Big Earth Data最新文献

{"title":"Understanding spatial patterns in the drivers of greenness trends in the Sahel-Sudano-Guinean region","authors":"M. Jiang, L. Jia, M. Menenti, Yelong Zeng","doi":"10.1080/20964471.2022.2146632","DOIUrl":"https://doi.org/10.1080/20964471.2022.2146632","url":null,"abstract":"ABSTRACT The region-wide spatial pattern of the drivers of vegetation trends in the African Sahel-Sudano-Guinean region, one of the main drylands of the world, has not been fully investigated. Time-series satellite earth observation datasets were used to investigate spatiotemporal patterns of the vegetation greenness changes in the region and then a principal component regression method was applied to identify the region-wide spatial pattern of driving factors. Results find that vegetation greening is widespread in the region, while vegetation browning is more clustered in central West Africa. The dominant drivers of vegetation greenness have a distinct spatial pattern. Climatic factors are the primary drivers, but the impacts of precipitation decrease from north to south, while the impacts of temperature are contrariwise. Coupled with climatic drivers, land cover changes lead to greening trends in the arid zone, especially in the western Sahelian belt. However, the cluster of browning trends in central West Africa can primarily be attributed to the human-induced land cover changes, including an increasing fractional abundance of agriculture. The results highlight the spatial pattern of climatic and anthropic factors driving vegetation greenness changes, which helps natural resources sustainable use and mitigation of climate change and human activities in global dryland ecosystems.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"79 2","pages":"298 - 317"},"PeriodicalIF":4.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72539770","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":"Gridded 20-year climate parameterization of Africa and South America for a stochastic weather generator (CLIGEN)","authors":"A. Fullhart, G. Ponce-Campos, M. Meles, Ryan P. McGehee, G. Armendariz, P. S. Oliveira, Cristiano Das Neves Almeida, J. C. de Araújo, W. Nel, D. Goodrich","doi":"10.1080/20964471.2022.2136610","DOIUrl":"https://doi.org/10.1080/20964471.2022.2136610","url":null,"abstract":"ABSTRACT CLIGEN is a stochastic weather generator that creates statistically representative timeseries of daily and sub-daily point-scale weather variables from observed monthly statistics and other parameters. CLIGEN precipitation timeseries are used as climate input for various risk-assessment modelling applications as an alternative to observe long-term, high temporal resolution records. Here, we queried gridded global climate datasets (TerraClimate, ERA5, GPM-IMERG, and GLDAS) to estimate various 20-year climate statistics and obtain complete CLIGEN input parameter sets with coverage of the African and South American continents at 0.25 arc degree resolution. The estimation of CLIGEN precipitation parameters was informed by a ground-based dataset of >10,000 locations worldwide. The ground observations provided target values to fit regression models that downscale CLIGEN precipitation input parameters. Aside from precipitation parameters, CLIGEN’s parameters for temperature, solar radiation, etc. were in most cases directly calculated according to the original global datasets. Cross-validation for estimated precipitation parameters quantified errors that resulted from applying the estimation approach in a predictive fashion. Based on all training data, the RMSE was 2.23 mm for the estimated monthly average single-event accumulation and 4.70 mm/hr for monthly maximum 30-min intensity. This dataset facilitates exploration of hydrological and soil erosional hypotheses across Africa and South America.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"66 1","pages":"349 - 374"},"PeriodicalIF":4.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74323605","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":"Generating high-resolution climatological precipitation data using SinGAN","authors":"Yang Wang, H. Karimi","doi":"10.1080/20964471.2022.2140868","DOIUrl":"https://doi.org/10.1080/20964471.2022.2140868","url":null,"abstract":"ABSTRACT High-resolution (HR) climate data are indispensable for studying regional climate trends, disaster prediction, and urban development planning in the face of climate change. However, state-of-the-art long-term global climate simulations do not provide appropriate HR climate data. Deep learning models are often used to obtain high-resolution climate data. However, due to the fact that these models require sufficient low-resolution (LR) and HR data pairs for the training process, they cannot be applied to scenario with inadequate training data. In this paper, we explore the applicability of a single image generative adversarial network (SinGAN) in generating HR climate data. SinGAN relies on single LR input data to obtain the corresponding HR data. To improve the performance for extreme-value regions, we propose a SinGAN combined with the weighted patchGAN discriminator (WSinGAN). The proposed WSinGAN outperforms comparable models in generating HR precipitation data, and its results are close to real HR data with sharp gradients and more refined small-scale features. We also test the scalability of the pre-trained WSinGAN for unseen samples and show that although only a single LR sample is used to train WSinGAN, it can still produce reliable HR data for unseen data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"4 1","pages":"81 - 100"},"PeriodicalIF":4.