Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-19 Cooperative journals: 《中国科学院院刊》
Abstract： Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource. It is a new pattern for scientific discovery with less dependence on causality and heavy dependence on data correlation. It has become a data-intensive scientific paradigm, following previous paradigms of empirical, theoretical and computational science. The paradigm has shifted the methodology of scientific research from theories and models based on causal analysis to comprehensive mechanistic scientific discovery including correlation analysis. As a branch of big data, scientific big data includes internal characteristics such as non-repeatability, high uncertainty, high dimensionality, and computational complexity. External characteristics include data type, data volume, data acquisition, and data analysis. All these characteristics bring new challenges for the techniques and methods of processing scientific big data. On the basis of the above analysis, we raise four recommendations: scientific cognition of scientific big data, construction of scientific big data infrastructure, establishment of a scientific data research center, and the structuring of a scientific big data academic platform.
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-19 Cooperative journals: 《中国科学院院刊》
Abstract： Recent improvements in capability and ability of storing and utilizing vast quantity of data has enabled revolutionary innovation of big data analytics. Big data in just a span of few years have occupied strategic importance in many aspects of human society, making data an important commodity and a valuable resource in the era of knowledge based economies. Scientific research into big data collection, storage, analysis, and exploitation have developed rapidly and continues to progress at a rapid pace. At the same time, the amount of historical Earth observation data generated over the past five decades and the continued human and capital resource investments of many countries, public and private corporations ensures improved generation of Earth observation well into the future with exponentially increasing volumes of information on Earth systems and science. This has given rise to a new class of big data termed as “Big Earth Data”. Big Earth Data has macro-level capabilities that enable rapid and accurate monitoring of Earth, and is increasingly gaining importance in Earth sciences, adding value to its utilization in problem driven science and innovation. This paper introduces the characteristics of Big Earth Data and analyzes its great potential for development, particularly in regards to the role that Big Earth Data can play in transforming Earth science. With this context the paper outlines the Project on Big Earth Data Science Engineering (CASEarth) of the Chinese Academy of Sciences Strategic Priority Research Program and highlights how the CASEarth would contribute to ensure the actual use of Big Earth Data in support of the achievement of Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document. The potential prospects of developing Big Earth Data and its ability to integrate geosciences, information sciences, and space science and technology makes it a strategic future endeavor for revolutionizing Earth Science as a whole.
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-19 Cooperative journals: 《中国科学院院刊》
Abstract： Resource and environmental monitoring have always been an important part of land sustainable management which ground survey and remote sensing monitoring are two fundamental ways. The crowd sourcing geographic data (CSGD) brought by smart phones provides new opportunity for the ground investigation of resources and environment. Meanwhile, the rapid development of cloud computing makes it possible to allow people to process massive remote sensing data much more efficient and accurate. Compared with traditional data acquisition methods, data in the cloud is easier to acquire and process. Based on this, a big data method for environment monitoring is introduced based on CSGD and cloud-based resource data. The large amount of human resources required for traditional resource environment monitoring are no longer needed as the professional services of cloud computing are proposed. It will gradually replace the traditional governmental business on resource survey. The participation of the public avoids a large amount of investment. This approach ultimately leads to efficient and crowd-sourced resource management.
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-19 Cooperative journals: 《中国科学院院刊》
Abstract： Earth observation (EO) data, as the basic and strategic resource of a country, plays an important role in national economy, social development, and defense security, and a new era opportunity of the transition from a “great country of data” to a “strong country of data” is coming to China. This paper summarizes the existing conditions and recent trends of opening and sharing of EO data from both international and domestic perspectives, and then analyzes the problems and challenges of opening and sharing EO data in China. Finally, three suggestions are proposed to promote the opening and sharing of China’s EO data, namely, (1) the construction of data governance system needs to be strengthened to consolidate the foundation for opening and sharing EO data; (2) a sustainable data sharing ecosystem needs to be maintained from regulation and technology; (3) the innovative service modes of data sharing should be created to deepen the application of EO data. By strengthening the data opening and sharing, the potential value of China’s EO big data can be discovered and the strategic role of EO data can be fully motivated. Therefore, the international competitiveness of China’s big EO data will be effectively enhanced.
Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology submitted time 2020-07-14 Cooperative journals: 《干旱区研究》
Abstract：荒漠河岸带植被在维护极端干旱区生态稳定起着极其重要的作用，研究干旱区荒漠河岸带植被对生态输水的响应及其变化过程，对生态保育恢复及输水政策制定具有重要意义。本文以塔里木河下游流域内的荒漠河岸带植被为研究对象，利用Landsat8 OLI、Sentinel-2A等数据构建植被覆被数据与典型监测断面植被指数时序数据，分析2013—2018年荒漠河岸带植被时空变化特征，并结合地下水位数据分析荒漠河岸带植被对生态输水的响应。结果表明：2013—2018年间，塔里木河下游植被面积呈持续的增加趋势，其中灌木面积恢复最大。胡杨和草本距离河岸较近，沿河岸带植被恢复的区域分布位于距离河道1.0km和2.5km的范围，而灌木林恢复区域在双通道输水措施和地下水上升的影响下，沿河岸11km范围内的灌木均呈现不同程度增加。通过对不同生态断面的3种主要植被的长势分析表明，当地下水埋深大于-5.75m时，塔里木河下游植被出现明显改善。
Subjects: Geosciences >> Geography submitted time 2019-01-11 Cooperative journals: 《干旱区地理》
Abstract：本研究利用MODIS-NDVI产品生成中亚天山2000-2016年植被覆盖度，利用线性回归法和偏相关法分析了中亚天山植被时空变化特征及驱动因子。结果表明：中亚天山植被生长及变化趋势具有显著的区域分异性，纬度分区上，中天山和北天山西部植被覆盖度较高的草原、农田和森林在2000-2016年呈现退化趋势；南天山和北天山东部植被覆盖度较低的荒漠、草原和灌丛在同期表现出改善趋势，而中国境内的东天山与境外的西天山相比具有较低的植被覆盖度以及总体改善的变化趋势。中亚天山气候在2000-2016年显示出“暖湿化”特征，温度升高幅度（5.9%）远大于降水增加幅度（1.3%），温度、降水与植被覆盖度的显著相关比例为18.0%和42.6%，降水是中亚天山植被变化的主要气候驱动因素。以巴音布鲁克草原为代表的东天山部分草原受到过度放牧的影响而退化严重，建议加强植被退化区的生态修复与保护力度。
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the intrusion of untrusted users in remote sensing cloud platform, this paper designed a user behavior authentication scheme based on the characteristics of remote sensing cloud user behavior and Bayesian network algorithm. The scheme discussed the users' behavior authentication mechanism of remote sensing cloud platform, and according to the users' behavior characteristics, the scheme established an authentication set on users’ behavior. Combining with the predictive characteristics of bayesian network algorithm and user behavior properties to set up a Bayesian network model for authentication grade prediction. The weight information of user behavior attributes analyzed in this model is applied to the user grade prediction algorithm, which made the algorithm more secure and accurate for remote sensing cloud user authentication safer accurately, so as to realize the prediction of user behavior authentication level. Simulation examples show that the model is effective to identify untrusted users accurately, and can ensure the security of remote sensing cloud platform.
Subjects: Energy Science >> Geography of Energy submitted time 2016-12-26 Cooperative journals: 《中国科学院院刊》
Abstract：“胡焕庸线”是我国自黑龙江瑷珲至云南腾冲呈北东—南西走向延伸的人口密度分界线，其形成和发展与自然条件诸如地形、地貌、气候、水文等要素密切相关，更与社会、经济及人类活动相关。面向中国的经济与社会可持续发展，李克强总理提出了“胡焕庸线”“该不该破?能不能破?如何破?”三大问题。文章基于空间信息和相关时空数据的综合分析，通过典型地区的实地调查，提出了“胡焕庸线”应该破及其依据、“胡焕庸线”可以破及其理由、破解“胡焕庸线”的科学思路3点认识。在此基础上，进一步提出了破解“胡焕庸线”的4点建议，即：（1）多方并举提高西部水资源承载力，“三业”联动铸就西部大发展新模式；（2）打造中国绿色新能源基地，构建耗能密集-节水型高新技术产业；（3）“群”“带”结合走西部城镇化之路，挖潜革新促东西部均衡发展；（4）打造以人为本环境吸引各路人才，构建利益均沾机制保障创新供给。
Subjects: Biology >> Botany >> Plant ecology, plant geography submitted time 2016-05-03
Abstract： Aims To clarify the status of the carbon storage of forest ecosystem at different ages in Anhui Province, and to identify the maximum carbon sequestration potential of climax forest controlled by current natural environment conditions. Methods Field investigation method and BIOME4 model. Important findings At present, the total carbon storage of forests in Anhui Province is 714.5 Tg C, including 402.1 Tg C in vegetation and 312.4 Tg C in soil. Generally, the total and vegetation carbon density always present an increasing trend in the natural growth process of forest ecosystems in Anhui Province. Soil carbon density increases at the periods from young to near mature forests, but decreases gradually after near mature forest. Young and middle-aged forests account for 75% of the total forests area in Anhui Province, and there will be a potential additional carbon of 125.4 Tg C in case of young and middle-aged forests developing to near mature stand stage. Results from BIOME4 simulation showed that there will be a potential additional carbon of 245.7 Tg C (i.e. total carbon sequestration), including vegetation carbon sequestration of 153.7 Tg C, and soil carbon sequestration of 92 Tg C if forests develop to climax forest ecosystems in Anhui Province.
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