Big Earth Data最新文献

{"title":"Earth observation and geospatial big data management and engagement of stakeholders in Hungary to support the SDGs","authors":"S. Mihály, Gábor Remetey-Fülöpp, D. Kristóf, A. Czinkóczky, Tamás Palya, L. Pásztor, Pál Rudan, G. Szabó, L. Zentai","doi":"10.1080/20964471.2021.1940733","DOIUrl":"https://doi.org/10.1080/20964471.2021.1940733","url":null,"abstract":"ABSTRACT To support the monitoring and reporting processes during implementation of the Sustainable Development Goals, well-developed, commonly recognized Earth observations and geospatial data, methods, innovations, committed professionals, and strong sustainability policies are necessary. This article informs the readers on the Earth observation and geoinformation developments and innovations, and on the engagement of profession, academy and governance to support implementation of the Sustainable Development Goals in Hungary. Description, analyses and critical assessments are given on the elements selected from Hungarian sustainable-oriented Earth observation and geospatial novelties: (a) Working Group for Sustainable Development mission and national sustainability-policy, (b) international partnerships, domestic activities and achievements, (c) status of the professional education, (d) spatial databases and services to support implementation of the sustainable development, (e) a case study on the internationally recognized soil geoinformation system, (f) national Earth Observation Information System and perspectives of its applications for monitoring the sustainability. The article conclusion strongly advises the Hungarian realization of (a) institutionalization of the Earth observation and geospatial tools and capacity for sustainable development, (b) their use in integration with statistical data, (c) establishment of national spatial information infrastructure and (d) development and spreading of the use of big data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"3 1","pages":"306 - 351"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81923007","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":"Spatial patterns of urban green space and its actual utilization status in China based on big data analysis","authors":"Yiyi Huang, Tao Lin, Guoqin Zhang, Yongguan Zhu, Zhiwei Zeng, Hong Ye","doi":"10.1080/20964471.2021.1939990","DOIUrl":"https://doi.org/10.1080/20964471.2021.1939990","url":null,"abstract":"ABSTRACT Urban green space (UGS) is essential for sustainable urbanization and human well-being. The utilization status of UGS is closely related to the provision of ecosystem services for urban residents. Limitations on data availability, however, have led to the absence of a comprehensive approach for evaluating the actual utilization status of UGS at a large scale. Furthermore, differences in actual UGS utilization between intra-urban and peri-urban areas have not received enough attention. This study used big data analysis by combining point of interest (POI) and land use and cover change (LUCC) to quantify the spatial patterns of UGS utilization, and to evaluate the actual utilization status of UGS in 366 cities on the Chinese mainland. We also explored the differences in the actual utilization of UGS in intra-urban and peri-urban areas. The results showed that 94.01% of UGS resources in China had not been utilized. There was a clear pattern of spatial mismatch between the stock and the actual utilization of UGS, especially in the northwestern region indicated by the Hu Huanyong Line. The actual utilization rate of UGS was closely related to the regional development level. There was a certain mismatch between the actual utilization and stock of intraurban green space (IUGS). The hot spots of the actual utilization rate of IUGS were in Yunnan, Guizhou, and Sichuan Provinces in southwestern China. The differences in UGS actual utilization rates between IUGS and peri-urban green space (PUGS) were small in eastern China, but large in southwestern and northwestern China. The actual utilization rate of IUGS in most Chinese cities was significantly larger than that of PUGS, indicating that PUGS were not well utilized. Our results provide scientific support for urban and regional planners in targeting specific areas for UGS design and development, and in optimizing future UGS planning in China.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"100 1","pages":"391 - 409"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73264135","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":"Innovative approaches to the Sustainable Development Goals using Big Earth Data","authors":"Huadong Guo, Dong Liang, Fa-Ju Chen, Zeeshan Shirazi","doi":"10.1080/20964471.2021.1939989","DOIUrl":"https://doi.org/10.1080/20964471.2021.1939989","url":null,"abstract":"ABSTRACT 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 conduct 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 indicators. Big Earth Data, a special class of big data, integrates multi-source 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 measure 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 environmental considerations.