Environ Monit Assess (2011) 179:123–135DOI 10.1007/s10661-010-1723-x

Patch size and shape influence the accuracy of mappingsmall habitat patches with a global positioning system

Daniel C. Dauwalter · Frank J. Rahel

Received: 21 January 2010 / Accepted: 20 September 2010 / Published online: 2 October 2010© Springer Science+Business Media B.V. 2010

Abstract Global positioning systems (GPS) areincreasingly being used for habitat mapping be-cause they provide spatially referenced data thatcan be used to characterize habitat structureacross the landscape and document habitat changeover time. We evaluated the accuracy of usinga GPS for determining the size and location ofhabitat patches in a riverine environment. Wesimulated error attributable to a mapping-gradeGPS receiver capable of achieving sub-meter ac-curacy onto discrete macrophyte bed and woodhabitat patches (2 to 177 m2) that were digitizedfrom an aerial photograph of the Laramie River,Wyoming, USA in a way that emulated field map-ping. Patches with simulated error were comparedto the original digitized patches. The accuracy inmeasuring habitat patches was affected most bypatch size and less by patch shape and complexity.Perimeter length was consistently overestimatedbut was less biased for large, elongate patches withcomplex shapes. Patch area was slightly overesti-

D. C. Dauwalter · F. J. RahelDepartment of Zoology and Physiology,University of Wyoming, Laramie, WY 82071, USA

Present Address:D. C. Dauwalter (B)Trout Unlimited, 910 Main Street, Suite 342,Boise, ID 83702, USAe-mail: ddauwalter@tu.org

mated for small patches but was unbiased for largepatches. Precision of area estimates was highestfor large (>100 m2), elongate patches. Percentspatial overlap, a measure of the spatial accuracyof patch location, was low and variable for thesmallest patches (2 to 5 m2). Mean percent spatialoverlap was not related to patch shape but theprecision of overlap was lower for small, elongate,and complex patches. Mapping habitat patcheswith a mapping-grade GPS can yield useful data,but research objectives will determine the accept-able amount of error and the smallest habitats thatcan be reliably measured.

Keywords Habitat mapping · Habitat patches ·Global positioning system · GPS ·Geographic information system · GIS ·Simulation · Error · Root mean squared error ·Trimble

Introduction

Measuring the size, shape, spatial position, andtemporal change of habitat patches used by ani-mals is important to understanding how habitatsinfluence individuals and populations (Knutsonet al. 1999; Kocik and Ferreri 1998; Linke et al.2005; Schlosser and Angermeier 1995). There aremany field methods for measuring aquatic (Bainand Stevenson 1999) and terrestrial (Braun 2005)

124 Environ Monit Assess (2011) 179:123–135

habitats, but most lack the ability to provide aspatial framework for habitat conditions. A spatialframework allows researchers to investigate howthe juxtaposition and connectivity among habi-tat patches influences the distribution and abun-dance of organisms (Knutson et al. 1999). Largequantities of georeferenced data can be collectedusing remote sensing techniques, and these dataare often available from the Internet. However,the relatively large grain size (i.e., low spatialresolution) of such data limits its usefulness forstudying habitat patchiness at the spatial scalesthat may be important for many small-bodied or-ganisms. Also, remotely sensed habitat data canhave high levels of misclassification error (20.4%misclassification of land cover; Wyoming GapAnalysis 1996) and can be expensive to obtain forsite-specific projects (Allen 1994; Fisher 2004).Therefore, biologists often resort to collectingspatially-referenced habitat data in the field andthe most common way to do this is by using aglobal positioning system (GPS).

Global positioning systems are increasinglybeing used to collect spatial data for environ-mental research and management (August et al.1994; Johnson and Barton 2004). For example,GPS has been used to map freshwater habitats(Jeffrey and Edds 1997; O’Connor and Rahel2009; Valley et al. 2005), marine habitats (Smithand Greenhawk 1998), terrestrial wildlife habitats(Hulbert and French 2001), and terrestrial vege-tation patches (Webster and Cardina 1997). Oncehabitat patches are mapped using GPS, their size,geographic location, proximity to other habitatpatches, and change in size and location over timecan be measured in a geographic information sys-tem (GIS; Baxter 2002; Dauwalter et al. 2006; LePichon et al. 2006; Torgersen et al. 2004; Websterand Cardina 1997). In general, the size of habitatpatches mapped in most studies is relatively large(>100 m2) compared to the size of habitat patchessuch as macrophtye beds and wood accumulationsthat are important to small-bodied organismssuch as stream fishes (Belica and Rahel 2008; LePichon et al. 2009). Thus, there is a need to evalu-ate the accuracy of habitat patches with a GPS atsmall spatial scales.

The accuracy of a GPS determines whether itcan be used to reliably map habitat because map-

ping errors can result in false conclusions regard-ing habitat characteristics, habitat change, andspecies–habitat relationships (Visscher 2006). Theaccuracy of GPS locations is typically expressedas a measure of precision. Precision is referencedas a root mean squared error (σerror) that is theproduct of the two independent errors, pseudor-ange error, also known as user equivalent rangeerror (σUERE), and dilution of precision (DOP).Pseudorange error is composed of several sourcesof error that affect the satellite-to-user range mea-surement; that is, the estimated distance betweena satellite and a GPS receiver used to estimatelocation (discussed in Conley et al. 2006). Pseudo-range error is approximated as a zero meanGaussian random variable, N(0, σUERE) (Conleyet al. 2006). Dilution of precision is unitless andexpresses the composite effect of user-satellitegeometry and GPS receiver satellite-selection al-gorithm (i.e., the satellites selected by the receiverto compute location) on the error in locations esti-mated using GPS; positional dilution of precision(PDOP) is most commonly used and expressesthe effect of satellite geometry on horizontal andvertical precision. If DOP can be assumed fixedbecause GPS locations are collected over smallareas and short time periods, then σerror = DOP× σUERE where DOP is known for that locationand time. If GPS locations are determined overlarger areas and longer time periods then DOPis a random variable and σerror = DOPrms × σuere

where DOPrms is the root mean square of DOPcomputed as cumulative density function of dis-crete DOP values (Leva et al. 1996). Regardless ofhow σerror is computed, two-dimensional error isreferenced as horizontal root mean squared error(σh−error) that results from the variance of errors

along the x and y axes: σh-error =√

σ 2x + σ 2

y . The

probability that a GPS location is within a circlehaving a radius of 1 σerror is 0.63 for a circular errordistribution and 0.69 for an elongated distribution.For 2 σerror, probabilities are from 0.95 to 0.98 de-pending on the circularity of the error distribution(Conley et al. 2006).

