MapReduce functions to remote sensing distributed data processing—Global vegetation drought monitoring as example

Summary Global change models for different applications are developed, according to the principle of remote sensing technology. Data for these models are generally remote sensing image, which is multiplatform, multidimentional, multiband, and multisource. Moreover, such data may be in different part...

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Vydáno v:Software, practice & experience Ročník 48; číslo 7; s. 1352 - 1367
Hlavní autoři: Zou, Quan, Li, Guoqing, Yu, Wenyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.07.2018
Témata:
Cloud computing
Computer architecture
Computer networks
data abstraction
Data processing
Detection
Distributed processing
Drought
Environmental monitoring
global vegetation drought‐monitoring model
MapReduce
Parallel programming
Remote sensing
Satellite imagery
Vegetation
ISSN:0038-0644, 1097-024X
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Shrnutí:Summary Global change models for different applications are developed, according to the principle of remote sensing technology. Data for these models are generally remote sensing image, which is multiplatform, multidimentional, multiband, and multisource. Moreover, such data may be in different parts of the world and perhaps up to terabyte or petabyte level. Therefore, a data‐intensive computing problem in the global change has emerged. Distributed computing infrastructures are suitable to store large‐scale datalike satellite images that have to be written only once and read frequently. The emergence of the cloud computing technology brings new information architecture, and global change models implemented in the cloud platform provide users with stable, effective, on‐demand cloud computing services. In this paper, the experiment is carried out on the cloud framework based on open cloud computing platform‐Hadoop. In addition, on this framework, it achieves a prototype example for monitoring global vegetation drought conditions. Oriented to a variety of remote sensing data, we propose an data format to achieve the unified description of remote sensing data. The data ion is to discretize the multidimensional remote sensing data for easy‐distributed storage and computation. Using MapReduce paradigm, the complexity of remote sensing algorithms is resolved. Experimental results show that based on the parallel programming method, good scalability changing with the amount of processed data in the Hadoop distributed environment.
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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2578