Earth observation community has entered into the era of big data.Family of Landsat sensors have collected massive medium resolution satellite images,which are valuable for long-term land surface monitoring.In order to...Earth observation community has entered into the era of big data.Family of Landsat sensors have collected massive medium resolution satellite images,which are valuable for long-term land surface monitoring.In order to significantly reduce the magnitude of data processing for remote sensing data users,Landsat-based Ready to Use(RTU)products have been produced.Main RTU products,including orthorectified products,land surface reflectance,land surface temperature,large-area mosaic image,and standard image map products,are described.The resulting Landsat RTU products are hosted on the RSGS earth observation data sharing web site for free download(http://ids.ceode.ac.cn/rtu/).These new products will provide consistent,standardized,multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.In the coming years,CASEarth DataBank system will be constructed,which is an intelligent data service platform for providing not only the RTU products from multi-source satellite data,but also big earth data analysis methods.展开更多
A healthy balanced diet and a healthy lifestyle are very closely linked.Whichever the biological link is,it is overwhelming to understand.Modifications in how food is served,divided up,and supervised,such as the intro...A healthy balanced diet and a healthy lifestyle are very closely linked.Whichever the biological link is,it is overwhelming to understand.Modifications in how food is served,divided up,and supervised,such as the introduction of nutritional hygiene standards,food handling practices,and the entry of macro and micronutrients,have had a big impact on human health in the last few decades.Growing evidence indicates that our gut microbiota may affect our health in ways that are at least in part influenced by our diet and the ingredients used in the preparation of our food and drinks,as well as other factors.As a new problem,this one is getting a lot of attention,but it would be hard to figure out how the gut microbiota and nutrition molecules work together and how they work in certain situations.Genetic analysis,metagenomic characterization,configuration analysis of foodstuffs,and the shift to digital health information have provided massive amounts of data that might be useful in tackling this problem.Machine learning and deep learning methods will be employed extensively as part of this research in order to blend complicated data frames and extract crucial information that will be capable of exposing and grasping the incredibly delicate links that prevail between diet,gut microbiome,and overall wellbeing.Nutrition,well-being,and gut microorganisms are a few subjects covered in this field.It takes into account not only databases and high-speed technology,but also virtual machine problem-solving skills,intangible assets,and laws.This is how it works:Computer vision,data mining,and analytics are all discussed extensively in this study piece.We also point out limitations in existing methodologies and new situations that discovered in the context of current scientific knowledge in the decades to come.We also provide background on"bioinformatics"algorithms;recent developments may seem to herald a revolution in clinical research,pushing traditional techniques to the sidelines.Furthermore,their true potential rests in their a展开更多
Background： Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be use...Background： Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be used to assess the potential impact of inundation by a proposed hydroelectricity dam in the Mokihinui gorge, New Zealand, on representation of natural forests. Specifically we ask： 1） How well are the types of forest represented Locally, regionally, and nationally; and 2） How does the number of distinct communities （i.e. beta diversity） in the target catchment compare with other catchments nationally？ Methods： For local and regional comparisons plant species composition was recorded on 45 objectively located 400 m2 vegetation plots established in each of three gorges, with one being the proposed inundation area of the Mokihinui lower gorge. The fuzzy classification framework of noise clustering was used to assign these plots to a specific alliance and association of a pre-existing national-scale classification. NationaLly, we examined the relationship between the number of alliances and associations in a catchment and either catchment size or the number of plots per catchment by fitting Generalised Additive Models. Results： The four alliances and five associations that were observed in the Mokihinui lower gorge arepresent in the region but limited locally. One association was narrowly distributed nationally, but is the mostfrequent association in the Mokihinui lower gorge; inundation may have consequences of national importance to its long-term persistence. That the Mokihinui lower gorge area had nearly twice as many plots that could not be assigned to pre- existing alliances and associations than either the Mokihinui upper or the Karamea lower gorges and proportionally more than the national dataset emphasises the compositional distinctiveness of this gorge. These outlier plots in the Mokihinui lower gorge may be unsorted assemblages of species or reflect sampling bias or that native- 展开更多
基金This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19090300]the National Key Research and Development Programs of China[grant numbers 2016YFA0600302 and 2016YFB0501502]+1 种基金the program of the National Natural Science Foundation of China[grant number 61401461]135 Strategy Planning of Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences.
