Abstract
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.
Highlights
Temperature, solar radiation and water are considered to be the most important microclimatic drivers that modulate the magnitude and frequency of carbon fluxes [1] and have always been important variables in ecosystem models [2, 3]. Adequate understanding of these field environment data as functions of space and time is necessary for developing empirical models of soil-vegetation-atmosphere transfer [4]; it could help in expert system design of precision agriculture [5] and quantified the uncertainty caused by in-situ method for satellite remote sensing validation and calibration [6]
empirical mode decomposition (EMD) separates input data sets into finite and often small number of intrinsic mode functions (IMFs) at different scales plus a residue, each IMF with its average circle, represents decomposed characteristic of the original time series at this time scale and residue shows the tendency of the original time series; Hilbert spectral analysis (HSA) on the IMFs provides a possible extraction of instantaneous frequencies and amplitudes (IF and IA), with which a time-frequency-energy distribution model could be built [24]
All sensor measurements from each wireless sensor networks (WSN) nodes were decomposed separately for each of environmental parameters
Summary
Temperature, solar radiation and water are considered to be the most important microclimatic drivers that modulate the magnitude and frequency of carbon fluxes [1] and have always been important variables in ecosystem models [2, 3]. Adequate understanding of these field environment data as functions of space and time is necessary for developing empirical models of soil-vegetation-atmosphere transfer [4]; it could help in expert system design of precision agriculture [5] and quantified the uncertainty caused by in-situ method for satellite remote sensing validation and calibration [6]. Little effort has been made for a complete examination of these three parameters (LST, soil moisture and PAR) in a same region and the spatial-temporal relationships among these three parameters
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