A pole-oriented discrete global grid system: Quaternary quadrangle mesh

Zhou, Mengyun; Jin, Chen; Gong, Jianya

doi:10.1016/j.cageo.2013.08.012

Cited by 14 publications

(10 citation statements)

References 18 publications

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“…Dutton described the Octahedral Quaternary Triangular Mesh (O-QTM) for positional data descriptions, which takes shape as a regular hierarchical triangulation of an octahedron embedded in a planet; it densifies four child triangles developed within each existing one, and each has a unique numeric address [18,19]. Zhou et al proposed a new pole-oriented DGGS (the Quaternary Quadrangle Mesh (QQM)) that uses semi-hexagon (a type of quadrangle) grids in the polar regions and rectangular grids elsewhere [20]. Despite their advantages of balanced projection deformation, regular shape, isotropy, and uniformity, the regular polyhedron-based grid systems are generally inefficient in data management, conversion and integration.…”

Section: Introductionmentioning

confidence: 99%

RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

Liu

Yao

et al. 2018

Remote Sensing

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In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

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Section: Introductionmentioning

confidence: 99%

RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

Liu

Yao

et al. 2018

Remote Sensing

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“…As multi-source sensor data, multi-type data, and duplicate data that are repeatedly covered by the same sensor need to be collaboratively produced in the same production task, the production reference of the product is required to determine the subdivision specification of the product (resolution, size, starting point of the subdivision, etc.) [28], [29]. Different data are then organized according to the same subdivision specification.…”

Section: B Data Integrationmentioning

confidence: 99%

Dps-MuSyQ: A Distributed Parallel Processing System for Multi-Source Data Synergized Quantitative Remote Sensing Products Producing

Tang

2020

IEEE Access

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With the development of earth observation technologies and the construction of earth observation systems, an increasing amount of remote sensing data are being obtained. These provide the datasets required for research on remote sensing monitoring across large areas. To compensate for the shortcomings of global and large-area temporal monitoring data, synergized computing using multi-source remote sensing data can improve the accuracy and temporal resolution of remote sensing monitoring. However, remote sensing data are drawn from multiple sources and multiple scales, and have a complex structure and large volume; in addition, the nested system architecture of multi-source synergized remote sensing products makes the design of large-scale multi-source synergized remote sensing monitoring systems difficult. In this paper, we describe the design and implementation of a distributed parallel processing system for multi-source data synergized quantitative remote sensing based on a distributed cluster platform. The system integrates the algorithms normalizing more than 30 kinds of data sources and producing 40 quantitative remote sensing products. The system also connects a number of centers for satellite data, serves for several applications, and implements dynamic expansion integration for highly efficient quantitative remote sensing products. The system has produced approximately 50 TB of quantitative remote sensing products, and the application of these data to agriculture, forestry, the environment, and water conservation has resulted in very positive effects. INDEX TERMS Multi-source data synergies, quantitative remote sensing, task parallelism, data integration, distributed system.

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“…A series of indicators used to evaluate the thing constitute the evaluation index set, of which the weights is not the same. The evaluation index set is a mapping of the evaluation elements set [29,32],where there are multiple mapping index sets for an evaluation element set. There are currently two typical standards for the principle of establishing an index system: one is comprehensive, non-overlapping (or redundant), of which indicators are easy to obtain; the second is scientificity, rationality and applicability [13,33].…”

Section: Accuracy Evaluation Of Hexagonal Grid Datamentioning

confidence: 99%

“…The checkpoint precision evaluation method can be used to extract the DEM valley line represented by a regular hexagonal grid, in which several geomorphic feature points in the study area and the edge are randomly selected as the detection points, and the hexagonal DEM and quadrilateral DEM established by the grid elevation error evaluation respectively data accuracy [9,26,27]. A series of geometric indicators when evaluating the properties of different grids, including the change rate of cellar area , rate of change of cellar angle , and change rate of cellar side length [17,28,29].…”

Section: Introductionmentioning

confidence: 99%

A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

Ying¹,

G²,

Yao³

et al. 2020

Preprint

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With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

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A pole-oriented discrete global grid system: Quaternary quadrangle mesh

Cited by 14 publications

References 18 publications

RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

Dps-MuSyQ: A Distributed Parallel Processing System for Multi-Source Data Synergized Quantitative Remote Sensing Products Producing

A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

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