| 分布式MODIS植被供水指数生产模型研究与实现 |
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| 引用本文： | 孟令奎,朱会玲,谢文君,胡艺杰,张文.分布式MODIS植被供水指数生产模型研究与实现[J].武汉大学学报(信息科学版),2018,43(5):676-683. |
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| 作者姓名： | 孟令奎 朱会玲 谢文君 胡艺杰 张文 |
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| 作者单位： | 1.武汉大学遥感信息工程学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家重点研发计划2017YFC0405806高分辨率对地观测系统重大专项（民用部分）08-Y30B07-9001-13/15 |
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| 摘 要： | 遥感监测已成为旱情监测的重要手段，其中植被供水指数（vegetation supply water index，VSWI）产品是进行遥感旱情监测的重要参考依据，但其生产涉及的数据量大，处理周期长，严重影响监测数据处理的及时性。面向分布式计算环境，提出了一种基于开放服务网关协议（open services gateway initiative，OSGI）的分布式中分辨率成像光谱仪（Moderate-Resolution Imaging Spectroradiometer，MODIS）植被供水指数产品生产模型（OSGI-based VSWI distributed production model，ODPM），通过该模型将MODIS影像的植被供水指数产品生产算法转化成OSGI的Bundle组件，使其能够在分布式OSGI的平台中进行动态的部署与安装，充分利用局域网内的计算资源，实现快速的遥感数据处理与监测产品生产。利用中国7大流域数据对模型的稳定性进行实验，结果表明，相对传统的单机处理模式，该模型的运算速度提升3倍左右，内存占用减少大约2/3倍，并具有良好的计算稳定性。ODPM模型在提升数据处理部门的工作效率方面具有重要意义，可在海量遥感数据处理、大范围遥感监测产品生产领域发挥显著的作用。
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| 关 键 词： | OSGI ODPM模型 旱情监测 VSWI产品生产 分布式 内存占用 总耗时 |
| 收稿时间： | 2016-03-15 |
Distributed Production Method for MODIS VSWI Based on OSGI |
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| Institution: | 1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China2.Information Center, Ministry of Water Resources, Beijing 100053, China3.School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou 450045, China |
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| Abstract: | As the drought occurred frequently, and remote sensing technology matures, the drought remote sensing monitoring has become an important means of drought monitoring, and the vegetation supply water index(VSWI) products is an important reference for drought monitoring, but its production involves large amount of data, and the processing cycle is long, which influence the timeliness seriously. Based on OSGI service, a distributed production model of VSWI is proposed, which turn the VSWI algorithm into an OSGI module called Bundle. This Bundle can be deployed and installed dynamically in a distributed OSGI platform, which can make full use of the resources in LAN, and be helpful to raise up the operating rate of image processing, as well as lessen the memory footprint, which have been proved in this paper. This model will play an important role in massive remote sensing data processing and emergency monitoring. |
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