Our objective was to determine how horizontalGPS error (σh-error) influences the characteriza-tion of discrete two-dimensional habitat patchesand the detection of habitat change over time

Environ Monit Assess (2011) 179:123–135 125

when habitats are mapped using GPS. To meetthis objective we quantified the error associatedwith mapping aquatic habitat patches using GPSin the Laramie River, a high plains stream insoutheastern Wyoming, USA. We explored howthe amount of error was related to habitat sizeand shape. We focused on discrete patches con-sisting of macrophyte beds or wood accumulationsthat are important to fishes in riverine habitats(Belica and Rahel 2008; O’Connor and Rahel2009). Determining how GPS error affects mea-surement of discrete two-dimensional habitats willhelp to identify the size and shape of habitats thatcan be reliably measured, and the magnitude ofhabitat change that can be detected, when habitatsare mapped with a GPS. Although we evaluatethe effect of GPS error in the context of map-ping riverine habitats, our results are applicableto any two-dimensional discrete habitats that aremeasured and mapped using GPS under similarenvironmental conditions.

Methods

Habitat patches

We evaluated the error in mapping habitatpatches with GPS by simulating horizontal GPSerror onto discrete macrophyte bed and woodpatches that were digitized from an aerial pho-tograph of the Laramie River, Albany County,Wyoming, USA (Fig. 1). An aerial photographof a 2-km segment of the Laramie River wastaken after leaf-off in autumn 2003 and thenortho-rectified (Horizons Inc., Rapid City, SouthDakota, USA). This segment of the LaramieRiver is a high plains stream with some riparianareas with trees and shrubs (Salix spp.) and otherareas dominated by herbs and grasses. The aerialphotograph had a 0.1-m pixel resolution with 90%of all features accurate to 0.042 m and the remain-ing features accurate to 0.084 m. All 97 macro-phyte and wood habitat patches >2 m2 within the

Fig. 1 The 2-km studysegment of the LaramieRiver, Wyoming, USAwhere habitat patcheswere digitized from anaerial photo to evaluatethe effects of GPS erroron measuring habitatpatches. Insert shows anexample of a macrophytepatch and a wood patchthat were digitized fromthe aerial photo

126 Environ Monit Assess (2011) 179:123–135

Fig. 2 An example of asimulation where GPSerror was added onto adigitized macrophytepatch in the LaramieRiver, Wyoming, USA.The original patch isshown by the solidoutline, and the simulatedpatch with error is shownby the dotted outline

2-km segment of the Laramie River were digi-tized as polygons in the GIS software ArcGIS 9.2(ESRI, Redlands, CA). Digitizing was done in away that simulated on the ground mapping with aGPS receiver: patches were digitized as polygons,and polygon vertices were placed every 0.5 malong the margin of each habitat patch. This pro-cedure has been used previously during GPS map-ping of Laramie River habitat patches (O’Connorand Rahel 2009).

Digitized patches included a range of sizes andshapes. We also digitized simulated patches con-sisting of squares, rectangles, or crosses to deter-mine how GPS error was related to patch sizeand shape. Digitized habitat patches were char-acterized using three metrics: area, elongation,and complexity. Area was used to characterizehabitat patch size. Elongation was measured as:Area/Length2, where length is measured alongthe longest axis. Elongation ranges from near 0for very elongate shapes to 1 for a square andis commonly used to quantify watershed mor-phology (Gallagher 1999a). Shape complexity wasmeasured as the perimeter-to-area ratio: Perime-ter/(2 · (Area · π))0.5. This ratio ranges from 1for a perfect circle to >1 for complex shapes thathave much longer perimeters per unit area andis often referred to as the shoreline developmentindex (Gallagher 1999b). Area, elongation, andcomplexity of digitized patches were measuredusing ArcGIS 9.2. All GIS files were displayed

and analyzed in the Universal Transverse Mer-cator, Zone 13 coordinate system, and WGS84datum.

Area (m2)1 10 100 1000

Elo

ngat

ion

(Elo

ngat

e)

(Circ

ular

)

0.0

0.2

0.4

0.6

0.8

1.0WoodMacrophyte

(a)

Area (m2)1 10 100 1000

Com

plex

ity

(Sim

ple)

(

Com

plex

)

1.0

2.0

3.0

4.0

5.0

6.0(b)

Fig. 3 Relationships among area, elongation, and shapecomplexity for macrophyte and wood patches in theLaramie River, Wyoming, USA

