United States 

Department of 

Agriculture Estimates of Recreation Use 

Forest Service in the White River Drainaae. 

Northeastern Forest 

Experiment Station Vermont 

Research Paper NE-658 

Ronald J. Glass 

Gerald S. Walton 

Herbert E. Echelberger 
Abstract 

An observation technique that incorporates modified, stratified sampling was 

used to estimate in-stream recreation use in the White River Drainage in Vermont. 

Results were reported by season (spring, summer, and fall); day of week 

("weekends and holidays" and "weekdays"); time of day (early morning, 

midmorning, early afternoon, and evening); kind of activity (fishing, boating, 

floating, swimming, and other); and portion of stream (main stem, tributaries, 

and feeders). Summer had the highest use followed by spring and fall. Except 

in fall, weekends and holidays received substantially more recreation use than 

weekdays. Midmorning and early afternoon had the greatest use. Fishing was 

the predominant use in the spring, while swimming was the single most popular 

activity in summer. The main stem received the greatest use followed by tributaries 

and feeders. 

The Authors 

RONALD J. GLASS is a research economist with the Forest Recreation Research 

unit at the Northeastern Forest Experiment Station's George D. Aiken Forestry 

Sciences Laboratory at Burlington, Vermont. He also has worked with the 

Economic Research Service of the U.S. Department of Agriculture and the 

Alaska Department of Fish and Game. He received M.S. and Ph.D. degrees in 

economics from the University of Minnesota and the State University of New 

York at Syracuse. 

GERALD S. WALTON is a mathematical statistician with the biometrics Group 

of the Northeastern Forest Experiment Station's Center for Biological Control at 

Hamden, Connecticut. He earned a B.A. degree in Mathematics from the 

University of California at Berkeley and a Master of Forest Science degree 

from Harvard University. 

HERBERT E. ECHELBERGER is a research social scientist and Project Leader 

of the Forest Recreation Research unit at the Northeastern Forest Experiment 

Station at Burlington, Vermont. He earned B.S. and M.S. degrees in Forestry 

from Southern Illinois University and a Ph.D. in resource management from 

the State University of New York's College of Environmental Science and Forestry 

at Syracuse. 

Manuscript received for publication 17 June 1991 

Northeastern Forest Experiment Station 

5 Radnor Corporate Center 

100 Matsonford Road, Suite 200 

P.O. Box 6775 

Radnor, Pennsylvania 19087-4585 

February 1992 
Introduction 

Reliable information on the kinds and intensity of stream 

recreation use in the Northeast is needed by resource 

management agencies at both the state and Federal 

level. As a result, the Green Mountain National Forest, 

USDl Fish and Wildlife Service, and Vermont Fish and 

Wildlife Department joined the Northeastern Forest 

Experiment Station in a study of recreation use in the 

'White River Drainage, Vermont (Fig. 1). Specifically, an 

observation method was used to estimate recreation use 

on the White River and its tributaries. 

'The White River Drainage has special interest because it 

is a principal component of a multimillion dollar state and 

federal effort' to reestablish a viable population of sea-run 

Atlantic salmon (Salmo salar) in the Connecticut River 

Ilrainage. The initial return of sea-run salmon has been 

observed in the White River since 1986, so baseline 

information on recreation use was sought to help determine 

possible impacts if a salmon sport fishery is established. 

r4ls0, there is concern that heavy human use may disturb 

salmon in holding pools. 

Apart from restoration of Atlantic salmon, reliable estimates 

of recreation use on the White River are needed for 

several other purposes. Both state and Federal agencies 

desire information on human use patterns to identify sites 

to be considered for public acquisition. Further, budget 

allocations by public agencies are being increasingly 

influenced by the, level of human use. Previous studies 

on the White River have been limited in scope. In 1979, 

Manning conducted a study of four Vermont rivers, including 

the White, that focused on the relationship of anglers to 

other recreationists. The results, based on personal 

interviews, indicated that anglers, the most frequent 

recreationists, experienced the highest level of conflict 

vvith other recreational users. 

Elsewhere, recreation use has been estimated successfully 

by the use of mail questionnaires or personal interviews 

(Cushwa and McGinnes 1963; Echelberger and Moeller 

1977), but such techniques are not readily adaptable to 

tlie conditions that exist on the White River. Approaches 

that combine interviews and observation (James et al. 

1971; Rawhouser et al, 1989) have been effective in 

some situations but, again, were not readily adaptable to 

cur White River study. 

