Machine Learning Techniques Applied to the Assessment of GPS Accuracy under the Forest Canopy

The geographic location of points using global positioning system (GPS) receivers is less accurate in forested environments than in open spaces because of signal loss and the multipath effect of tree trunks, branches, and leaves. This has been confirmed in studies that have concluded that a relationship exists between measurement accuracy and certain variables that characterize forest canopy, such as tree density, basal area, and biomass volume. However, the practical usefulness of many of these studies is limited because they are often limited to describing associations between the variables and mean errors in the measurement interval, when measurements should be made in real time and in intervals of seconds. In this work, machine learning techniques were applied to build mathematical models that would associate observation error and GPS signal and forest canopy variables. The results reveal that the excessive complexity of the signal prevents accurate measurement of observation error, especially in the ...

[1]  E. Næsset,et al.  Contributions of differential GPS and GLONASS observations to point accuracy under forest canopies , 2000 .

[2]  Shane Ward,et al.  The Effects of Peripheral Canopy on DGPS Performance on Forest Roads , 2001 .

[3]  P. Coppin,et al.  Impact of forest canopy on quality and accuracy of GPS measurements , 1999 .

[4]  José Ramón Rodríguez-Pérez,et al.  Assessment of Low-Cost GPS Receiver Accuracy and Precision in Forest Environments , 2007 .

[5]  E. Næsset.  Effects of Differential Single- and Dual-Frequency GPS and GLONASS Observations on Point Accuracy under Forest Canopies , 2001 .

[6]  Omid Omidvar,et al.  Neural Networks and Pattern Recognition , 1997 .

[7]  Mieczysław Bakuła,et al.  Performance of RTK Positioning in Forest Conditions: Case Study , 2009 .

[8]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[9]  Fernando Castedo-Dorado,et al.  Development of a stand density management diagram for radiata pine stands including assessment of stand stability , 2009 .

[10]  I. Sawaguchi,et al.  Positioning precision and sampling number of DGPS under forest canopies , 2003, Journal of Forest Research.

[11]  Francois Blais,et al.  TRACEABLE 3D IMAGING METROLOGY: EVALUATION OF 3D DIGITIZING TECHNIQUES IN A DEDICATED METROLOGY LABORATORY , 2007 .

[12]  Isao Sawaguchi,et al.  A study of the effects of stems and canopies on the signal to noise ratio of GPS signals , 2005, Journal of Forest Research.

[13]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[16]  Christian Piedallu,et al.  Effects of Forest Environment and Survey Protocol on GPS Accuracy , 2005 .

[17]  Yi Jao Chen,et al.  Clarifying the relationship between quality of global positioning system data and precision of positioning , 2010 .

[18]  Timothy P. McDonald,et al.  Using the global positioning system to map disturbance patterns of forest harvesting machinery , 2002 .

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[20]  Allan Pinkus,et al.  Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  H. Hasegawa,et al.  Comparing the precision and accuracy of GPS positioning in forested areas , 2003, Journal of Forest Research.

[23]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[24]  Glen Murphy,et al.  Developing methods for pre-harvest inventories which use a harvester as the sampling tool , 2006 .

[25]  E. Naesset,et al.  Assessing Point Accuracy of DGPS Under Forest Canopy Before Data Acquisition, in the Field and After Postprocessing , 2002 .

[26]  John P. Fulton,et al.  Automated time study of skidders using global positioning system data , 2005 .

[27]  H. Hasegawa,et al.  Estimation of GPS positional accuracy under different forest conditions using signal interruption probability , 2007, Journal of Forest Research.

[28]  T. Yoshimura,et al.  Application of dual-frequency GPS receivers for static surveying under tree canopies , 2003, Journal of Forest Research.

[29]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[30]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[31]  Tetsuro Sakai,et al.  Effects of polyline simplification of dynamic GPS data under forest canopy on area and perimeter estimations , 2005, Journal of Forest Research.

[32]  J. Álvarez-González,et al.  Temporal variations and distribution of carbon stocks in aboveground biomass of radiata pine and maritime pine pure stands under different silvicultural alternatives , 2006 .