Environ Monit Assess (2011) 179:123–135 127

Simulating GPS error

Using the digitized habitat patches, we simulatedhorizontal GPS error (σh-error) onto each poly-gon vertex of a digitized patch to simulate theeffects of GPS error on habitat patches mappedin the field. We simulated error associated withthe Trimble ProXRS GPS receiver (Trimble Nav-igation Limited, Sunnyvale, CA, USA) that iscommonly used to measure aquatic and terres-trial habitats (Belica and Rahel 2008; Schillingand Wolter 2000; Webster and Cardina 1997).The ProXRS receiver has a horizontal root mean

square of 0.5-m (σh-error =√

1N (

N∑i=1

(hi)2) = 0.5 m;

where N = number of position observations indataset and hi = horizontal error of ith observa-tion) when code phase signals are used for satelliterange measurements and data are differentiallycorrected (see Cosentino et al. 2006) and col-lected under the following conditions: minimumof four satellites; maximum PDOP of six, min-

imum signal-to-noise ratio of 39 dBHz, mini-mum satellite elevation of 15◦, and reasonableatmospheric and multipath conditions (Datasheet:GPS Pathfinder Pro XRS receiver; Trimble Navi-gation LTD, Westminster, CO). These conditionsare meant to represent ideal environmental con-ditions for collecting GPS data, are commonlyused to control the quality of GPS data dur-ing data collection and post-processing, and areset as quality-control defaults in the ProXRS re-ceiver. The GPS receiver will not allow data tobe collected when these conditions are not met.The manufacturer-specified horizontal root meansquare is determined by collecting GPS data atapproximately a 5-s interval for several hours (upto 24 h) at a known location, comparing the errorin GPS determined positions to the known posi-tion, and summarizing the distribution of knownerrors as the horizontal root mean square of errors(Trimble Navigation Limited 1997). GPS errorwas added onto the x and y coordinates of eachpolygon vertex by randomly selecting error for

Fig. 4 Percent error inperimeter length and areaof habitat patches inrelation to patch area,elongation, and shapecomplexity after GPSerror was simulated fordigitized wood andmacrophytes patches.Circles represent themean and error barsrepresent 1 SD of 1,000simulations per patch

Area (m2)1 10 100 1000

Are

a E

rror

(%

)

-30

-20

-10

0

10

20

3040

(Elongate) (Circular) Elongation

0.0 0.2 0.4 0.6 0.8

Are

a E

rror

(%

)

-30

-20

-10

0

10

20

3040

(Simple) (Complex) Complexity

1 2 3 4 5 6

Are

a E

rror

(%

)

-30

-20

-10

0

10

20

3040

(b)

d

f

Area (m2)1 10 100 1000

Per

imet

er E

rror

(%

)

0

20

40

60

80

100

120

140

(Elongate) (Circular) Elongation

0.0 0.2 0.4 0.6 0.8

Per

imet

er E

rror

(%

)

0

20

40

60

80

100

120

140

(Simple) (Complex) Complexity

1 2 3 4 5 6

Per

imet

er E

rror

(%

)

0

20

40

60

80

100

120

140

a

c

e

b

128 Environ Monit Assess (2011) 179:123–135

each coordinate from a Gaussian distribution. Thedistribution for each coordinate was specified us-

ing the relation σh-error =√

σ 2x + σ 2

y , where σ 2x =

σ 2y . Since σh-error = 0.5 m for the ProXRS receiver,

σx = σy ≈ 0.3535 m. Thus, the simulated error onthe x- and y-axes was drawn from a normal dis-tribution with a mean of 0 and a SD of 0.3535 m.Coordinates of vertices were exported into SAS,Version 9.3 (SAS Institute Inc, Cary, NC) anderror was simulated onto each vertex of all 97patches 1,000 times. After error was simulatedonto each vertex, the 1,000 replicated polygonsper patch were reconstructed in ArcGIS 9.2 andpatch characteristics were measured (Fig. 2).

We quantified GPS error in mapping habi-tats by comparing each simulated patch to theoriginal digitized patch. For each simulation,we computed the percent error in perime-ter as: [(Perimetersimulated − Perimeteroriginal) /Perimeteroriginal] × 100, and percent error in areaas: [(Areasimulated −Areaoriginal) /Areaoriginal]×100.The effect of error on documenting habitat change

was determined by comparing the percent arealoverlap between each simulated patch and theoriginal digitized patch: [Areaoverlap /Areaoriginal]×100. The amount of error in perimeter, area, andoverlap was compared to patch area, elongation,and complexity to determine how those factorsinfluenced the amount of error observed whenmapping habitat patches with a mapping-gradeGPS.

We used a statistical power analysis to deter-mine how horizontal GPS error might influencedetection of changes in patch size. We estimatedthe power to detect 5% to 100% changes in sizefor patches ranging from 2 to 200 m2. We useda Type I error rate of 5% (two-tailed α = 0.05).For the analysis, we specified that variances wereproportional to patch area (based on our sim-ulated data; variance/mean = 0.16) and samplesize was n = 2. A sample size of n = 2 was usedbecause we were interested in the ability to detectchanges in patch size when measuring a patch witha GPS receiver only once during an initial time

Fig. 5 Percent error inperimeter length and areaof known shapes ofdifferent sizes. Therectangle had a 5:1 lengthto width ratio (elongation= 0.2). All perimeter linesegments of the crosswere equal length. Circlesrepresent the mean anderror bars represent 1 SDof 1,000 simulations perpatch

Per

imet

er E

rror

(%

)

-100

102030405060

-100

102030405060

Area (m2)1 10 100 1000

-100

102030405060

Square

Rectangle

Cross

-40

-20

0

20

40

60

Are

a E

rror

(%

)

-40

-20

0

20

40

60

Area (m2)1 10 100 1000

-40

-20

0

20

40

60

Square

Rectangle

Cross

a

c

e

b

d

f

Environ Monit Assess (2011) 179:123–135 129

period and once again at a later date. The analysiswas conducted using an Excel tool developed byGerow (2007).

Results

The shape of habitat patches in the Laramie Riverchanged with patch area. Small patches werehighly variable and ranged in shape from round toelongate whereas large patches tended to be elon-gate (Fig. 3a). Patch complexity was higher forlarge, elongate patches (Fig. 3b). These relation-ships were similar for both macrophyte and woodhabitat patches.

Simulation of GPS error resulted in patchperimeters being consistently overestimated al-though the magnitude of error decreased withincreasing patch area (Fig. 4a). The average er-ror in perimeter estimates for small patches 2 to5 m2 ranged from 31% to 83% but the averageerror decreased to less than 10% for large patches>100 m2 (Fig. 4a). The tendency to overestimateperimeter length was variable but decreased forelongate patches (Fig. 4b) and for patches withcomplex shapes (Fig. 4c). Overall, perimeter wasmore precisely estimated for large, elongate, andcomplex patches (Fig. 4a–c).

