Pub Date : 2022-11-26DOI: 10.1080/20964471.2022.2146632
M. Jiang, L. Jia, M. Menenti, Yelong Zeng
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.
{"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}
Pub Date : 2022-11-18DOI: 10.1080/20964471.2022.2136610
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
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.
{"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}
Pub Date : 2022-11-13DOI: 10.1080/20964471.2022.2140868
Yang Wang, H. Karimi
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.
{"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}
Pub Date : 2022-11-07DOI: 10.1080/20964471.2022.2135234
R. Roncella, E. Boldrini, M. Santoro, P. Mazzetti, João Andrade, Nuno Catarino, S. Nativi
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.
{"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}
Pub Date : 2022-10-13DOI: 10.1080/20964471.2021.2017582
A. Baraldi, Luca D. Sapia, D. Tiede, M. Sudmanns, H. Augustin, S. Lang
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.
{"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}
Pub Date : 2022-10-04DOI: 10.1080/20964471.2022.2123946
Juanle Wang, Kun Bu, Dongmei Yan, Jingyue Wang, Bowen Duan, M. Zhang, Guojin He
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.
{"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}
Pub Date : 2022-10-02DOI: 10.1080/20964471.2022.2154974
Y. Qiu, H. Lappalainen, Tao Che, S. Sandven, T. Zhao
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
地球寒冷地区，特别是北极、南极和亚洲高山地区(HMA)，受冰冻圈变化的支配，具有固有的脆弱环境(Guo, 2018;Kulmala, 2018;郭等，2020;Li et al.， 2020;Yao et al.， 2022;地球观测组织(GEO)， 2022)。变暖重塑了冰冻圈所在的区域;它还影响了下游低地地区的水资源供应，开辟了北方海上航线，并影响了多年冻土丰富地区道路和基础设施的稳定性(Pulliainen et al.， 2019)。因此，水相的变化及其后果对环境和数十亿人的生活产生了重大影响。关于构成冰冻圈要素(包括雪、冰川、永久冻土、淡水冰、海冰和固体降水)的及时准确信息，为保护这些寒冷地区脆弱的生态系统和环境提供了数据证据支持，促进了环境资源的可持续开发，为水文气象模型提供了驱动数据，并支持陆地和海洋基础设施的安全使用(Pulliainen et al.， 2019;郭等人，2020)。因此，数据和信息的可用性有助于实现联合国可持续发展目标(UN SDGs) (Hu et al.， 2017;邱等，2016;邱等，2017;赵等，2021;赵等，2021;地理,2022)。通过国际计划和项目，促进了对地观测和增值数据集的开放共享和互操作意识;例如，地球观测组织(GEO)， GEO寒冷地区倡议(Qiu et al.， 2016;Pirazzini et al.， 2020;GEO, 2022)，以及泛欧亚实验计划(Lappalainen et al.， 2022)，以及与极地有关的环境项目，如欧盟地平线2020项目(Sandven et al.， 2020)中的北极综合观测系统(INTAROS)，以及中国科技部资助的多参数北极环境观测与信息服务项目(MARIS);ERA-PLANET Strand-4极地环境综合认识项目(iCUPE) (Petäjä et al.， 2020);中国科学院大地球数据科学工程项目CASEarth极地项目(Li et al.， 2020) (Guo, 2018);高山寒区数字“一带一路”工作组(Qiu et al.， 2017);和第三极环境(Yao et al.， 2012;姚等人，2022)。最近的许多发展都涉及到向公众提供越来越多的国家和国际地球观测系统不断收集的丰富数据。然而，数据集的开放给地球寒冷地区的研究带来了挑战。特别是，缺乏有效的大地球数据2022,VOL. 6, NO. 6。4,381 - 384 https://doi.org/10.1080/20964471.2022.2154974
{"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}
Pub Date : 2022-10-02DOI: 10.1080/20964471.2022.2131956
Drolma Lhakpa, Y. Qiu, Pa Lhak, Lijuan Shi, Maoce Cheng, B. Cheng
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.
{"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}
Pub Date : 2022-09-19DOI: 10.1080/20964471.2022.2116834
S. Chalov, V. Moreido, V. Ivanov, A. Chalova
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.
{"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}
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