Pub Date : 2022-01-02DOI: 10.1080/20964471.2022.2033424
Fa-Ju Chen, Zhongchang Sun
the urbanization intensity index (UII) to quantitatively measure urban dynamics in the vicinity of World Heritage sites, including a global human settle-ment layer, global population grid product, and global nighttime light imagery. The results show that the mean UII value at 79 world cultural heritage sites in the Belt and Road region increased from 0.26 in 2000 to 0.29 in 2015. The UII dataset provides valuable information for international communities to develop heritage preservation policies.
{"title":"Big earth data for achieving the sustainable development goals in the belt and road region","authors":"Fa-Ju Chen, Zhongchang Sun","doi":"10.1080/20964471.2022.2033424","DOIUrl":"https://doi.org/10.1080/20964471.2022.2033424","url":null,"abstract":"the urbanization intensity index (UII) to quantitatively measure urban dynamics in the vicinity of World Heritage sites, including a global human settle-ment layer, global population grid product, and global nighttime light imagery. The results show that the mean UII value at 79 world cultural heritage sites in the Belt and Road region increased from 0.26 in 2000 to 0.29 in 2015. The UII dataset provides valuable information for international communities to develop heritage preservation policies.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86972343","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 : 2022-01-02DOI: 10.1080/20964471.2021.2018789
Yuran Cui, Xiaosong Li
ABSTRACT Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.
{"title":"A new global land productivity dynamic product based on the consistency of various vegetation biophysical indicators","authors":"Yuran Cui, Xiaosong Li","doi":"10.1080/20964471.2021.2018789","DOIUrl":"https://doi.org/10.1080/20964471.2021.2018789","url":null,"abstract":"ABSTRACT Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76640088","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 : 2021-12-27DOI: 10.1080/20964471.2021.1989792
Xinyu Tang, X. Yao, Diyou Liu, Long Zhao, Li Li, Dehai Zhu, Guoqing Li
ABSTRACT When using distributed storage systems to store gridded remote sensing data in large, distributed clusters, most solutions utilize big table index storage strategies. However, in practice, the performance of big table index storage strategies degrades as scenarios become more complex, and the reasons for this phenomenon are analyzed in this paper. To improve the read and write performance of distributed gridded data storage, this paper proposes a storage strategy based on Ceph software. The strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data, finding the cluster location of gridded data through hash-like calculations. The method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded data. Random write and spatial query experiments proved the feasibility, effectiveness, and stability of this strategy. The experiments prove that the method has higher stability than, and that the average query time is 38% lower than that for, the large table index storage strategy, which greatly improves the storage and query efficiency of gridded images.
{"title":"A Ceph-based storage strategy for big gridded remote sensing data","authors":"Xinyu Tang, X. Yao, Diyou Liu, Long Zhao, Li Li, Dehai Zhu, Guoqing Li","doi":"10.1080/20964471.2021.1989792","DOIUrl":"https://doi.org/10.1080/20964471.2021.1989792","url":null,"abstract":"ABSTRACT When using distributed storage systems to store gridded remote sensing data in large, distributed clusters, most solutions utilize big table index storage strategies. However, in practice, the performance of big table index storage strategies degrades as scenarios become more complex, and the reasons for this phenomenon are analyzed in this paper. To improve the read and write performance of distributed gridded data storage, this paper proposes a storage strategy based on Ceph software. The strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data, finding the cluster location of gridded data through hash-like calculations. The method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded data. Random write and spatial query experiments proved the feasibility, effectiveness, and stability of this strategy. The experiments prove that the method has higher stability than, and that the average query time is 38% lower than that for, the large table index storage strategy, which greatly improves the storage and query efficiency of gridded images.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87249588","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 : 2021-12-14DOI: 10.1080/20964471.2021.1988426
C. Xue, Yangfeng Xu, Yawen He
ABSTRACT From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090).
{"title":"A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products","authors":"C. Xue, Yangfeng Xu, Yawen He","doi":"10.1080/20964471.2021.1988426","DOIUrl":"https://doi.org/10.1080/20964471.2021.1988426","url":null,"abstract":"ABSTRACT From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090).","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82337728","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 : 2021-12-08DOI: 10.1080/20964471.2021.1992916
Xingxing Wang, Y. Qiu, Yixiao Zhang, J. Lemmetyinen, B. Cheng, Wenshan Liang, M. Leppäranta
ABSTRACT Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P<0.001) with that obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data – the average correlation coefficient was 0.90 and the average RMSE was 7.87 days. The minimum RMSE was 4.39 days for CIF. The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations. The PMW LIP dataset provides the basic freeze–thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.