The area of large patches was more preciselyestimated than the area of small patches and therewas a tendency for the area of small patches tobe slightly overestimated (Fig. 4d). The averageerror for measurements of patch area ranged from−0.4% to 9.7% among all simulations for thesmallest patches (2 to 5 m2) and from −0.2% to0.0% for the largest patches (>100 m2). Precisiondid not change with patch elongation (Fig. 4e).Precision was not related to patch complexity ex-cept for the most complex patches (which alsowere large; >100 m2) where area was preciselyand accurately estimated (Fig. 4f).

When GPS error was simulated onto knownshapes, perimeter was overestimated for allshapes but was most biased for the complex crossshape that had the highest perimeter/area ratio(Fig. 5a, c, e). There was no bias in area estimates(Fig. 5b, d, f). The precision of perimeter andarea estimates increased with patch area for allshapes as evidenced by the decline in SD (Fig. 5).

Perimeter was estimated more precisely for theelongate rectangle than for the complex cross thatcontained more vertices (Fig. 5c, e). In contrast,area was estimated more precisely for complexcross shapes versus the square or elongate rectan-gle (Fig. 5b, d, f).

Percent overlap between digitized patches andpatches with simulated GPS error increased andbecame more precise as patch size increased(Fig. 6a). Overlap averaged from 59% to 79% forthe smallest patches (2 to 5 m2) and averaged from89% to 90% for the largest patches (>100 m2).There were no apparent trends in percent overlapwith patch elongation or complexity (Fig. 6b, c).

Area (m2)1 10 100 1000

% O

verla

p

0

20

40

60

80

100 a

(Elongate) (Circular) Elongation

0.0 0.2 0.4 0.6 0.8

% O

verla

p

0

20

40

60

80

100 b

(Simple) (Complex)Complexity

1 2 3 4 5 6

% O

verla

p

0

20

40

60

80

100 c

Fig. 6 Percent overlap in area of habitat patches withsimulated GPS error and original digitized patch plottedagainst patch area, elongation, and shape complexity. Cir-cles represent the mean and error bars represent 1 SD of1,000 simulations per patch

130 Environ Monit Assess (2011) 179:123–135

0

20

40

60

80

100

% O

verl

ap

0

20

40

60

80

100

Area (m2)

1 10 100 1000

0

20

40

60

80

100

Square

Rectangle

Cross

Fig. 7 Percent overlap in area between known shapesand the shapes with simulated GPS error as a function ofshape area. The rectangle had a 5:1 length to width ratio(elongation = 0.2). All perimeter line segments of the crosswere equal length. Circles represent the mean and errorbars represent 1 SD of 1,000 simulations per patch

Initial Patch Area (m2)

0 25 50 75 100 125 150 175 200

% C

hang

e in

Pat

ch A

rea

10

20

30

40

50

60

70

80

90

100

90

90

80

80

80

70

70

70

60

60

60

60

50

50

50

50

4040

40

40

40

303030

30

30

Fig. 8 Isopleths of statistical power, expressed as a per-centage, to detect changes in patch size for varying patchareas. Power was computed assuming variances were pro-portional to patch area (based on our simulated data;variance/mean = 0.16), sample size was n = 2, and α = 0.05

However, the precision of overlap was higher inlarge, elongate and complex patches (Fig. 6a–c).

Percent spatial overlap in known shapes withsimulated GPS error increased with shape areaand was slightly higher on average for the simple,symmetrical square versus the elongate rectangleand the complex cross (Fig. 7). For shapes with thesame area, the precision in overlap was lowest forthe elongate rectangle and highest for the complexcross and increased for all shapes as size increased(Fig. 7).

Given the error observed in GPS-measuredpatch sizes, the ability to detect changes in patchsize increased with patch area (Fig. 8). Therewas low power to detect changes of even 100%in patches less than ∼25 m2. However, moder-ate changes (60%) in patches >50 m2 and low-to-moderate changes (40%) to patches >100 m2

could be detected with good power (>0.60).

Discussion

Our results indicate that the error in measuringthe perimeter, area, and spatial location of habitatpatches with a GPS depends largely upon patcharea and to a lesser degree on patch shape. In mostcircumstances GPS can be used effectively to mea-sure habitat patches greater than 50 m2 regardlessof their shape. However, when habitat patchesare less than 50 m2, researchers must carefullyconsider the patch sizes and shapes they intend tomeasure against their research objectives in orderto determine whether GPS can be used effectively.

The precision in mapping habitat patches willdepend upon the accuracy of the GPS receiver.Consumer-grade receivers are least expensive andhave up to 19 m of error, mapping-grade receiversincluding the Trimble ProXRS that we used have0.5 to 1 m of error, and survey-grade receivershave 0.1 m of error or less (Table 1). Althoughsurvey-grade receivers have the best precision,they are expensive, require setup of a nearbybase station, and require greater user sophisti-cation. Thus, many natural resource applicationsinvolve the use of mapping-grade receivers to in-ventory habitat conditions at spatial scales smallerthan those that can be mapped by remote-sensing

Environ Monit Assess (2011) 179:123–135 131

Table 1 Manufacturer specified σh-error, observed σh-error, and mean horizontal error of consumer, mapping, and survey-grade GPS receivers reported from field studies

Receiver Manufacturer-specified Observed Mean error (SD) Sourceσh-error σh-error

a

Consumer-gradeGarmin V 4.6 11.3 8.9 (6.9) (Wing and Karsky 2006)

2.8 2.6 (0.9) (Wing et al. 2005)Garmin Etrex Vista 4.6 2.4 2.2 (1.0) (Wing et al. 2005)Garmin Geko 301 4.6 4.7 4.2 (2.0) (Wing et al. 2005)Garmin GPSmap 76S 4.6 1.5 1.4 (0.6) (Wing et al. 2005)

