China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned
In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/9/407 |
| _version_ | 1797486931627474944 |
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| author | Qianqian Cao Guoqing Li Xiaochuang Yao Yue Ma |
| author_facet | Qianqian Cao Guoqing Li Xiaochuang Yao Yue Ma |
| author_sort | Qianqian Cao |
| collection | DOAJ |
| description | In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster. |
| first_indexed | 2024-03-09T23:41:20Z |
| format | Article |
| id | doaj.art-e01196bb0a17442ea7c285919724dc00 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T23:41:20Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-e01196bb0a17442ea7c285919724dc002023-11-23T16:53:03ZengMDPI AGInformation2078-24892022-08-0113940710.3390/info13090407China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons LearnedQianqian Cao0Guoqing Li1Xiaochuang Yao2Yue Ma3Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaIn the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.https://www.mdpi.com/2078-2489/13/9/407China Data Cuberemote sensing data managementanalysis-ready dataGF-1 dataLandsat data |
| spellingShingle | Qianqian Cao Guoqing Li Xiaochuang Yao Yue Ma China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned Information China Data Cube remote sensing data management analysis-ready data GF-1 data Landsat data |
| title | China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned |
| title_full | China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned |
| title_fullStr | China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned |
| title_full_unstemmed | China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned |
| title_short | China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned |
| title_sort | china data cube cdc for big earth observation data practices and lessons learned |
| topic | China Data Cube remote sensing data management analysis-ready data GF-1 data Landsat data |
| url | https://www.mdpi.com/2078-2489/13/9/407 |
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