{"title":"A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing","authors":"Xingxing Wang, Y. Qiu, Yixiao Zhang, J. Lemmetyinen, B. Cheng, Wenshan Liang, M. Leppäranta","doi":"10.1080/20964471.2021.1992916","DOIUrl":"https://doi.org/10.1080/20964471.2021.1992916","url":null,"abstract":"ABSTRACT Lake ice phenology (LIP) is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts. Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe, North America, and the Tibetan Plateau, but there is a lack of data for inner Eurasia. In this work, enhanced-resolution passive microwave satellite data (PMW) were used to investigate the Northern Hemisphere Lake Ice Phenology (PMW LIP). The Freeze Onset (FO), Complete Ice Cover (CIC), Melt Onset (MO), and Complete Ice Free (CIF) dates were derived for 753 lakes, including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020. Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF, respectively, and the corresponding values of the RMSE were 11.84 and 10.07 days. The lake ice phenology in this dataset was significantly correlated (P<0.001) with that obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data – the average correlation coefficient was 0.90 and the average RMSE was 7.87 days. The minimum RMSE was 4.39 days for CIF. The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations. The PMW LIP dataset provides the basic freeze–thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82846054","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}
ABSTRACT In this paper, snow and ice thickness products derived from an updated Fast Ice Prediction System Version 2.0 (FIPS V2.0) in Prydz Bay, East Antarctica, are introduced and compared with in-situ observations. FIPS V2.0 is comprised of a newly-developed snowdrift parameterization compared to the original FIPS V1.0. The simulation domain covers the entire fast ice region in Prydz Bay and is configured to 720 grid cells, with a spatial resolution of 0.125°. The ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasting (ECMWF) were used as the atmospheric forcing. The in-situ observations were obtained near Zhongshan Station by the wintering team, and the measurement frequency of the snow and ice thicknesses was around one week. Both the FIPS V2.0 products and in-situ observations introduced in this paper cover the time periods from 2012 to 2016. The primary assessments based on the in-situ observations show that FIPS V2.0 has mean biases of 0.01 ± 0.07 m and 0.23 ± 0.09 m for snow and ice thickness simulations, respectively. The results indicate that the updated FIPS V2.0 produces a reasonable snow thickness due to the newly-developed snowdrift parameterization, but it overestimates the ice thickness due to the cold bias in the air temperature forcing. These 2-D snow and ice thickness distributions provide important references for sea ice thermodynamic studies, remote sensing validations, and icebreaker navigation assessments in this region. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00066.
{"title":"Snow and ice thicknesses derived from Fast Ice Prediction System Version 2.0 (FIPS V2.0) in Prydz Bay, East Antarctica: comparison with in-situ observations","authors":"Jiechen Zhao, Jin-Ming Cheng, Zhongxiang Tian, Xiaopeng Han, Hui Shen, Guanghua Hao, Honglin Guo, Qi Shu","doi":"10.1080/20964471.2021.1981196","DOIUrl":"https://doi.org/10.1080/20964471.2021.1981196","url":null,"abstract":"ABSTRACT In this paper, snow and ice thickness products derived from an updated Fast Ice Prediction System Version 2.0 (FIPS V2.0) in Prydz Bay, East Antarctica, are introduced and compared with in-situ observations. FIPS V2.0 is comprised of a newly-developed snowdrift parameterization compared to the original FIPS V1.0. The simulation domain covers the entire fast ice region in Prydz Bay and is configured to 720 grid cells, with a spatial resolution of 0.125°. The ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasting (ECMWF) were used as the atmospheric forcing. The in-situ observations were obtained near Zhongshan Station by the wintering team, and the measurement frequency of the snow and ice thicknesses was around one week. Both the FIPS V2.0 products and in-situ observations introduced in this paper cover the time periods from 2012 to 2016. The primary assessments based on the in-situ observations show that FIPS V2.0 has mean biases of 0.01 ± 0.07 m and 0.23 ± 0.09 m for snow and ice thickness simulations, respectively. The results indicate that the updated FIPS V2.0 produces a reasonable snow thickness due to the newly-developed snowdrift parameterization, but it overestimates the ice thickness due to the cold bias in the air temperature forcing. These 2-D snow and ice thickness distributions provide important references for sea ice thermodynamic studies, remote sensing validations, and icebreaker navigation assessments in this region. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00066.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77615981","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 : 2021-12-07DOI: 10.1080/20964471.2021.1996858