11.8 9.8 (6.6) (Bolstad et al. 2005)b

Magellan Meridian Platinum 3.5 20.0 19.6 (3.8) (Wing et al. 2005)Magellan SportTrak Map 3.5 1.9 1.8 (0.6) (Wing et al. 2005)

Mapping-gradeTrimble ProXR, XRS, XL 0.5 4.8 4.0 (2.8) (Liu 2002)b

0.5 NA (Sigrist et al. 1999)Trimble GeoXT <1 0.6 0.5 (0.3) (Wing et al. 2008)

0.9 0.6 (0.6) (Dauwalter et al. 2006)2.1 1.6 (1.4) (Bolstad et al. 2005)b

Leica GS20 0.3 1.0 0.9 (0.4) (Wing and Karsky 2006)GENEQ SXBlue 0.3 8.8 7.8 (4.0) (Wing and Eklund 2007)

Survey-gradeTrimble System 5700 0.25 NA 0.02 (NA) (Johnson and Barton 2004)

Precision and mean errors for mapping and survey-grade receivers are after differential correction. Data were collectedunder open-sky conditions unless otherwise noted. All units are metersNA not availablea computed as: σh-error =

√SD2 + Mean2 (from Naesset and Jonmeister 2002) if not reported directly

b Under forest canopy

techniques (Belica and Rahel 2008; Webster andCardina 1997).

Precision of GPS data is also dependent oncanopy cover (Naesset and Jonmeister 2002).Wing et al. (2008) found that horizontal errorsfrom post-processed GPS data collected with amapping grade receiver were not different be-tween open-sky and young-forest (canopy closure50%) conditions and only increased substantiallyin mature-forest (canopy closure nearly 100%)conditions. In addition, advancements in GPStechnology continue to be made to reduce theeffect of canopy cover on GPS data, and GPSreceivers are often marketed as being effectivein urban settings with challenging GPS environ-ments. The Laramie River is a high plains streamand has an interspersed matrix of cottonwoodgallery riparian areas against a background matrixof open riparian areas with grasses and sedges.The cottonwood gallery riparian areas never ap-proached complete canopy closure. Majority ofthe study segment represented near ideal GPS

mapping conditions because of the lack of com-plex terrain and little overhead canopy cover.Hence, our results are most likely to apply tosimilar physiographic regions as opposed to sitesin mountainous terrain with dense forest canopy.The effect of GPS error on habitat patch charac-teristics in regions with complex topography anddense forest canopy should be an area of futureresearch.

Given the widespread use of mapping-gradeGPS receivers, what can be done to improve ac-curacy when mapping habitat patches? Most GPSreceivers by default use code-phase signals to de-termine the distance between the GPS receiverand satellites. However, the use of carrier-phasesignals can increase the precision of GPS receiverscapable of using the signal, and the increase inprecision is dependent on the length of time a re-mote base station collects data. For example, eventhe ProXRS can use carrier phase and decreasethe σh-error from 0.5 to 0.3 m after 5 min and to0.1 m after 20 min (Trimble Navigation Limited

132 Environ Monit Assess (2011) 179:123–135

2005). However, use of carrier phase data requiresa second receiver to serve as a remote base sta-tion and requires more setup time (Deckert andBolstad 1996; Sigrist et al. 1999). It also requiresan uninterrupted lock on satellite signals for theduration of mapping that can be difficult to obtainin some field conditions. This makes it unreliablewithout planning and a field trial. Use of codephase versus carrier phase signals is discussed byCosentino et al. (2006) and Samama (2008).

Another option to increase precision is to col-lect more GPS data for a single location, suchas a vertex along a patch boundary. Multipleposition fixes can be averaged to estimate thespatial location of a single point or vertex on apolygon. However, Dauwalter et al. (2006) foundno statistical difference in the accuracy of GPSpoints when 1, 10, or 100 position fixes collectedat 1-s intervals were averaged to estimate loca-tions. Wing and Karsky (2006) also found noimprovement in accuracy when using 1 to 60 po-sition fixes to compute spatial location. GPS dataneeds to be collected for several minutes beforean appreciable gain in precision (e.g., 20% de-crease in σh-error) is observed using multiple posi-tion fixes to compute location estimates (Naessetand Jonmeister 2002; Trimble Navigation Limited1997). Collecting data for several minutes for eachpolygon vertex may not be feasible in most fieldmapping applications where one needs to collectmultiple vertices for each patch and numerouspatches need to be mapped. A more feasible op-tion may be to map habitat patches multiple timesand summarize the replicate patch measurements.In addition, if research questions are focused onthe total area or patch composition in a study areathen the GPS error will average out as long as thepatch characteristics of interest are unbiased (e.g.,area, but not perimeter).

Given the error associated with measuringsmall habitat patches with a GPS, certain met-rics used to quantify habitat are more reliablethan others. Proximity of habitats within a land-scape can influence the ecology of some or-ganisms (Matter 2006; Wiens 2002). Belica andRahel (2008) mapped macrophyte bed and woodhabitat patches in the Laramie River using GPSand found that inter-patch distance negativelyinfluenced the rate at which creek chubs (Se-

motilus atromaculatus) moved between patches.Swihart et al. (2003) found non-volant mammalsto occupy fewer patches in a landscape whenpatches were more isolated. The distance betweenpatches should be the metric least affected byGPS error when GPS is used for measurements.Although we did not assess the error in distancebetween patch boundaries directly, the error of aline segment is largest at the vertices and smallestbetween two vertices (Leung et al. 2004). There-fore, the error in the distance between habitatpatches is at most the error in spatial location ofthe two closest edges of two patches. This erroris constant regardless of patch size and wouldaverage less than 1 m for mapping-grade receiverswith a σh-error of 0.5 m.