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87109495","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":"Publishing NextGEOSS data on the GEOSS Platform","authors":"R. Roncella, E. Boldrini, M. Santoro, P. Mazzetti, João Andrade, Nuno Catarino, S. Nativi","doi":"10.1080/20964471.2022.2135234","DOIUrl":"https://doi.org/10.1080/20964471.2022.2135234","url":null,"abstract":"ABSTRACT This paper is the second of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform was created as the technological tool to implement interoperability among 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. This paper is focused on the analysis of the NextGEOSS datasets describing the data publishing process from NextGEOSS to the GEOSS platform. In particular, both the administrative registration and the technical registration were taken into consideration. One of the most important data shared by the GEOSS Platform are the NextGEOSS datasets: the present study provides some insights in terms of GEOSS user searches for NextGEOSS data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1 1","pages":"413 - 427"},"PeriodicalIF":4.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80109315","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 Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 2: Software developments","authors":"A. Baraldi, Luca D. Sapia, D. Tiede, M. Sudmanns, H. Augustin, S. Lang","doi":"10.1080/20964471.2021.2017582","DOIUrl":"https://doi.org/10.1080/20964471.2021.2017582","url":null,"abstract":"ABSTRACT Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"57 1","pages":"694 - 811"},"PeriodicalIF":4.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80308274","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":"Classification framework and semantic labeling for Big Earth Data","authors":"Juanle Wang, Kun Bu, Dongmei Yan, Jingyue Wang, Bowen Duan, M. Zhang, Guojin He","doi":"10.1080/20964471.2022.2123946","DOIUrl":"https://doi.org/10.1080/20964471.2022.2123946","url":null,"abstract":"ABSTRACT Big Earth Data refers to the multidimensional integration and association of scientific data, including geography, resources, environment, ecology, and biology. An effective data classification system and label management strategy are important foundations for long-term management of data resources. The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program (CASEarth). This study constructed two sets of classification and coding systems that realize classification by mapping each other; namely, the geosphere-level and Sustainable Development Goals (SDGs) indicator classifications. This technique was based on natural language processing technology and solved problems with subject-word segmentation, weight calculation, and dynamic matching. A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100. Furthermore, we expect our study to provide the methodology and technical support for user-oriented classification and label management services for Big Earth Data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"34 1","pages":"886 - 903"},"PeriodicalIF":4.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80316312","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":"Observations and geophysical value-added datasets for cold high mountain and polar regions","authors":"Y. Qiu, H. Lappalainen, Tao Che, S. Sandven, T. Zhao","doi":"10.1080/20964471.2022.2154974","DOIUrl":"https://doi.org/10.1080/20964471.2022.2154974","url":null,"abstract":"The Earth’s cold regions, in particular, the Arctic, Antarctic, and High-Mountain Asia (HMA), are dominated by the changing cryosphere and have inherently fragile environments (Guo, 2018; Kulmala, 2018; Guo et al., 2020; Li et al., 2020; Yao et al., 2022; Group on Earth Observations (GEO), 2022). Warming has reshaped the regions where the cryosphere is located; it has also been affecting water availability in lowland downstream areas, opening up northern sea routes, and affecting the stability of roads and infrastructure in permafrost rich areas (Pulliainen et al., 2019). Changes in the phase of water and its consequences have thus had a major impact on the environment and the lives of billions of people. Timely and accurate information on the elements that comprise the cryosphere, including snow, glaciers, permafrost, freshwater ice, sea ice, and solid precipitation, provide the data-evidenced support to the protection of these cold regions’ fragile ecosystems and environment, facilitating the sustainable exploitation of environmental resources, providing driven data for hydrometeorological model, and supporting the safe use of infrastructure over land and ocean (Pulliainen et al., 2019; Guo et al., 2020). The availability of data and information thus helps with the achievement of United Nations Sustainable Development Goals (UN SDGs) (Hu et al., 2017; Qiu et al., 2016; Qiu et al., 2017; Zhao et al., 2021; Zhao et al., 2021; GEO, 2022). Awareness of the open sharing and interoperability of Earth observations and valueadded datasets has been promoted by international programs and projects; for example, the Group on Earth Observations (GEO), the GEO Cold Regions Initiative (Qiu et al., 2016; Pirazzini et al., 2020; GEO, 2022), and the Pan-Eurasian Experiment program (Lappalainen