Duluth Laboratories & Administration 5013 Miller Trunk Highway Duluth, Minnesota 55811 Coleraine Laboratories One Gayley Avenue P.O. Box 188 Coleraine, Minnesota 55722
By Michael Joyce, Ph.D. Ron Moen, Ph.D. Report Number: NRRI/TR -2018/28, Release 1.0
SUMMARY
One of the main factors that affect GPS location accuracy is the type of GPS receiver being used. In general, more expensive receivers (e.g., mapping-grade or survey-grade receivers) provide better accuracy, and GPS users must balance GPS receiver cost with location accuracy when determining which receiver to use. Applications of GPS often require use of GPS receivers in less than ideal conditions while GPS manufacturers often report accuracy specifications that can be expected under ideal conditions. Forest canopies reduce GPS accuracy by interfering with signal transmission between GPS satellites and the GPS receiver and causing multipath errors. When GPS receivers are to be used in forest conditions and accuracy thresholds must be met, it is important to conduct accuracy testing in forest conditions rather than relying on accuracy specifications provided by the manufacturer.
We tested the accuracy of the SXBlue II + GNSS, a modular, mapping-grade GPS receiver, under forest canopies in northeastern Minnesota. We estimated cumulative accuracy to evaluate the relationship between collection period and accuracy. GPS test sites covered a range of canopy conditions. We compared accuracy among sites to determine how canopy closure influenced location accuracy. Finally, we compared post-hoc methods to evaluate accuracy based on characteristics of the sites and acquired GPS fixes. The SXBlue II + GNSS receiver typically provided meter or sub-meter accuracy, even under forest canopy. Maximum accuracy was achieved after 10- 30 minutes. Accuracy was lower at sites with higher canopy closure values. In sites with canopy closure >65%, maximum accuracy was reduced to 1.5 m. Post-hoc filtering to remove outliers did not improve accuracy. There was a strong, positive relationship between 50% CEP, a measure of location precision, and accuracy, suggesting that 50% CEP can be used for post-hoc accuracy assessment. Our results suggest that the SXBlue II + GNSS provides sufficient accuracy for a wide range of applications, including those that require GPS location measurement in forest conditions
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INTRODUCTION
With the increasing availability of LiDAR data for forestry and wildlife applications, precise geographic positioning is critical to ensure features of interest (e.g., field plot locations, animal locations, etc.) can be compared directly with the corresponding LiDAR data or derived products. Spatial overlap is affected by both global positioning system (GPS) accuracy and horizontal accuracy of LiDAR data. GPS accuracy is usually a bigger source of positional error (White et al. 2013). LiDAR data often have horizontal accuracy within 1 meter. Relatively inexpensive recreation-grade (also known as consumer-grade) GPS receivers typically have THE accuracy of about 9 meters when used under closed forest canopy (Wing and Eklund 2007, Wing 2008).
Survey-grade GPS receivers can achieve centimeter-level accuracy, but tend to be cost-prohibitive for many applications (Laes et al. 2011, White et al. 2013). Guidelines for forest inventory modeling using LiDAR typically recommend using mapping-grade GPS receivers capable of obtaining locations with sub-meter accuracy under forest canopy (Laes et al. 2011). Mapping-grade receivers cost less than survey-grade receivers, and they can typically achieve sub-meter to 2 m accuracy (White et al. 2013). Modular mapping-grade GPS receivers now available are less expensive but still as accurate.
GPS position error is typically caused by interference with the signal being broadcast from the satellite and received by the GPS unit. Given that the GPS satellites are about 20,000 km above the earth, it is not surprising that interference occurs. Forest canopies obstruct the signal, especially when moisture is present (Johnson and Barton 2004, Edson and Wing 2012), and can also reflect the signal and cause multipath interference in which the receiver has difficulty identifying the signal amongst the noise (Wing 2008). For these reasons, using GPS in forested environments is often associated with reduced accuracy. One solution to improve accuracy is to use differential correction with a base station. If a base station is located at a known location the error in the position can be calculated, although base stations are not typically located under a forest canopy. If the same satellites are used, the error should be the same at the unknown location where a GPS unit is, and the result of this differential correction is higher precision. Differential GPS approaches include both real-time differential correction, for which the GPS unit receives corrections in real time from a base station, and post-processing when corrections are applied after the GPS data have been acquired.
Another solution to reduce GPS error is the use of Space-Based Augmentation Systems (SBAS), such as the Wide Area Augmentation System (WAAS) that covers Central and North America. SBAS utilizes a network of ground reference stations with known locations which provide information to a master station that calculates corrections that can be applied over a wide area. SBAS calculates separate correction factors for different error sources (e.g., ionospheric errors, GPS satellite timing errors, GPS satellite orbit errors) rather than calculating the total effect of these factors. Corrections are broadcast using a constellation of geostationary satellites, allowing use of SBAS for real-time correction without the need for communicating with a differential GPS base station.
Our objectives were to test the accuracy of a modular, mapping-grade GPS receiver under forest canopies in northeastern Minnesota. The GPS receiver we tested is capable of sub-meter GPS accuracy under ideal conditions but has not been tested in conditions under a forest canopy. We (1) evaluated the relationship between the length of the data collection period and accuracy, (2) identified the effect of tree canopy closure on the accuracy, and (3) tested potential post-hoc methods to evaluate accuracy based on site characteristics or GPS data. Our results are specific to the receiver and software that we used, but could logically be extended to other GPS units in similar conditions.
