximately 11.13%. The maximum decrease in the safety factor is approximately 4.5%; therefore, these changes in extreme daily rainfall should be considered in future landslide prevention policies.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"9 3 1","pages":"101 - 125"},"PeriodicalIF":4.0,"publicationDate":"2022-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78263112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area","authors":"Thanh Trinh, Binh Thanh Luu, T. Le, Duong Huy Nguyen, Trong Van Tran, Thi Hai Van Nguyen, K. Q. Nguyen, Lien Thi Nguyen","doi":"10.1080/20964471.2022.2043520","DOIUrl":"https://doi.org/10.1080/20964471.2022.2043520","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"34 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90083156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}