et al., 2022), as well as environmental projects concerned with polar regions, such as the Integrated Arctic Observation System (INTAROS), which is part of the EU’s Horizon 2020 project (Sandven et al., 2020), and its counterpart project Multi-Parameters Arctic Environmental Observations and Information Services funded by Ministry of Science and Technology of China (MARIS); the ERA-PLANET Strand-4 Integrative and Comprehensive Understanding on Polar Environments project (iCUPE) (Petäjä et al., 2020); the CASEarth Poles project (Li et al., 2020), which is part of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (Guo, 2018); the Digital Belt and Road Program Working Group on High Mountain and Cold Regions (Qiu et al., 2017); and the Third Pole Environment (Yao et al., 2012; Yao et al., 2022). Many recent developments have been concerned with the rich deliverables of data gathered continuously by the increasing number of national and international Earth observation systems to the public. However, the opening up of datasets is posing challenges for the study of the Earth’s cold regions. In particular, the lack of the efficient BIG EARTH DATA 2022, VOL","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"128 1","pages":"381 - 384"},"PeriodicalIF":4.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82916453","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":"Long-term records of glacier evolution and associated proglacial lakes on the Tibetan Plateau (1976‒2020)","authors":"Drolma Lhakpa, Y. Qiu, Pa Lhak, Lijuan Shi, Maoce Cheng, B. Cheng","doi":"10.1080/20964471.2022.2131956","DOIUrl":"https://doi.org/10.1080/20964471.2022.2131956","url":null,"abstract":"ABSTRACT The glaciers on the Tibetan Plateau (TP) constitute critical sources of water for the proglacial lakes and many rivers found downstream. To better understand the evolution of glaciers and the impact of this on proglacial lakes, seven glaciers corresponding to continenṅtal, subcontinental, and marine climate types that are influenced by westerlies and the Indian summer monsoon were selected for study. The evolution of the edges of these glaciers and their associated proglacial lakes were identified based on the visual interpretation of Landsat TM/ETM+/OLI images. A dataset covering the period 1976–2020 that included the glacier and proglacial lake edge vectors was then created. The relative errors in the areas of the individual glaciers were less than 3%, and for the proglacial lakes these errors were in the range 0%–7%. The dataset was used to effectively compare the changes in glaciers and proglacial lakes that have occurred over the past four decades. The most striking changes that were found were the retreat of glaciers and the formation of small proglacial lakes. This dataset could also be used as a proxy to support research on changes in mountain glaciers, particularly their response to climate change and water resources. This response is of great scientific significance and is important in many applications, including assessments of the ecological problems caused by melting glaciers. The dataset can be downloaded from http://doi.org/10.57760/sciencedb.j00076.00131.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"456 ","pages":"435 - 452"},"PeriodicalIF":4.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72543991","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 elevation change dataset in Greenland ice sheet from 2003 to 2020 using satellite altimetry data","authors":"Bojin Yang, Shuang Liang, Huabing Huang, Xinwu Li","doi":"10.1080/20964471.2022.2116796","DOIUrl":"https://doi.org/10.1080/20964471.2022.2116796","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74500795","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":"Assessing suspended sediment fluxes with acoustic Doppler current profilers: case study from large rivers in Russia","authors":"S. Chalov, V. Moreido, V. Ivanov, A. Chalova","doi":"10.1080/20964471.2022.2116834","DOIUrl":"https://doi.org/10.1080/20964471.2022.2116834","url":null,"abstract":"ABSTRACT Surrogate measures are becoming increasingly used to measure suspended sediment flux, but only few particular computer techniques of data processing are recently developed. This study demonstrates capabilities of acoustic Doppler current profilers (ADCPs) to infer information regarding suspended-sand concentrations in river systems and calculate suspended sediment flux via big data analytics which includes process of analyzing and data mining of measurements based on ADCP signal backscatter intensity data. We present here specific codes done by R language using RStudio software with open-source tidyverse and plotly packages aimed to generate tables containing data of suspended load for cells, verticals and whole cross-section based on backscattering values from 600 kH Teledyne RDInstruments RioGrande WorkHorse ADCP unit, as well perform estimates of morphometric, suspended sediment concentration (SSC) and velocity characteristics of the flow. The developed tools enabled to process large data array consisting of over 56,526,480 geo-referenced values of river depth, streamflow velocity, and backscatter intensity for each river cross-section measured at six case study sites in Russia.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"196 1","pages":"504 - 526"},"PeriodicalIF":4.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77431505","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}