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"94 1","pages":"263 - 276"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79170215","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":"Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities","authors":"Bin Chen, Bing Xu, P. Gong","doi":"10.1080/20964471.2021.1939243","DOIUrl":"https://doi.org/10.1080/20964471.2021.1939243","url":null,"abstract":"ABSTRACT Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape 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 emerging 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 sample 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 facilitate multi-scale fine-resolution EULUC mapping research.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"27 1","pages":"410 - 441"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74614645","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":"Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development","authors":"Christopher J. Owers, R. Lucas, D. Clewley, Carole Planque, S. Punalekar, Belle Tissott, Sean M. T. Chua, P. Bunting, N. Mueller, G. Metternicht","doi":"10.1080/20964471.2021.1948179","DOIUrl":"https://doi.org/10.1080/20964471.2021.1948179","url":null,"abstract":"ABSTRACT Earth Observation (EO) has been recognised as a key data source for supporting the United Nations Sustainable Development Goals (SDGs). Advances in data availability and analytical capabilities have provided a wide range of users access to global coverage analysis-ready data (ARD). However, ARD does not provide the information required by national agencies tasked with coordinating the implementation of SDGs. Reliable, standardised, scalable mapping of land cover and its change over time and space facilitates informed decision making, providing cohesive methods for target setting and reporting of SDGs. The aim of this study was to implement a global framework for classifying land cover. The Food and Agriculture Organisation’s Land Cover Classification System (FAO LCCS) provides a global land cover taxonomy suitable to comprehensively support SDG target setting and reporting. We present a fully implemented FAO LCCS optimised for EO data; Living Earth, an open-source software package that can be readily applied using existing national EO infrastructure and satellite data. We resolve several semantic challenges of LCCS for consistent EO implementation, including modifications to environmental descriptors, inter-dependency within the modular-hierarchical framework, and increased flexibility associated with limited data availability. To ensure easy adoption of Living Earth for SDG reporting, we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input parameters. Living Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countries.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"13 1 1","pages":"368 - 390"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78038566","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":"Big data in support of the Sustainable Development Goals: a celebration of the establishment of the International Research Center of Big Data for Sustainable Development Goals (CBAS)","authors":"Huadong Guo, H. Hackmann, Ke Gong","doi":"10.1080/20964471.2021.1962621","DOIUrl":"https://doi.org/10.1080/20964471.2021.1962621","url":null,"abstract":"In the last century the impacts of human activity on natural processes that sustain the Earth’s biosphere, atmosphere, hydrosphere and lithosphere and that provide the bedrock of human life support systems, have grown to the extent that they pose a credible existential threat to humanity. Today, the biggest challenge for science, technology and innovation (STI) is to contribute to the pursuit of global sustainability as exemplified in the Sustainable Development Goals (SDGs) that were adopted by the United Nations (UN) in 2015. Referred to as the 2030 Agenda for Sustainable Development, the SDGs comprise an ambitious, integrated framework of goals that represent humanity’s commitment to comprehensive and transformative action in response to the world’s most pressing social, economic, and environmental problems. In developing strategies for the successful achievement of the 2030 Agenda, the UN recognizes the importance of integrating scientific evidence in policy and decisionmaking processes. Through the Technology Facilitation Mechanism (TFM) and other means at its disposal, the UN encourages multi-stakeholder engagement and partnerships that can effectively mobilize and utilize STI to generate actionable knowledge and contribute practical solutions to global sustainability demands, problems, and challenges. One of the key aspects that the UN is focusing on is improving access to, and ensuring the quality of, reliable data sources. Doing so allows us to establish what situations, risks, and ongoing policies should be considered in order to correctly analyze data and develop effective strategies. The lack of a comprehensive implementation plan for the Global Indicator Framework for the Sustainable Development Goals and Targets, adopted by the UN in 2017 as a means of measuring and monitoring progress towards the SDGs, exposes the challenges and systems gaps in data collection. It points to a pressing need for the urgent identification of well-defined collection methods, which hitherto have prevented the successful implementation of the indicator framework. The International Science Council report “A Guide to SDG Interactions: from Science to Implementation” further stresses the importance of data as a driver for policy-making, by highlighting the need to observe and evaluate the dynamic interaction between different SDGs when formulating implementation policies through an integrated and trans-disciplinary scientific approach. Ensuring sustainable development therefore calls for