{"title":"A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data","authors":"Shanmu Ma, Jingjuan Liao, Ruofan Jing, Jiaming Chen","doi":"10.1080/20964471.2023.2295632","DOIUrl":"https://doi.org/10.1080/20964471.2023.2295632","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"40 2","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451148","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 : 2023-11-20DOI: 10.1080/20964471.2023.2280279
Yining Yu, F. Hui, Yu Zhou, Chong Liu, Xiao Cheng
{"title":"The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season","authors":"Yining Yu, F. Hui, Yu Zhou, Chong Liu, Xiao Cheng","doi":"10.1080/20964471.2023.2280279","DOIUrl":"https://doi.org/10.1080/20964471.2023.2280279","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"60 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020","authors":"Fei Li, Shupo Ma, Jinhuan Zhu, H. Zou, Peng Li, Libo Zhou","doi":"10.1080/20964471.2023.2277551","DOIUrl":"https://doi.org/10.1080/20964471.2023.2277551","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"63 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266271","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 : 2023-11-15DOI: 10.1080/20964471.2023.2273058
Antonia Ivanda, Ljiljana Šerić, Dušan Žagar, Krištof Oštir
{"title":"An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea","authors":"Antonia Ivanda, Ljiljana Šerić, Dušan Žagar, Krištof Oštir","doi":"10.1080/20964471.2023.2273058","DOIUrl":"https://doi.org/10.1080/20964471.2023.2273058","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"55 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139273089","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 : 2023-11-09DOI: 10.1080/20964471.2023.2275854
Thi Thu Trang Ngo, François Pinet, David Sarramia, Myoung-Ah Kang
Recent advances in big and streaming data systems have enabled real-time analysis of data generated by Internet of Things (IoT) systems and sensors in various domains. In this context, many applications require integrating data from several heterogeneous sources, either stream or static sources. Frameworks such as Apache Spark are able to integrate and process large datasets from different sources. However, these frameworks are hard to use when the data sources are heterogeneous and numerous. To address this issue, we propose a system based on mediation techniques for integrating stream and static data sources. The integration process of our system consists of three main steps: configuration, query expression and query execution. In the configuration step, an administrator designs a mediated schema and defines mapping between the mediated schema and local data sources. In the query expression step, users express queries using customized SQL grammar on the mediated schema. Finally, our system rewrites the query into an optimized Spark application and submits the application to a Spark cluster. The results are continuously returned to users. Our experiments show that our optimizations can improve query execution time by up to one order of magnitude, making complex streaming and spatial data analysis more accessible.
{"title":"A mediation system for continuous spatial queries on a unified schema using Apache Spark","authors":"Thi Thu Trang Ngo, François Pinet, David Sarramia, Myoung-Ah Kang","doi":"10.1080/20964471.2023.2275854","DOIUrl":"https://doi.org/10.1080/20964471.2023.2275854","url":null,"abstract":"Recent advances in big and streaming data systems have enabled real-time analysis of data generated by Internet of Things (IoT) systems and sensors in various domains. In this context, many applications require integrating data from several heterogeneous sources, either stream or static sources. Frameworks such as Apache Spark are able to integrate and process large datasets from different sources. However, these frameworks are hard to use when the data sources are heterogeneous and numerous. To address this issue, we propose a system based on mediation techniques for integrating stream and static data sources. The integration process of our system consists of three main steps: configuration, query expression and query execution. In the configuration step, an administrator designs a mediated schema and defines mapping between the mediated schema and local data sources. In the query expression step, users express queries using customized SQL grammar on the mediated schema. Finally, our system rewrites the query into an optimized Spark application and submits the application to a Spark cluster. The results are continuously returned to users. Our experiments show that our optimizations can improve query execution time by up to one order of magnitude, making complex streaming and spatial data analysis more accessible.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":" 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135292498","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 : 2023-11-05DOI: 10.1080/20964471.2023.2273594
Yi Qiang, Lei Zou, Heng Cai
Quantitative assessment of community resilience can provide support for hazard mitigation, disaster risk reduction, disaster relief, and long-term sustainable development. Traditional resilience assessment tools are mostly theory-driven and lack empirical validation, which impedes scientific understanding of community resilience and practical decision-making of resilience improvement. In the advent of the Big Data Era, the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand, measure, and promote community resilience. This article provides a comprehensive review of the definitions of community resilience, along with the traditional and emerging data and methods of quantitative resilience measurement. The theoretical bases, modeling principles, advantages, and disadvantages of the methods are discussed. Finally, we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience. This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.
{"title":"Big Earth Data for quantitative measurement of community resilience: current challenges, progresses and future directions","authors":"Yi Qiang, Lei Zou, Heng Cai","doi":"10.1080/20964471.2023.2273594","DOIUrl":"https://doi.org/10.1080/20964471.2023.2273594","url":null,"abstract":"Quantitative assessment of community resilience can provide support for hazard mitigation, disaster risk reduction, disaster relief, and long-term sustainable development. Traditional resilience assessment tools are mostly theory-driven and lack empirical validation, which impedes scientific understanding of community resilience and practical decision-making of resilience improvement. In the advent of the Big Data Era, the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand, measure, and promote community resilience. This article provides a comprehensive review of the definitions of community resilience, along with the traditional and emerging data and methods of quantitative resilience measurement. The theoretical bases, modeling principles, advantages, and disadvantages of the methods are discussed. Finally, we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience. This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"75 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135726253","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 : 2023-10-20DOI: 10.1080/20964471.2023.2268322
Claire Obuchowicz, Charlotte Poussin, Gregory Giuliani
Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.