METHODS
Field Testing
We tested the horizontal accuracy of the SXBlue II + GNSS receiver (Geneq Inc., Montreal, Quebec, Canada), a compact Global Navigation Satellite System (GNSS) receiver. In ideal conditions with an unobstructed view of the sky, the SXBlue II + GNSS should provide sub-meter horizontal accuracy 95% of the time (Geneq Inc., 2014). In many locations the view of the sky is obstructed by trees, hillsides, or other structures. We determined expected accuracies when using the SXBlue II receiver under forest canopy.
The SXBlue II + GNSS receiver uses conventional real-time differential corrections obtained from a Space Based Augmentation System (SBAS) to improve position accuracy. The SXBlue II + GNSS unit receives location information from both GPS and GLONASS satellite constellations. Use of both satellite systems improves accuracy and reduces the chance that poor satellite geometry will reduce position accuracy by increasing the number of satellites that are available to determine the position.
The SXBlue II + GNSS receiver is one component of a modular system to collect location information at a field site (Fig. 1). The two other required components of the system are data acquisition hardware and data collection software. Many types of computers can be used as the acquisition hardware, including smart phones, laptops, PDA, and tablets. There are also options for data collection software, including free mobile applications, ArcGIS Collector, and Microsoft Windows-compatible software. Data collection software acquires data from one of 3 available communication options: (1) Bluetooth port (Class 1), (2) USB Port (Type B, female port), and (3) RS-232 Serial Port.
Figure 1. Components of the system we used:
We used a tablet (Samsung Galaxy Tab A) and mobile applications to collect location data at test sites using the SXBlue II + GNSS receiver. We used the ‘Bluetooth GPS’ application (Version 1.3.7, GG MobLab) to establish a Bluetooth connection between the receiver and the tablet, and the ‘GPSlogger’ application (Version 91, Mendhak) to collect location information. Both mobile applications are available free of charge from Google Play. We set GPSlogger settings to record a point every 2 seconds. With this setting, points were actually recorded every 3 seconds. We could not analyze the effect of the number of satellites or dilution of precision measurements on location error because GPSlogger does not store those values.
We conducted stationary tests at 9 georeferenced survey markers (Fig. 2) from October 2016- November 2017. We obtained information on survey markers from St. Louis County’s Survey Explorer website (http://gis.stlouiscountymn.gov/gisviewers/surveyexplorer.aspx). We viewed corner reports of potential sites and selected sites that were georeferenced with reported horizontal accuracies ≤0.05 m. The final set of sites included a range of forest conditions, from open, non-forested sites to mature, closed-canopy sites.
Figure 2. Map of GPS test sites at georeferenced survey markers in St. Louis County, Minnesota.
We used a standard protocol at each site to ensure location data were collected consistently. The SXBlue II receiver was assembled and turned on in an area with a clear view of the sky (i.e., with no vegetation directly overhead). We kept the receiver and antennae in an area with a clear view of the sky until the receiver indicated it had achieved a differential position and it had obtained an SBAS lock (i.e., indicator lights ‘DGPS’ and ‘DIFF’ were illuminated). The SXBlue II antenna was then attached to a tripod and positioned directly over the survey monument. We measured the height of the antenna above ground to account for variations in antenna height among sites. Once the receiver was in position over the survey monument, we allowed the receiver to track satellites and acquire SBAS corrections for 5 minutes before beginning to collect location data. We collected location data for at least one hour at each site (Table 1). Mean test duration was 161 minutes (SE = 38, minimum = 60 min, maximum = 370 min).
We measured canopy closure over each survey monument with a convex densitometer using Strickler’s (1959) modification. Mean canopy closure at test sites was 57% (SE = 11%; minimum = 0%; maximum = 97%). Five of 9 test sites had canopy closures >65% (Table 1). We also recorded site (forest cover type, tree species, relative age or size, etc.) and weather (cloud cover, wind conditions, etc.) conditions.
Data Analysis
For each test, we calculated the cumulative average x- and y-coordinates following the addition of each new position fix. We calculated cumulative accuracy over time as the straight-line distance between each cumulative average coordinate and the “true” site coordinates obtained from the corner report. This allowed us to evaluate accuracy at specific time intervals and determine how long it took to achieve a sub-meter location. We limited cumulative accuracy calculations to the first hour of testing and pooled data among sites to calculate mean cumulative accuracy and 95% confidence interval. We used simple linear regression to test for a relationship between accuracy and canopy closure, antennae height, or precision. Individual models were fit for each variable because the sample size was too small to fit multivariate models. We used 50% circular error probability (CEP) as the measure of precision.
We calculated direction and angular dispersion for each site (i) at 5-minute intervals over the first hour of testing to evaluate directional bias. Direction was calculated as the angle from the true location to the cumulative average coordinate for a given time interval. Angular dispersion was calculated using equations given by Zar (1984). Dispersion is a measure of concentration of angles that ranges from zero to one. Values near one indicate high concentration and, therefore, directional bias.
Cumulative accuracy estimates described above do not capture variation in location accuracy at any given site. To evaluate variation in SXBlue II accuracy over time, we changed the start time for each test and re-calculated cumulative accuracy over time for individual one-hour sample intervals. For each site, we used a total of 5 different sample intervals. For tests lasting ≥5 hours (n = 2), sample intervals started at 0, 1, 2, 3, and 4 hours. For tests lasting >1 but <5 hours (n = 4), sample intervals started every 15-30 minutes. For tests lasting 1 hour (n = 3), samples started at 0, 5, 10, 15, and 20 minutes, and each test duration got progressively shorter. We used the 5 sample intervals to calculate mean and 95% confidence intervals for cumulative accuracy over time. We then used site-level mean accuracies to compute grand mean cumulative accuracies and 95% confidence intervals.