innovative ideas utilizing new and multiple sources of data and information. This has been made possible by the rapid digitization of society in the past decades. Mass quantities of data on human activities and behaviors and on environmental changes – “Big Data” – have created enormous value and resulted in inventive services that enable the inclusion of digital concepts in a wide variety BIG EARTH DATA 2021, VOL. 5, NO. 3, 259–262 https://doi.org/10.1080/20964471.2021.1962","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1994 1","pages":"259 - 262"},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82424906","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":"Data science for oceanography: from small data to big data","authors":"Chengcheng Qian, Baoxiang Huang, Xueqing Yang, Ge Chen","doi":"10.1080/20964471.2021.1902080","DOIUrl":"https://doi.org/10.1080/20964471.2021.1902080","url":null,"abstract":"ABSTRACT The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven. Based on the types and distribution of oceanographic data, this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science. The ocean science has not fully entered the era of big data. There are two ways to expand the amount of oceanographic data to better understanding and management of the ocean. On the data level, fully exploit the potential value of big and small ocean data, and transform the limited, small data into rich, big data, will help to achieve this. On the application level, oceanographic data are of great value if realize the federation of the core data owners and the consumers. The oceanographic data will provide not only a reliable scientific basis for climate, ecological, disaster and other scientific research, but also provide an unprecedented rich source of information that can be used to make predictions of the future.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"49 1","pages":"236 - 250"},"PeriodicalIF":4.0,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74249254","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 new spectral index for the quantitative identification of yellow rust using fungal spore information","authors":"Yu Ren, H. Ye, Wenjiang Huang, Huiqin Ma, Anting Guo, Chao Ruan, Linyi Liu, Binxiang Qian","doi":"10.1080/20964471.2021.1907933","DOIUrl":"https://doi.org/10.1080/20964471.2021.1907933","url":null,"abstract":"ABSTRACT Yellow rust (Puccinia striiformis f. sp. Tritici) is a frequently occurring fungal disease of winter wheat (Triticum aestivum L.). During yellow rust infestation, fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins. We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index (YRSI). The estimation accuracy and stability were evaluated using two years of leaf spectral data, and the results were compared with eight indices commonly used for yellow rust detection. The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data (R2 = 0.710, RMSE = 0.097) and outperformed the published indices (R2 = 0.587, RMSE = 0.120) for the validation using the 2002 spectral data. The random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested, healthy, and aphid–infested wheat spectral data. The YRSI provided the best performance.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"5 1","pages":"201 - 216"},"PeriodicalIF":4.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78983994","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":"Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite","authors":"Boxiong Qin, Biao Cao, Z. Bian, Ruibo Li, Hua Li, Xueting Ran, Yongming Du, Qing Xiao, Qinhuo Liu","doi":"10.1080/20964471.2021.1912898","DOIUrl":"https://doi.org/10.1080/20964471.2021.1912898","url":null,"abstract":"ABSTRACT Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"19 1","pages":"161 - 181"},"PeriodicalIF":4.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84683000","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":"Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence","authors":"F. Meng, Ling Huang, Anping Chen, Yao Zhang, S. Piao","doi":"10.1080/20964471.2021.1920661","DOIUrl":"https://doi.org/10.1080/20964471.2021.1920661","url":null,"abstract":"ABSTRACT Plant phenology is a key parameter for accurately modeling ecosystem dynamics. Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations, satellite data have been widely used for broad-scale phenology studies. Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures (leaves), but not necessarily that of photosynthetic activities. Leveraging data of the recently emerging solar-induced chlorophyll fluorescence (SIF) that is directly related to photosynthesis, and the traditional MODIS Normalized Difference Vegetation Index (NDVI), we investigated the similarities and differences on the start and end of the growing season (SOS and EOS, respectively) of the Tibetan Plateau. We found similar spatiotemporal patterns in SIF-based SOS (SOSSIF) and NDVI-based SOS (SOSNDVI). These spatial patterns were mainly driven by temperature in the east and by precipitation in the west. Yet the two satellite products produced different spatial patterns in EOS, likely due to their different climate dependencies. Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns, especially on regions with scarce field data. This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"2 1","pages":"182 - 200"},"PeriodicalIF":4.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78793807","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}