地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (6): 874-881.doi: 10.3724/SP.J.1047.2014.00874

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基于MapReduce的空间敏感性分析并行算法设计

李帆1,2(), 何洪林1,*(), 任小丽1,2, 张黎1, 路倩倩1,2, 于贵瑞1   

  1. 1. 中国科学院地理科学与资源研究所 生态系统网络观测与模拟重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2013-12-28 修回日期:2014-02-24 出版日期:2014-11-10 发布日期:2014-11-01
  • 通讯作者: 何洪林 E-mail:davidlee0408@gmail.com;hehl@igsnrr.ac.cn
  • 作者简介:

    作者简介:李 帆(1987-),男,山西太原人,硕士生,研究方向为生态信息学。E-mail:davidlee0408@gmail.com

  • 基金资助:
    科学院先导专项“应对气候变化的碳收支认证及相关问题”(XDA05050600);国家生态系统观测评估技术系统集成研究与示范(2013BAC03B00);MSR-CNIC Windows Azure 合作计划项目“基于Windows Azure的陆地生态系统碳水通量估算与不确定性研究”;国家科技部环保公益性行业科研专项(gyh5031103)

Research on Spatial Sensitivity Analysis Using Parallel Algorithm Based on MapReduce

LI Fan1,2(), HE Honglin1,*(), REN Xiaoli1,2, ZHANG Li1, LU Qianqian1,2, YU Guirui1   

  1. 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-12-28 Revised:2014-02-24 Online:2014-11-10 Published:2014-11-01
  • Contact: HE Honglin E-mail:davidlee0408@gmail.com;hehl@igsnrr.ac.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

近年来,随着遥感空间数据广泛应用于生态系统,推动了区域尺度生态遥感参数模型的发展。敏感性分析对识别模型关键参数,降低模型不确定性和完善模型具有重要作用。区域尺度的生态遥感参数模型,在进行模型参数敏感性分析时,由于涉及到空间数据的复杂运算,单机环境无法满足快速分析的要求。为了提高生态遥感参数模型空间敏感性分析效率,本文以青藏高原为研究区域,利用植被光合模型VPM(Vegetation Photosynthesis Model)和开源云计算平台Hadoop,设计和实现了基于Sobol′的生态遥感参数模型空间敏感性分析并行算法,并在实验室集群环境下进行算法分析,验证了算法的有效性和适用性。该算法的核心是利用MapReduce并行编程技术,对空间敏感性分析中的地图抽样和模型迭代过程进行任务分割,将分割后的子任务分配至不同的计算节点进行并行计算。实验表明,本文提出的并行策略,能有效缩短地图抽样和模型迭代计算时间,相比于单机算法,并行算法的运行速度提高了14倍左右。

关键词: 遥感参数模型, 空间敏感性分析, Sobol′, MapReduce

Abstract:

In recent years, with the rapid development of remote sensing technology, the spatial data represented by remote sensing images is widely used in ecosystem modeling, which promoted the development of ecological remote sensing parametric model in the regional scale. Sensitivity analysis is a key step for ecosystem model uncertainty quantification. It can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. Due to the intensive computation of spatial data during the sensitivity analysis, the traditional stand-alone environment cannot meet the requirements of rapid analysis for the regional scale remote sensing parametric model. This study designed and realized a parallel algorithm of Sobol′ spatial sensitivity analysis utilizing Hadoop, which is an open source cloud computing platform, based on VPM (Vegetation Photosynthesis Model). In order to verify the efficiency of the algorithm, we designed a comparison experiment to compare the efficiency differences of the traditional serial algorithm and the parallel algorithm. The parallel programming technology we used in this research was MapReduce, which divided the processes of map sampling and the iterative calculation during the spatial sensitivity analysis into subtasks, and assigned them to multiple computing nodes for parallel computing. The numerical experiment showed that the parallel strategy proposed in this study effectively shortened the time of model iterative calculations and significantly improved the efficiency of spatial sensitivity analysis for ecological remote sensing parametric model. Compared with the serial algorithm, the computing efficiency of the parallel algorithm was enhanced by 14 times.

Key words: remote sensing parametric model, spatial sensitivity analysis, Sobol′, MapReduce