Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

[1]  J. W. Groenigen,et al.  Constrained optimisation of soil sampling for minimisation of the kriging variance , 1999 .

[2]  Steven Fortune,et al.  Voronoi Diagrams and Delaunay Triangulations , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[3]  Achim Dobermann,et al.  Sampling optimization based on secondary information and its utilization in soil carbon mapping , 2006 .

[4]  S. P. Anderson,et al.  Critical Zone Observatories: Building a network to advance interdisciplinary study of Earth surface processes , 2008, Mineralogical Magazine.

[5]  Alfred Stein,et al.  Constrained Optimization of Spatial Sampling using Continuous Simulated Annealing , 1998 .

[6]  J. W. van Groenigen.  Spatial Simulated Annealing for Optimizing Sampling , 1997 .

[7]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[8]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[9]  Don L. Stevens,et al.  Spatial properties of design-based versus model-based approaches to environmental sampling , 2006 .

[10]  Brian R. Johnson,et al.  NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure , 2010 .

[11]  Leon E. Borgman,et al.  Three-Dimensional, Frequency-Domain Simulations of Geological Variables , 1984 .

[12]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[13]  Alfred Stein,et al.  An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons , 2003 .

[14]  Baoping Yan,et al.  A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China , 2014, IEEE Geoscience and Remote Sensing Letters.

[15]  Robert Haining,et al.  Spatial Data Analysis: Theory and Practice , 2003 .

[16]  Pravesh Debba,et al.  Field Sampling Scheme Optimization Using Simulated Annealing , 2010 .

[17]  Jinfeng Wang,et al.  A review of spatial sampling , 2012 .

[18]  Jinfeng Wang,et al.  Modeling Spatial Means of Surfaces With Stratified Nonhomogeneity , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Evangelos A. Yfantis,et al.  Efficiency of kriging estimation for square, triangular, and hexagonal grids , 1987 .

[20]  D. J. Brus,et al.  Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion) , 1997 .

[21]  Zhengyuan Zhu,et al.  Spatial sampling design for parameter estimation of the covariance function , 2005 .

[22]  Kirk Martinez,et al.  Environmental Sensor Networks: A revolution in the earth system science? , 2006 .

[23]  Tomislav Hengl,et al.  A practical guide to geostatistical mapping of environmental variables , 2007 .

[24]  Robert B. Waide,et al.  Long-Term Ecological Research Network , 2013 .

[25]  Dale L. Zimmerman,et al.  Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction , 2006 .

[26]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[27]  Lara Fontanella,et al.  Optimal spatial sampling schemes for environmental surveys , 2004, Environmental and Ecological Statistics.

[28]  Gerard B. M. Heuvelink,et al.  Optimizing the spatial pattern of networks for monitoring radioactive releases , 2011, Comput. Geosci..

[29]  Feng Zhou,et al.  Scientometric analysis of geostatistics using multivariate methods , 2007, Scientometrics.

[30]  M. Cowles.  Statistical Computing , 2004 .

[31]  A. Warrick,et al.  Optimization of Sampling Locations for Variogram Calculations , 1987 .

[32]  David M. Holland,et al.  Complementary co-kriging: spatial prediction using data combined from several environmental monitoring networks , 2005 .

[33]  David Russo,et al.  Design of an Optimal Sampling Network for Estimating the Variogram , 1984 .

[34]  Irena Hajnsek,et al.  A Network of Terrestrial Environmental Observatories in Germany , 2011 .

[35]  W. G. Müller,et al.  Optimal designs for variogram estimation , 1999 .

[36]  Jinfeng Wang,et al.  A spatial sampling optimization package using MSN theory , 2011, Environ. Model. Softw..

[37]  Paul Switzer,et al.  Optimal Network Designs in Spatial Statistics , 2002 .

[38]  G. Matheron.  Principles of geostatistics , 1963 .

[39]  Terence L. van Zyl,et al.  The Sensor Web: systems of sensor systems , 2009, Int. J. Digit. Earth.