The error associated with measuring the area ofhabitat patches increased as patch size decreasedand was considerable for small patches (2 to 5 m2).Many questions in ecology require that the abun-dance of organisms be scaled to the availability ofresources. Krauss et al. (2005) found that butterflydensities in Germany were related to the size ofgrassland patches and the quality and abundanceof larval food plants. Accurate estimates of habitatarea are required for densities to be measuredaccurately. Moreover, species–area relations arean important component of biodiversity studies(Angermeier and Schlosser 1989; Gleason 1922),and the area of habitats can influence colonizationand extinction dynamics of habitats (MacArthurand Wilson 1967). When ecological questions arefocused on small spatial scales that have smallgrains and extents (sensu Palmer and White 1994),accurate measurement of habitat area is essen-tial. The error inherent in GPS data collectedwith mapping-grade receivers may, therefore, beof limited utility when ecological questions arefocused on small habitat patches (2 to 5 m2). How-ever, small patches should not be ignored becausethey can support assemblages that can equal therichness and diversity of large patches (Hirst andAttrill 2008).

Boundaries of habitat patches mediate the ex-change of individuals among different patches andcan enhance biodiversity (Ries et al. 2004; Wiens2002). Certain insects have higher densities nearpatch edges whereas others have higher densitiesaway from edges, and the amount of edge per

Environ Monit Assess (2011) 179:123–135 133

unit area can alter densities and diversity within apatch (Olson and Andow 2008). While perimeterlength of habitat patches can be difficult to mea-sure exactly because of its fractal dimension (e.g.,how long is the coastline of Britian?; Mandelbrot1967), ecologists define habitat patch perime-ters at a resolution they perceive to influencetheir study organisms. The perimeters of habitatpatches we identified in the Laramie River werehighly variable and typically overestimated whenthey were measured with a GPS, and there wasmore bias in smaller habitats with complex shapes(i.e., higher perimeter/area ratio). Consequently,caution must be used when estimating the lengthof habitat boundaries and computing landscapemetrics such as edge density of small patches.Overestimating the amount of patch boundaryin a landscape mosaic can bias how edge effectsare perceived to influence species movements andbiodiversity across a landscape of small habitats.Biased estimates of edge length can also overaccentuate the effects of human disturbances thatincrease landscape fragmentation (Bar Massadaet al. 2008).

Habitats can be temporally dynamic.They change in size, shape, and connectivity(Hilderbrand et al. 1999; Remshardt and Fisher2009). Baden et al. (2003) mapped eelgrass(Zostera marina) meadows along the Swedishcoast and documented a decline in distributionfor 50 of 69 meadows. Because eelgrass meadowswere larger than 1 ha, the GPS error in theirhabitat maps was negligible. Dauwalter andFisher (2008) mapped stream habitats overtime using a mapping-grade GPS receiver andfound that stream habitats shrunk and becamedisconnected during low-flow periods in late-summer and autumn. However, the reconnectionof these habitats during high winter flows allowedsmallmouth bass (Micropterus dolomieu) to accessthermally unique winter habitats. The habitatsused by smallmouth bass averaged 960 m2 (range10 to 4,092 m2), and our results suggest that theaccuracy in measuring the area of those habitatswas high. Webster and Cardina (1997) evaluatedthe use of GPS to monitor the growth andinvasion of a weed patch over time. They detecteda 113% increase in the area of a hemp dogbane(Apocynum cannabinum) patch over 1 year. This

patch was large enough (expanding from 116to 241 m2) that measurements of area shouldbe relatively accurate and precise. Detectingmeaningful habitat change over time requiresknowledge of how much spatial overlap can belost when measuring patches with a GPS.

Global positioning systems are increasingly be-ing used to monitor aquatic and terrestrial habi-tats. If small, individual habitats less than 5 m2 areof interest then detecting changes in size of indi-vidual patches will be difficult using a mapping-grade GPS receiver. Mapping such small patchesmay require a more accurate and precise survey-grade GPS receiver or traditional grid-based sys-tem that can be georeferenced (Matter 2006).For studies focused on large patches greater than50 m2 using a mapping-grade GPS receiver isprobably acceptable and provides a useful tech-nique for characterizing habitat conditions atspatial scales below those that require remotesensing. When habitat patches are between 5 to50 m2, researchers should carefully evaluate theerrors associated with a mapping grade GPS re-ceiver against their research objectives to deter-mine if the GPS error will allow quantification ofhabitat patch characteristics at the desired levelof accuracy or detection of sufficient levels ofchange over time. For example, given the level ofGPS error we observed with the Trimble ProXRS,detecting a 40% increase in patch size with reason-able certainty (power of 60%) would be possiblefor a 100-m2 patch but not a 25-m2 patch. How-ever, detecting 100% change in patch size wouldbe possible for even 25 m2 patches. Our resultsindicate that using a GPS to map habitat patchesin streams is a viable tool for researchers andmanagers if care is taken to consider the effectsof horizontal GPS error on the measurement ofpatch characteristics.

Acknowledgements S. Prager, J. Hammerlinck, A.J.Carlson, E.S. Hansen, K. Fesenmyer, and K.G. Gerowassisted with this research and provided helpful reviews ofthe manuscript.

References

Allen, C. D. (1994). Ecological perspective: Linking ecol-ogy, GIS, and remote sensing to ecosystem manage-ment. In V. A. Sample (Ed.), Remote sensing and GIS

134 Environ Monit Assess (2011) 179:123–135

in ecosystem management (pp. 111–139). Washington:Island.

Angermeier, P. L., & Schlosser, I. J. (1989). Species-arearelationships for stream fishes. Ecology, 70, 1450–1462.

August, P., Michaud, J., Labash, C., & Smith, C. (1994).GPS for environmental applications: Accuracy andprecision of location data. Photogrammetric Engineer-ing and Remote Sensing, 60, 41–45.

Baden, S., Gullstrëm, M., Lundén, B., Pihl, L., &Rosenberg, R. (2003). Vanishing seagrass (Zosteramarina, L.) in Swedish coastal waters. Ambio, 32, 374–377.

