Effect of Occupation Time on the Horizontal
Accuracy of a Mapping-Grade GNSS Receiver
under Dense Forest Canopy
Robert J. McGaughey, Kamal Ahmed, Hans-Erik Andersen, and Stephen E. Reutebuch
Abstract
A mapping-grade dual frequency
GNSS
receiver was tested un-
der dense forest canopy to determine the effect of occupation
time on horizontal accuracy. The U.S. Forest Service Forest
Inventory and Analysis unit in the Pacific Northwest has been
using 32 of these units to collect over 7,000 plot locations
since 2013. In this study, one-hour
GNSS
static occupations
were collected at 33 ground-surveyed positions with Trimble
GeoXH6000 mapping-grade and Javad Triumph1 survey-
grade receivers. Rover files were differentially post-processed
and horizontal accuracy of each post-processed position
was computed. Results indicated that 1.85 m accuracy (n =
990) could be achieved with the GeoXH6000 receiver with
15-minute occupations; however, maximum horizontal error
was 7.01 m. Increasing occupation time to 20 minutes did not
result in a significant improvement in accuracy. No correla-
tion was found between the horizontal precision of a post-
processed position reported by the postprocessing software
and the field-measured horizontal accuracy of the positions.
Introduction
National Forest Inventories (
NFI
) are designed to produce and
report estimates of forest resources. The types of estimates
produced include forest area, volume, condition, removals,
growth, mortality, and overall trends in land use. Estimates
are generally summarized to represent various levels, includ-
ing owner group (e.g., state, private, federal), ecological units,
survey unit, forest type, and tree species. Inventory data have
traditionally been used by public and private land managers,
planners, and researchers to address a variety of informa-
tion needs. NFIs are established in many countries around
the world, providing information to the local government
and commercial timber industry as well as supporting global
efforts to monitor the effects of climate change, carbon emis-
sions and sequestration, and biological diversity.
In the United States, The
NFI
is conducted by the Forest Ser-
vice’s Forest Inventory and Analysis program (
FIA
).
FIA
collects,
analyzes, and reports information on the status and trends of
America’s forests: how much forest exists, where it exists, who
owns it, and how it is changing, as well as how the trees and
other forest vegetation are growing and how much has died or
has been removed in recent years.
FIA
uses four related surveys
to characterize different aspects of America’s forests: forest
monitoring, ownership survey, timber product output survey,
and utilization studies. The forest monitoring component uses
a three-phase sample with permanent sample sites located
across the United States. Phase 1 uses remotely-sensed data for
stratification and to identify whether or not a location is for-
ested. Phase 2 consists of one sample site for every 6,000 acres,
where field crews collect data on forest type, site attributes,
tree species, tree size, and overall tree condition. Phase 3 uti-
lizes a subset of the Phase 2 sample sites where crews measure
a broader suite of forest health attributes including tree crown
conditions, lichen community composition, understory vegeta-
tion, down woody debris, and soil attributes.
FIA
samples and
summarizes data for the continental states, Alaska, Hawaii, and
US Pacific Island Territories and Protectorates. The program in-
cludes over 323,000 phase 2 sample locations. Approximately
116,400 of these locations are forested and about 20,000 are
measured each year (Vogt and Smith, 2017). The
FIA
database
is one of the most comprehensive and complete samples of
vegetation conditions in the world. For the sample sites, coarse
locations are obtained using aerial imagery to guide crews to
the site. In the field, crews typically record a location using a
global navigation satellite system (
GNSS
). Plot confidentiality
requirements limit the distribution of the actual plot locations.
However, cooperators can obtain and use the actual locations
after completing a formal spatial data request process and sign-
ing a non-disclosure agreement.
For
FIA
, the location (typically acquired using a low-cost,
consumer-grade
GPS
unit) is primarily used to relocate the
sample site. While an accurate position is always desirable,
the position only needs to be good enough to help crews plan
for travel to and from the site and to relocate the site each
time it is measured. Accuracies for the
GNSS
receivers typi-
cally used by field crews range from a few meters to several
decameters depending on the quality of the
GNSS
receiver, the
forest conditions at the site, and postprocessing of the
GNSS
data (Bolstad
et al
., 2005; Hoppus and Lister, 2007). This level
of accuracy has been sufficient for
FIA
. However, the exten-
sive area covered by
FIA
data, repeated measurements over
time, consistent protocol, and general availability of the data
make the plot data useful for a variety of applications many
of which would benefit from more accurate locations.
NFI
data
have been combined with a variety of remotely-sensing data to
map characteristics over large areas and to help detect changes
over time (Tomppo
et al
., 2008; Zald
et al
., 2014; Blackard
et al
., 2008). Historically, rectification error present in the
remotely-sensed data (Landsat
TM
) has been such that improv-
ing the accuracy of the
GNSS
location did not always result in
improvements in analysis results (McRoberts, 2010). However,
the remote sensing world is changing. High resolution digital
Robert J. McGaughey, Hans-Erik Andersen, and Stephen E.
Reutebuch (retired) are with the USDA Forest Service, Pacific
Northwest Research Station, University of Washington, PO
Box 352100, Seattle, WA, 98195-2100 (
).
Kamal Ahmed is with Cairo University, Faculty of
Engineering, Cairo, Egypt
Photogrammetric Engineering & Remote Sensing
Vol. 83, No. 12, December 2017, pp. 861–868.
0099-1112/17/861–868
© 2017 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.83.12.861
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
November 2017
861