Xiaoming Li, Ke Wu, Bingqing Huang
ABSTRACT The Arctic is one of the most significant changing areas on the Earth under the climate change scenario. More regions in the Arctic are becoming ice-free oceans in the melting season or through the whole year. Therefore, ocean wind and wave, as the two most important parameters in the air–sea interface, are drawing significant attention to the Arctic Ocean. Scatterometer and radar altimeter are the two traditional remote sensing instruments for ocean wind and wave observations, while the former is limited by coarse spatial resolution and the latter has small spatial coverage. Wind and wave data in high spatial resolution and wide coverage by synthetic aperture radar (SAR) are currently lacking in the Arctic Ocean. We developed an ocean wind and wave dataset by Sentinel-1 SAR in the pan-Arctic Ocean (above 60°N), covering January 2017 to May 2021. By comparing with sea surface wind speed data of scatterometer, the SAR-retrieved wind data achieve an accuracy of 1.23 m/s, in terms of root mean square error (RMSE). Compared with significant wave height data of radar altimeter, the SAR retrievals have an RMSE of 0.66 m. The data records are in the standard NetCDF-4 format. The dataset is publicly available at: http://www.dx.doi.org/10.11922/sciencedb.00834.
{"title":"Pan-Arctic ocean wind and wave data by spaceborne SAR","authors":"Xiaoming Li, Ke Wu, Bingqing Huang","doi":"10.1080/20964471.2021.1996858","DOIUrl":"https://doi.org/10.1080/20964471.2021.1996858","url":null,"abstract":"ABSTRACT The Arctic is one of the most significant changing areas on the Earth under the climate change scenario. More regions in the Arctic are becoming ice-free oceans in the melting season or through the whole year. Therefore, ocean wind and wave, as the two most important parameters in the air–sea interface, are drawing significant attention to the Arctic Ocean. Scatterometer and radar altimeter are the two traditional remote sensing instruments for ocean wind and wave observations, while the former is limited by coarse spatial resolution and the latter has small spatial coverage. Wind and wave data in high spatial resolution and wide coverage by synthetic aperture radar (SAR) are currently lacking in the Arctic Ocean. We developed an ocean wind and wave dataset by Sentinel-1 SAR in the pan-Arctic Ocean (above 60°N), covering January 2017 to May 2021. By comparing with sea surface wind speed data of scatterometer, the SAR-retrieved wind data achieve an accuracy of 1.23 m/s, in terms of root mean square error (RMSE). Compared with significant wave height data of radar altimeter, the SAR retrievals have an RMSE of 0.66 m. The data records are in the standard NetCDF-4 format. The dataset is publicly available at: http://www.dx.doi.org/10.11922/sciencedb.00834.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87650594","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 : 2021-11-28DOI: 10.1080/20964471.2021.1996866
Bing Han, Tengteng Qu, Zili Huang, Qiangyu Wang, Xinlong Pan
ABSTRACT The occurrence of large-magnitude disasters has significantly aroused public attention regarding diversified site selection of emergency facilities. In particular, emergency airport site selection (EASS) is highly complicated, and relevant research is rarely conducted. Emergency airport site selection is a scenario with a wide spatiotemporal range, massive data, and complex environmental information, while traditional facility site selection methods may not be applicable to a large-scale time-varying airport environment. In this work, an emergency airport site selection application is presented based on the GeoSOT-3D global subdivision grid model, which has demonstrated good suitability of the discrete global grid system as a spatial data structure for site selection. This paper proposes an objective function that adds a penalty factor to solve the constraints of coverage and the environment in airport construction. Through multiple iterations of the simulated annealing algorithm, the optimal airport construction location can be selected from multiple preselected points. With experimental verifications, this research may effectively and reasonably solve the emergency airport site selection issue under different circumstances.