Bain, M. B., & Stevenson, N. J. (1999). Aquatic habitatassessment: Common methods. Bethesda: AmericanFisheries Society.

Bar Massada, A., Gabay, O., Perevolotsky, A., & Carmel,Y. (2008). Quantifying the effect of grazing and shrub-clearing on small scale spatial pattern of vegetation.Landscape Ecology, 23, 327–339.

Baxter, C. V. (2002). Fish movement and assemblage dy-namics in a Pacific Northwest riverscape. Ph.D. disser-tation, Oregon State University, Corvallis.

Belica, L. A. T., & Rahel, F. J. (2008). Movements ofcreek chubs, Semotilus atromaculatus, among habitatpatches in a plains stream. Ecology of Freshwater Fish,17, 258–272.

Bolstad, P., Jenks, A., Berkin, J., Horne, K., & Reading,W. H. (2005). A comparison of autonomous, WAAS,real-time, and post-processed global positioning sys-tems (GPS) accuracies in northern forests. NorthernJournal of Applied Forestry, 22, 5–11.

Braun, C. E. (2005). Techniques for wildlife investigationsand management. Bethesda: The Wildlife Society.

Conley, R., Cosentino, R., Hegarty, C. J., Kaplan, E. D.,Leva, J. L., Uijt de Haag, M., et al. (2006). Per-formance of stand-alone GPS. In E. D. Kaplan &C. J. Hegarty (Eds.), Understanding GPS: Princi-ples and applications (pp. 301–378). Norwood: ArtechHouse.

Cosentino, R. J., Diggle, D. W., Uijt de Haag, M., Hegarty,C. J., Milbert, D., & Nagle, J. (2006). Differential GPS.In E. D. Kaplan & C. J. Hegarty (Eds.), UnderstandingGPS: Principles and applications (pp. 379–458). Nor-wood: Artech House.

Dauwalter, D. C., & Fisher, W. L. (2008). Spatial and tem-poral patterns in stream habitat and smallmouth basspopulations in eastern Oklahoma. Transactions of theAmerican Fisheries Society, 137, 1072–1088.

Dauwalter, D. C., Fisher, W. L., & Belt, K. C. (2006). Map-ping stream habitats with a global positioning system:Accuracy, precision, and comparison with traditionalmethods. Environmental Management, 37, 271–280.

Deckert, C., & Bolstad, P. V. (1996). Forest canopy, ter-rain, and distance effects on global positioning systempoint accuracy. Photogrammetric Engineering and Re-mote Sensing, 62, 317–321.

Fisher, W. L. (2004). Future of geographic information sys-tems in fisheries. In W. L. Fisher & F. J. Rahel (Eds.),Geographic information systems in f isheries (pp. 259–266). Bethesda: American Fisheries Society.

Gallagher, A. S. (1999a). Drainage basins. In M. B. Bain& N. J. Stevenson (Eds.), Aquatic habitat assessment:Common methods (pp. 25–34). Bethesda: AmericanFisheries Society.

Gallagher, A. S. (1999b). Lake morphology. In M. B. Bain& N. J. Stevenson (Eds.), Aquatic habitat assessment:Common methods (pp. 165–173). Bethesda: AmericanFisheries Society.

Gerow, K. G. (2007). Power and sample size estimationtechniques for fisheries management: assessment anda new computational tool. North American Journal ofFisheries Management, 27, 397–404.

Gleason, H. A. (1922). On the relation between species andarea. Ecology, 3, 158–162.

Hilderbrand, R. H., Lemly, A. D., & Dolloff, C. A. (1999).Habitat sequencing and the importance of discharge ininferences. North American Journal of Fisheries Man-agement, 19, 198–202.

Hirst, J. A., & Attrill, M. J. (2008). Small is beautiful:An inverted view of habitat fragmentation in seagrassbeds. Estuarine Coastal and Shelf Science, 78, 811–818.

Hulbert, I. A. R., & French, J. (2001). The accuracy of GPSfor wildlife telemetry and habitat mapping. Journal ofApplied Ecology, 38, 869–878.

Jeffrey, J. D., & Edds, D. R. (1997). A global positioningsystem for aquatic surveys. Fisheries, 22(12), 16–20.

Johnson, C. E., & Barton, C. C. (2004). Where in theworld are my field plots? Using GPS effectively inenvironmental field studies. Frontiers in Ecology andthe Environment, 2, 475–482.

Knutson, M. G., Sauer, J. R., Olsen, D. A., Mossman,M. J., Hemesath, L. M., & Lannoo, M. J. (1999).Effects of landscape composition and wetland frag-mentation on frog and toad abundance and speciesrichness in Iowa and Wisconsin, USA. ConservationBiology, 13, 1437–1446.

Kocik, J. F., & Ferreri, C. P. (1998). Juvenile productionvariation in salmonids: population dynamics, habitat,and the role of spatial relationships. Canadian Journalof Fisheries and Aquatic Sciences, 55, 191–200.

Krauss, J., Steffan-Dewenter, I., Muller, C. B., &Tscharntke, T. (2005). Relative importance ofresource quantity, isolation and habitat quality forlandscape distribution of a monophagous butterfly.Ecography, 28, 465–474.

Le Pichon, C., Gorges, G., Boët, P., Baudry, J., Goreaud,F., & Faure, T. (2006). A spatially explicit resource-based approach for managing stream fishes in river-scapes. Environmental Management, 37, 322–335.

Le Pichon, C., Gorges, G., Baudry, J., Goreaud, F., &Boët, P. (2009). Spatial metrics and methods for river-scapes: Quantifying variability in riverine fish habitatpatterns. Environmetrics, 20, 512–526.

Leung, Y., Jiang-Hong, M., & Goodchild, M. F. (2004). Ageneral framework for error analysis in measurement-based GIS Part 4: Error analysis in length and areameasurements. Journal of Geographical Systems, 6,403–428.

