Data from: Spatial-Temporal Analysis of Environmental Data of North Beijing District Using Hilbert-Huang Transform

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Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

附件文件为关联文章提供补充数据集。温度、太阳辐射与水体是生态系统模型中的核心关键变量，可通过无线传感器网络（Wireless Sensor Network, WSN）进行采集测量。开展有效的数据分析，对于提取具有研究价值的时空信息至关重要。本研究基于希尔伯特-黄变换（Hilbert-Huang Transform, HHT）对无线传感器网络采集数据集展开分析，探究野外季节性环境条件的长期变化规律。该数据采集网络由36个无线节点组成，部署于中国北京CBD西北部的延庆区，覆盖面积达100平方公里，采集了2011年为期三个月的环境参数观测数据。本次分析采用经验模态分解/集合经验模态分解（Empirical Mode Decomposition/Ensemble Empirical Mode Decomposition, EMD/EEMD）将时序数据分解为有限个固有模态函数（Intrinsic Mode Function, IMFs）。通过希尔伯特-黄变换分析，成功揭示了数据集的时空分布特征。本研究表明，借助无线传感器网络与希尔伯特-黄变换，能够更深入地解析环境参数间的时空关联关系，具备良好的应用潜力。