The use of secondary variables such as traffic counts 

also have been successful (James 1971; James and Rich 

1966), but the easy accessibility of much of the White 

River Drainage and its location along multipurpose 

transportation corridors made this technique inappropriate 

for our study. 

Finally, observation techniques used to estimate recreation 

use and participant behavior have proven successful in 

certain situations. Instantaneous count sampling has been 

effective for estimating person-days of recreational activity 

on small areas (Echelberger and Moeller 1973; Schreuder 

et al. 1975; Tyre and Siderelis 1979). However, this 

approach does not lend itself to a study area with the 

characteristics of the White River Drainage. Observation 

also has been used successfully to quantify recreational 

activity and behavior of recreationists (Heberlein and 

Dunwiddie 1979). 

We decided to record stream use through direct observation 

from a distance for several reasons. First, distant 

observation is less costly and less of an infringement on 

users than approaches requiring personal contact. Second, 

the characteristics of the White River Drainage are such 

that use can be observed conveniently whereas personal 

contact with some users (e.g., tubers and kayakers) is 

likely to be difficult. The effective use of mail questionnaires 

to the general population was hampered by the fact that 

there is a large population with relatively easy access to 

the Drainage but only a small proportion of the population 

uses the resource for recreation. Third, Federal agencies 

must obtain Office of Management and Budget approval 

to use personal-contact survey instruments, a timeconsuming procedure that can result in critical delays in 

monitoring changes in stream use. Finally, this study was 

designed to estimate recreation use for varying time periods, 

and, therefore, differs from other observation studies that 

have concentrated on estimates of numbers of users at 

specific points in time. And our study was designed to 

estimate recreation use for the entire system of the White 

River (main stem, tributaries, and feeders) while previous 

work concentrated on the main stem. 

'A cooperative agreement between state and Federal agencies 

to reestablish the Atlantic salmon in the Connecticut River Basin 

was established in 1967. The Connecticut River Atlantic Salmon 

Commission was formed in 1984. 
White River Jct. 

0 

Figure 1 .-White River Drainage in Vermont. 

2 
Methods 

'The White River Drainage is a complex network of rivers 

isnd streams encompassing a 710-square-mile area. A list 

of streams in this area was provided by the Vermont 

13epartment of Fish and Wildlife. A stratified sampling 

technique was used to estimate stream-corridor recreation 

use within the White River Drainage from April 11 to 

October 25 during the 1987 Vermont trout fishing season. 

We included any such activity that occurred one-half hour 

before sunrise to one-half hour after sunset. Streams 

were visited and segmented by natural boundaries such 

as tributaries, or to a 5-foot minimum width. 

The field observation was designed to accommodate one 

person working 5 days a week, 8 hours a day. The work 

schedule was designed to include every weekend day 

and holiday; the remaining days in the week were selected 

at random to fill the week's work schedule. To accommodate 

the full range of times during any day, three work shifts 

were designated: the early shift was an 8-hour period 

beginning one-half hour before sunrise; the late shift was 

an 8-hour period ending one-half hour after sunset; and 

tlhe middle shift was from 8 a.m. to 4 p.m. The work 

shift for any day was assigned at random, as described 

in the Appendix, to complete the work schedule for the 

season. 

L.onger stretches of streams were divided into segments 

using boundaries that could be identified easily in the 

field. The order in which the segments were to be observed 

was randomly selected as described in the Appendix, and 

provided prior to the field work. This scheme allowed the 

field crew to visit as many stream segments as possible 

during a work shift. The observer, in recording the required 

information on a visit to a given segment (see data sheet, 

F'igure 2) was instructed to proceed along the stream 

segment and record all individuals engaged in a stream 

recreation activity and the time the activity was observed. 

hny activity that was recreational in nature and took place 

on the stream or directly adjacent to it was recorded. To 

better facilitate reporting and attain higher confidence in 

results, recreational activities were divided into five 

categories: fishing, boating, floating, swimming, and "other" 

(see footnote, Table 3). 

Results 

Recreation use by activity type was estimated for each 

category of stream segment-main stem, tributaries, and 

feeder streams. In total daily recreation use for the entire 

White River system, weekends and holidays were the 

busiest during the spring and summer seasons (Table 1). 