{"title":"Emergency airport site selection using global subdivision grids","authors":"Bing Han, Tengteng Qu, Zili Huang, Qiangyu Wang, Xinlong Pan","doi":"10.1080/20964471.2021.1996866","DOIUrl":"https://doi.org/10.1080/20964471.2021.1996866","url":null,"abstract":"ABSTRACT The occurrence of large-magnitude disasters has significantly aroused public attention regarding diversified site selection of emergency facilities. In particular, emergency airport site selection (EASS) is highly complicated, and relevant research is rarely conducted. Emergency airport site selection is a scenario with a wide spatiotemporal range, massive data, and complex environmental information, while traditional facility site selection methods may not be applicable to a large-scale time-varying airport environment. In this work, an emergency airport site selection application is presented based on the GeoSOT-3D global subdivision grid model, which has demonstrated good suitability of the discrete global grid system as a spatial data structure for site selection. This paper proposes an objective function that adds a penalty factor to solve the constraints of coverage and the environment in airport construction. Through multiple iterations of the simulated annealing algorithm, the optimal airport construction location can be selected from multiple preselected points. With experimental verifications, this research may effectively and reasonably solve the emergency airport site selection issue under different circumstances.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87928868","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 : 2021-11-26DOI: 10.1080/20964471.2021.2009228
Huadong Guo, H. Hackmann, Ke Gong
The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals (SDGs). Big data improves the extent to which scientific evidence and innovative technological solutions can be adopted to meet these goals. However, the tools and methods of big data are still somewhat of a novelty in this respect, and their value must therefore be demonstrated to stakeholders. To this end, the scientific and academic communities are working to provide relatable examples of the benefits and potential uses of big data for SDGs. Alternative solutions to capacity and infrastructure challenges can be offered, especially in the developing world, to facilitate the dissemination of knowledge and ultimately informed actions. Big data enables innovative uses of emerging tools and methodologies to solve sustainability challenges at multiple scales and dimensions. This issue is the second in a series of two issues being published by the Big Earth Data journal. The first was published in August 2021. This December issue compiles six papers from experts in leading institutes on data and science. Charlotte Poussin et al. focus on SDG 15, in particular on the drying conditions in Switzerland. Utilizing a time series of Landsat images spanning 35 years, they derived annual and seasonal NDWI and studied water content evolution at various scales. They identified a slow drying tendency at the country scale at low and mid-altitudes. They demonstrated an important application of Earth observation data for nationalscale monitoring in support of SDG 15. Hiromichi Fukui et al. present the concepts of Digital Earth as a valuable platform to enable green transformation as envisioned by the international community in adopting the SDGs. Working on the concept of Essential Variables within the Digital Earth Framework, the authors propose a conceptual design of Essential SDG Variables for Digital Earth and introduce use and implementation cases. Zahra Assarkhaniki et al. present results of an experiment comparing two machine learning classification approaches designed to detect settlements in Jakarta, Indonesia, using openly accessible very high resolution Landsat 8 satellite images for identification. The method improves the scientific process to support implementation of the SDGs. Zaffar Mohamed-Ghouse et al. explored how partnerships facilitate the implementation of big Earth data concepts in addressing SDGs from the perspective of leaders and employees from federal and state government, professional organizations, academia, and BIG EARTH DATA 2021, VOL. 5, NO. 4, 443–444 https://doi.org/10.1080/20964471.2021.2009228