{"title":"Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors","authors":"Claire Obuchowicz, Charlotte Poussin, Gregory Giuliani","doi":"10.1080/20964471.2023.2268322","DOIUrl":"https://doi.org/10.1080/20964471.2023.2268322","url":null,"abstract":"Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569942","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 : 2023-10-11DOI: 10.1080/20964471.2023.2265105
Anh Phan, Hiromichi Fukui
This study investigated variations in nitrogen dioxide (NO2) levels in Ukraine during two significant periods: the COVID-19 pandemic lockdown in 2020 and the armed conflict with Russia in 2022. Original and reprocessed Sentinel 5P data products were utilized for the analysis. A machine learning model was employed to generate a business-as-usual NO2 time series that accounted for meteorological variability. For the nine most populous cities in Ukraine, during the lockdown in 2020 we observed a moderation of increases in NO2 levels during the lockdown compared to the pre-lockdown levels. Looking at the same months during the conflict period in 2022, we identified much more significant reductions in NO2 level in these cities, averaging 12.1% for original and 18.1% for reprocessed datasets. Besides our examination of major urban areas, we observed reductions in NO2 levels in areas surrounding coal power plants damaged or destroyed by the conflict. For the major urban areas in Ukraine, we conclude that changes in daily anthropogenic activities due to the conflict-related events had more substantial impacts on NO2 levels than did COVID-19 lockdown.
{"title":"Quantifying the impacts of the COVID-19 pandemic lockdown and the armed conflict with Russia on Sentinel 5P TROPOMI NO <sub>2</sub> changes in Ukraine","authors":"Anh Phan, Hiromichi Fukui","doi":"10.1080/20964471.2023.2265105","DOIUrl":"https://doi.org/10.1080/20964471.2023.2265105","url":null,"abstract":"This study investigated variations in nitrogen dioxide (NO2) levels in Ukraine during two significant periods: the COVID-19 pandemic lockdown in 2020 and the armed conflict with Russia in 2022. Original and reprocessed Sentinel 5P data products were utilized for the analysis. A machine learning model was employed to generate a business-as-usual NO2 time series that accounted for meteorological variability. For the nine most populous cities in Ukraine, during the lockdown in 2020 we observed a moderation of increases in NO2 levels during the lockdown compared to the pre-lockdown levels. Looking at the same months during the conflict period in 2022, we identified much more significant reductions in NO2 level in these cities, averaging 12.1% for original and 18.1% for reprocessed datasets. Besides our examination of major urban areas, we observed reductions in NO2 levels in areas surrounding coal power plants damaged or destroyed by the conflict. For the major urban areas in Ukraine, we conclude that changes in daily anthropogenic activities due to the conflict-related events had more substantial impacts on NO2 levels than did COVID-19 lockdown.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097635","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}
Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.
{"title":"The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine","authors":"Farzane Mohseni, Amirhossein Ahrari, Jan-Henrik Haunert, Carsten Montzka","doi":"10.1080/20964471.2023.2257905","DOIUrl":"https://doi.org/10.1080/20964471.2023.2257905","url":null,"abstract":"Due to the coarse scale of soil moisture products retrieved from passive microwave observations (SMPMW), several downscaling methods have been developed to enable regional scale applications. However, it can be challenging for users to access final data products and algorithms, as well as managing different data sources and formats, various data processing methods, and the complexity of the workflows from raw data to information products. Here, the Google Earth Engine (GEE), which as of late offers SMPMW, is used to implement a workflow for retrieving 1 km SM at a depth of 0–5 cm using MODIS optical/thermal measurements, the SMPMW coarse scale product, and a random forest regression. The proposed method was implemented on the African continent to estimate weekly SM maps. The results of this study were evaluated against in-situ measurements of three validation networks. Overall, in comparison to the original SMPMW product, which was limited by a spatial resolution of only 9 km, this method is able to estimate SM at 1 km spatial resolution with acceptable accuracy (an average correlation coefficient of 0.64 and a ubRMSD of 0.069 m3/m3). The results show that the proposed method in GEE provides a precise estimation of SM with a higher spatial resolution across the entire continent.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910958","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 : 2023-08-30DOI: 10.1080/20964471.2023.2247864
Liang Zhang, Yang Liu, Jingyun Zheng, Z. Hao
{"title":"The summer standardized precipitation evapotranspiration index (SPEI) dataset for six European regions over the past millennium reconstructed by tree-ring chronologies","authors":"Liang Zhang, Yang Liu, Jingyun Zheng, Z. Hao","doi":"10.1080/20964471.2023.2247864","DOIUrl":"https://doi.org/10.1080/20964471.2023.2247864","url":null,"abstract":"","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"42 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85366169","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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