文摘Earth observation community has entered into the era of big data.Family of Landsat sensors have collected massive medium resolution satellite images,which are valuable for long-term land surface monitoring.In order to significantly reduce the magnitude of data processing for remote sensing data users,Landsat-based Ready to Use(RTU)products have been produced.Main RTU products,including orthorectified products,land surface reflectance,land surface temperature,large-area mosaic image,and standard image map products,are described.The resulting Landsat RTU products are hosted on the RSGS earth observation data sharing web site for free download(http://ids.ceode.ac.cn/rtu/).These new products will provide consistent,standardized,multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.In the coming years,CASEarth DataBank system will be constructed,which is an intelligent data service platform for providing not only the RTU products from multi-source satellite data,but also big earth data analysis methods.
文摘A healthy balanced diet and a healthy lifestyle are very closely linked.Whichever the biological link is,it is overwhelming to understand.Modifications in how food is served,divided up,and supervised,such as the introduction of nutritional hygiene standards,food handling practices,and the entry of macro and micronutrients,have had a big impact on human health in the last few decades.Growing evidence indicates that our gut microbiota may affect our health in ways that are at least in part influenced by our diet and the ingredients used in the preparation of our food and drinks,as well as other factors.As a new problem,this one is getting a lot of attention,but it would be hard to figure out how the gut microbiota and nutrition molecules work together and how they work in certain situations.Genetic analysis,metagenomic characterization,configuration analysis of foodstuffs,and the shift to digital health information have provided massive amounts of data that might be useful in tackling this problem.Machine learning and deep learning methods will be employed extensively as part of this research in order to blend complicated data frames and extract crucial information that will be capable of exposing and grasping the incredibly delicate links that prevail between diet,gut microbiome,and overall wellbeing.Nutrition,well-being,and gut microorganisms are a few subjects covered in this field.It takes into account not only databases and high-speed technology,but also virtual machine problem-solving skills,intangible assets,and laws.This is how it works:Computer vision,data mining,and analytics are all discussed extensively in this study piece.We also point out limitations in existing methodologies and new situations that discovered in the context of current scientific knowledge in the decades to come.We also provide background on"bioinformatics"algorithms;recent developments may seem to herald a revolution in clinical research,pushing traditional techniques to the sidelines.Furthermore,their true potential rests in their a
基金funded by Meridian Energy Limited,New Zealandby Core funding for Crown Research Institutes from the New Zealand Ministry of Business,Innovation and Employment’s Science and Innovation Group
文摘Background： Ecosystem representation is one key component in assessing the biodiversity impacts of land-use changes that will irrevocably alter natural ecosystems. We show how detailed vegetation plot data can be used to assess the potential impact of inundation by a proposed hydroelectricity dam in the Mokihinui gorge, New Zealand, on representation of natural forests. Specifically we ask： 1） How well are the types of forest represented Locally, regionally, and nationally; and 2） How does the number of distinct communities （i.e. beta diversity） in the target catchment compare with other catchments nationally？ Methods： For local and regional comparisons plant species composition was recorded on 45 objectively located 400 m2 vegetation plots established in each of three gorges, with one being the proposed inundation area of the Mokihinui lower gorge. The fuzzy classification framework of noise clustering was used to assign these plots to a specific alliance and association of a pre-existing national-scale classification. NationaLly, we examined the relationship between the number of alliances and associations in a catchment and either catchment size or the number of plots per catchment by fitting Generalised Additive Models. Results： The four alliances and five associations that were observed in the Mokihinui lower gorge arepresent in the region but limited locally. One association was narrowly distributed nationally, but is the mostfrequent association in the Mokihinui lower gorge; inundation may have consequences of national importance to its long-term persistence. That the Mokihinui lower gorge area had nearly twice as many plots that could not be assigned to pre- existing alliances and associations than either the Mokihinui upper or the Karamea lower gorges and proportionally more than the national dataset emphasises the compositional distinctiveness of this gorge. These outlier plots in the Mokihinui lower gorge may be unsorted assemblages of species or reflect sampling bias or that native-