大数据分析和概念的快速发展使我们能够使我们的努力多样化，并增加实施可持续发展目标(sdg)的机会。大数据提高了采用科学证据和创新技术解决方案来实现这些目标的程度。然而，在这方面，大数据的工具和方法仍然是一种新奇的东西，因此它们的价值必须向利益相关者展示。为此，科学界和学术界正在努力提供大数据对可持续发展目标的益处和潜在用途的相关实例。对于能力和基础设施的挑战，可以提供替代解决方案，特别是在发展中国家，以促进知识的传播，并最终采取明智的行动。大数据使新兴工具和方法的创新应用能够在多个尺度和维度上解决可持续发展的挑战。这一期是《大地球数据》杂志出版的两期系列中的第二期。第一本于2021年8月出版。12月的这一期汇集了来自领先数据和科学研究所的专家的六篇论文。Charlotte Poussin等人关注可持续发展目标15，特别是瑞士的干燥条件。利用35年的时间序列Landsat图像，他们获得了年度和季节性NDWI，并研究了不同尺度下的含水量演变。他们发现，在全国范围内，低海拔和中海拔地区存在缓慢的干旱趋势。他们展示了地球观测数据在国家尺度监测中的重要应用，以支持可持续发展目标15。Hiromichi Fukui等人提出了数字地球的概念，认为它是一个有价值的平台，可以实现国际社会在采用可持续发展目标时所设想的绿色转型。基于数字地球框架中基本变量的概念，作者提出了数字地球基本可持续发展目标变量的概念设计，并介绍了使用和实现案例。Zahra Assarkhaniki等人展示了一项实验的结果，该实验比较了两种机器学习分类方法，旨在检测印度尼西亚雅加达的定居点，使用公开获取的非常高分辨率的Landsat 8卫星图像进行识别。该方法改进了科学流程，以支持可持续发展目标的实施。Zaffar mohammed - ghouse等人从联邦和州政府、专业组织、学术界和big Earth data 2021 (VOL. 5, NO. 5)的领导者和员工的角度探讨了伙伴关系如何促进大地球数据概念在解决可持续发展目标中的实施。4,443 - 444 https://doi.org/10.1080/20964471.2021.2009228
{"title":"Big data in support of the sustainable development goals (continued): 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":"10.1080/20964471.2021.2009228","DOIUrl":"https://doi.org/10.1080/20964471.2021.2009228","url":null,"abstract":"The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals (SDGs). Big data improves the extent to which scientific evidence and innovative technological solutions can be adopted to meet these goals. However, the tools and methods of big data are still somewhat of a novelty in this respect, and their value must therefore be demonstrated to stakeholders. To this end, the scientific and academic communities are working to provide relatable examples of the benefits and potential uses of big data for SDGs. Alternative solutions to capacity and infrastructure challenges can be offered, especially in the developing world, to facilitate the dissemination of knowledge and ultimately informed actions. Big data enables innovative uses of emerging tools and methodologies to solve sustainability challenges at multiple scales and dimensions. This issue is the second in a series of two issues being published by the Big Earth Data journal. The first was published in August 2021. This December issue compiles six papers from experts in leading institutes on data and science. Charlotte Poussin et al. focus on SDG 15, in particular on the drying conditions in Switzerland. Utilizing a time series of Landsat images spanning 35 years, they derived annual and seasonal NDWI and studied water content evolution at various scales. They identified a slow drying tendency at the country scale at low and mid-altitudes. They demonstrated an important application of Earth observation data for nationalscale monitoring in support of SDG 15. Hiromichi Fukui et al. present the concepts of Digital Earth as a valuable platform to enable green transformation as envisioned by the international community in adopting the SDGs. Working on the concept of Essential Variables within the Digital Earth Framework, the authors propose a conceptual design of Essential SDG Variables for Digital Earth and introduce use and implementation cases. Zahra Assarkhaniki et al. present results of an experiment comparing two machine learning classification approaches designed to detect settlements in Jakarta, Indonesia, using openly accessible very high resolution Landsat 8 satellite images for identification. The method improves the scientific process to support implementation of the SDGs. Zaffar Mohamed-Ghouse et al. explored how partnerships facilitate the implementation of big Earth data concepts in addressing SDGs from the perspective of leaders and employees from federal and state government, professional organizations, academia, and BIG EARTH DATA 2021, VOL. 5, NO. 4, 443–444 https://doi.org/10.1080/20964471.2021.2009228","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85146554","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 : 2021-11-18DOI: 10.1080/20964471.2021.1994362
Yanjun Wang, Fuchao Li, Bin Zhang, Xiaofeng Li
ABSTRACT With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.
{"title":"Development of a component-based interactive visualization system for the analysis of ocean data","authors":"Yanjun Wang, Fuchao Li, Bin Zhang, Xiaofeng Li","doi":"10.1080/20964471.2021.1994362","DOIUrl":"https://doi.org/10.1080/20964471.2021.1994362","url":null,"abstract":"ABSTRACT With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85173788","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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