Reliability of GPS/GNSS-based positioning in a forestry environment

R. Cuneyt Erenoglu

Journal of Forestry Research ›› 2016, Vol. 28 ›› Issue (3) : 605 -614.

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Journal of Forestry Research ›› 2016, Vol. 28 ›› Issue (3) : 605 -614. DOI: 10.1007/s11676-016-0332-0
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Reliability of GPS/GNSS-based positioning in a forestry environment

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Abstract

The critical environment is one of the main insufficient to positioning. Geodetic observing systems such as the global positioning system (GPS) and the global navigation satellite systems (GNSS) are routinely used to estimate the contaminating effects by critical environment. In an effort to define the accuracy and reliability of GPS/GNSS positioning, we investigated the data having contaminating effects due to forestry environment. Some reliability criteria and geometric concepts were defined and then examined by them. Two sets of data were collected in open sky and closed canopy separately. The analysis of the observed data was performed using the reliability criteria and geometric concepts. The accuracy and reliability of positioning strongly depended on the canopy ratio and satellite availability. The minimum detectable error on baseline was estimated about 2.5 mm under closed canopy. The number of observable satellites and minimal detectable errors were computed for each epoch. The minimal biases on estimated baselines, bias-to-noise ratios for estimating baseline components and probability of success of the integer ambiguity solution were defined in case of forest canopy. Finally, geometric quality could be achieved using the factors of dilution of precision. Thus, the presented accuracy and reliability concepts fulfill the requirement proposed by the global geodetic observing system in forest environment.

Keywords

Accuracy / Canopy / Forestry / GPS/GNSS / Reliability

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R. Cuneyt Erenoglu. Reliability of GPS/GNSS-based positioning in a forestry environment. Journal of Forestry Research, 2016, 28(3): 605-614 DOI:10.1007/s11676-016-0332-0

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References

[1]

Baarda W. Statistical concepts in Geodesy, 1967, Delft: Netherlands Geodetic Commission.

[2]

Baarda W. A testing procedure for use in geodetic networks, 1968, Delft: Netherlands Geodetic Commission.

[3]

Bakuła M. An approach to reliable rapid static GNSS surveying. Surv Rev, 2012, 44(327): 265-271.

[4]

Clarke B. Aviator’s guide to GPS, 1994, New York: McGraw-Hill.

[5]

Hasegawa H, Yoshimura T. Estimation of GPS positional accuracy under different forest conditions using signal interruption probability. J For Res, 2007, 12: 1-7.

[6]

Hofmann-Wellenhof B, Lichtenegger H, Collins J (2001) GPS: theory and practice, 5th revised edn. Springer, Wien

[7]

Huber PJ. Robust estimation of a location parameter. Ann Math Statistics, 1964, 35(1): 73-101.

[8]

James L, Watson D, Hansen W. Using LiDAR data to map gullies and headwater streams under forest canopy. South Carolina, USA. Catena, 2007, 71: 132-144.

[9]

Kaplan ED. Understanding GPS: principles and applications, 1996, Boston: Artech House Publishers.

[10]

Koch KR. Parameter estimation and hypothesis testing in linear models, 1999, Berlin: Springer

[11]

Kuang SL. Geodetic network analysis and optimal design, 1996, Michigan: Ann Arbor Press Inc..

[12]

Leick A. GPS satellite surveying, 2003 3 New York: Wiley.

[13]

Li B, Shen Y, Feng Y, Gao W, Yang L. GNSS ambiguity resolution with controllable failure rate for long baseline network RTK. J Geodesy, 2014, 88(2): 99-112.

[14]

Misra P, Enge P. Global positioning system: signals, measurements, and performance, 2001, Lincoln: Ganga-Jumuna Press.

[15]

Parkins A. Increasing GNSS RTK availability with a new single-epoch batch partial ambiguity resolution algorithm. GPS Solut, 2011, 15(4): 391-402.

[16]

Rizos C. Principle and practice of GPS. Monograph 17, 1997, Sydney: School of Geomatic Engineering, The University of New South Wales.

[17]

Strang G, Borre K. Linear Algebra, Geodesy and GPS, 1997, Wellesley: Wellesley-Cambridge Press.

[18]

Tachiki Y, Yoshimura T, Hasegawa H, Mita T, Sakai T, Nakamura F. Effects of polyline simplification of dynamic GPS data under forest canopy on area and parameter estimations. J For Res, 2005, 10: 419-427.

[19]

Teunissen PJG. The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation. J Geodesy, 1995, 70(1–2): 65-82.

[20]

Teunissen PJG. The LAMBDA method for the GNSS compass. Artif Satell, 2007, 41(3): 89-103.

[21]

Teunissen PJG, Kleusberg A (1998) GPS for Geodesy, 2nd enlarged edn. Springer, Heidelberg

[22]

Teunissen PJG, Khodabandeh A. Review and principles of PPP-RTK methods. J Geodesy, 2015, 89(3): 217-240.

[23]

Verhagen S (2004) The GNSS integer ambiguities: estimation and validation, Ph.D. Thesis Report, the Department of Mathematical Geodesy and Positioning of the Delft University of Technology, Thijsseweg

[24]

Wang J, Feng Y. Reliability of partial ambiguity fixing with multiple GNSS constellations. J Geodesy, 2013, 87(1): 1-14.

[25]

Wing MG, Eklund A. Elevation measurement capabilities of consumer-grade global positioning system (GPS) receivers. J For, 2007, 105: 91-94.

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