Leva, J. L., Uijt de Haag, M., & Van Dyke, K. (1996). Per-formance of standalone GPS. In E. D. Kaplan (Ed.),

Environ Monit Assess (2011) 179:123–135 135

Understanding GPS: Principles and applications (pp.237–320). Boston: Artech House.

Linke, J., Franklin, S. E., Huettmann, F., & Stenhouse,G. B. (2005). Seismic cutlines, changing landscapemetrics and grizzly bear landscape use in Alberta.Landscape Ecology, 20, 811–826.

Liu, C. J. (2002). Effects of selective availability on GPSpositioning accuracy. Southern Journal of AppliedForestry, 26, 140–145.

MacArthur, R. H., & Wilson, E. O. (1967). The theory ofisland biogeography. Princeton: Princeton UniversityPress.

Mandelbrot, B. (1967). How long is the coast of Britain?Statistical self-similarity and fractional dimension. Sci-ence, 156, 636–638.

Matter, S. F. (2006). Changes in landscape structure de-crease mortality during migration. Oecologia, 150,8–16.

Naesset, E., & Jonmeister, T. (2002). Assessing pointaccuracy of DGPS under forest canopy before dataacquisition, in the field and after postprocessing. Scan-dinavian Journal of Forest Research, 17, 351–358.

O’Connor, R. R., & Rahel, F. J. (2009). A patch perspec-tive on summer habitat use by brown trout Salmotrutta in a high plains stream in Wyoming, USA. Ecol-ogy of Freshwater Fish, 18, 473–480.

Olson, D., & Andow, D. (2008). Patch edges and insectpopulations. Oecologia, 155, 549–558.

Palmer, M. W., & White, P. S. (1994). Scale dependenceand the species-area relationship. American Naturalist,144, 717–740.

Remshardt, W. J., & Fisher, W. L. (2009). Effects ofvariation in streamflow and channel structure onsmallmouth bass habitat in an alluvial stream. RiverResearch and Applications, 25, 661–674.

Ries, L., Fletcher, R. J., Battin, J., & Sisk, T. D. (2004).Ecological responses to habitat edges: Mechanisms,models, and variability explained. Annual Review ofEcology, Evolution, and Systematics, 35, 491–522.

Samama, N. (2008). Global positioning: Technologies andperformance. Hoboken: Wiley.

Schilling, K. E., & Wolter, C. F. (2000). Application of GPSand GIS to map channel features in Walnut Creek,Iowa. Journal of the American Water Resources Asso-ciation, 36, 1423–1434.

Schlosser, I. J., & Angermeier, P. L. (1995). Spatial varia-tion in demographic processes of lotic fishes: concep-tual models, empirical evidence, and implications forconservation. In J. L. Nielsen (Ed.), Evolution and theaquatic ecosystem: Def ining unique units in populationconservation (pp. 392–401). Bethesda: American Fish-eries Society. Symposium 17.

Sigrist, P., Coppin, P., & Hermy, M. (1999). Impact offorest canopy on quality and accuracy of GPS. Inter-national Journal of Remote Sensing, 20, 3595–3610.

Smith, G. F., & Greenhawk, K. N. (1998). Shellfishbenthic habitat assessment in the Chesapeake Bay:

progress toward integrated technologies for mappingand analysis. Journal of Shellf ish Research, 17, 1433–1437.

Swihart, R. K., Atwood, T. C., Goheen, J. R., Scheiman,D. M., Munroe, K. E., & Gehring, T. M. (2003). Patchoccupancy of North American mammals: Is patchinessin the eye of the beholder? Journal of Biogeography,30, 1259–1279.

Torgersen, C. E., Gresswell, R. E., & Bateman, D. S.(2004). Pattern detection in stream networks: Quan-tifying spatial variability in fish distribution. In T.Nishida, P. J. Kailola, & C. E. Hollingworth (Eds.),GIS/Spatial analyses in f ishery and aquatic sciences(Vol. 2, pp. 405–420). Saitama: Fishery and AquaticGIS Research Group.

Trimble Navigation Limited (1997). Characterizing accu-racy of Trimble Pathf inder mapping receivers. West-minster: Trimble Navigation Limited. Document 101Commercial Systems Group, Surveying and MappingSystems.

Trimble Navigation Limited (2005). Datasheet: GPSPathf inder Pro XRS reciever. Westminster: TrimbleNavigation Limited.

Valley, R. D., Drake, M. T., & Anderson, C. S. (2005).Evaluation of alternative interpolation techniques forthe mapping of remotely-sensed submersed vegetationabundance. Aquatic Botany, 81, 13–25.

Visscher, D. R. (2006). GPS measurement error and re-source selection functions in a fragmented landscape.Ecography, 29, 458–464.

Webster, T. M., & Cardina, J. (1997). Accuracy of a globalpositioning system (GPS) for weed mapping. WeedTechnology, 11, 782–786.

Wiens, J. A. (2002). Riverine landscapes: Taking landscapeecology into the water. Freshwater Biology, 47, 501–515.

Wing, M. G., & Eklund, A. (2007). Performance compar-ison of a low-cost mapping grade global positioningsystems (GPS) receiver and consumer grade GPS re-ceiver under dense forest canopy. Journal of Forestry,105, 9–14.

Wing, M. G., & Karsky, R. (2006). Standard and real-timeaccuracy and reliability of a mapping-grade GPS in aconiferous western Oregon forest. Western Journal ofApplied Forestry, 21, 222–227.

Wing, M. G., Eklund, A., & Kellogg, L. D. (2005).Consumer-grade global positioning system (GPS) ac-curacy and reliability. Journal of Forestry, 103, 169–173.

Wing, M. G., Eklund, A., Sessions, J., & Karsky, R.(2008). Horizontal measurement performance of fivemapping-grade global positioning system receiverconfigurations in several forested settings. WesternJournal of Applied Forestry, 23, 166–171.

Wyoming Gap Analysis (1996). Land cover for Wyoming,metadata. Laramie: Spatial Data and VisualizationCenter.