Estimated use was 2,401 person-hours per day during 

summer weekends and holidays compared to 874 personhours for weekdays. By contrast, daily use was estimated 

at 873 person-hours for fall weekdays but only 274 personhours for weekends and holidays. Further, the main stem 

received the most use during the spring and summer. 

The tributaries were used more heavily in the fall than in 

spring. Viewing of fall foliage (even when limited to viewing 

from bridges, rocks, or islands), was a major seasonal 

use, especially on the tributaries. Feeder streams had the 

least use during each of the seasons. It should be noted 

that standard errors are relatively high in all cases, so a 

more detailed breakdown of recreational activities, as they 

vary with day of the week and season of the year, would 

not provide reliable results. 

The middle of the day (midmorning and early afternoon) 

generally was the most popular time of recreation use 

(Table 2). A notable exception was during summer 

weekends and holidays when the evening period received 

more use than midmorning but not as much as early 

afternoon. The latter had the highest use ('1,352 personhours per day) of any time period for any season. In all 

cases except fall weekends and holidays, the early morning 

received the least use. Again, relatively high standard 

errors discouraged a mdre detailed examination of use 

estimates by kinds of recreation. 

Participation in various kinds of recreation varied 

considerably by season of the year and day of the week 

(Table 3). In person-hours of participation, fishing was 

most popular in the spring but ranked third in popularity 

during summer. Most fishing occurred on weekends and 

holidays. As with most recreational activities, fishing 

declined substantially during the fall. Boating occurred 

most frequently during the spring, primarily on weekends 

and holidays. Floating and swimming were almost 

exclusively summer activities, though there was limited 

participation in the spring. The catchall "other" category 

showed substantial use in the fall, much of this related to 

foliage viewers who clambered over rocks or viewed from 

bridges and other spots within the stream banks. 

Total recreation use for the entire White River system 

reached nearly 205,000 person-hours during 1987 (Table 

4). For many managerial purposes, especially those dealing 

with the threat of potential use conflicts, the upper levels 

of the confidence interval may provide more meaningful 

insights. At 90 percent probability, it is estimated that 

total recreation use over the entire season did not exceed 

208,350 person-hours. Considering the miles of stream 

involved, the upper level at 90 percent probability amounts 

to 555 person-hours per mile of stream for the entire 

season. 

On a seasional basis, summer had the heaviest use but 

also included a larger number of days than spring or fall. 

Fishing and swimming were the most popular activities, 

though "other" (which includes a number of specific 

activities) was the largest category. This category included 

a large proportion of passive activities such as sunbathing 

and laying on rocks during the summer. During spring, 

fishing was the major on-stream recreation use in the 

drainage. In summer, swimming was the leading activity, 

followed by fishing and boating. While fishing was the 

leading individual activity during the autumn, the "other" 

category had the highest use. 
Table 1.-Estimates of daily on-stream recreation use in White River Drainage by season of year, day of week, 

and stream segment, 1987 

Season Day of week 

Spring Weekdays 

Weekends and 

holidays 

Summer Weekdays 

Fall 

Weekends and 

holidays 

Weekdays 

Statistical 

measures 

Person-hours of use (no.) 

Standard error 

Observational runs (no.) 

Person-hours of use (no.) 

Standard error 

Observational runs (no.) 

Person-hours of use (no.) 

Standard error 

Observational runs (no.) 

Person-hours of use (no.) 

Standard error 

Observational runs (no.) 

Person-hours of use (no.) 

Standard error 

Observational runs (no.) 

Stream category 

Main stem Tributaries Feeders Entire system 

148 59 0 207 

35 35 0 50 

52 20 12 84 

Weekends and Person-hours of use (no.) 136 138 0 274 

holidays Standard error 39 66 0 76 

Observational runs (no.) 30 21 19 70 

Table 2.-Estimated daily'on-stream recreation use by time of day, day of week, and season of year for White 

River Drainage, 1987 

Early morning Midmorning Early afternoon Evening All day 

Season Day of week Person Standard Person Standard Person Standard Person Standard Person Standard 

hours error hours error hours error hours error hours error 

Number Number Number Number Number 

Spring Weekdays 29 20 41 13 101 40 36 19 207 50 

Weekends and 

holidays 32 16 753 330 486 124 198 84 1,469 363 

Summer Weekdays 47 24 246 80 451 103 130 36 874 137 

Weekends and 

holidays 122 86 343 70 1,352 243 584 193 2,401 330 

Fall Weekdays 79 21 312 178 373 141 109 44 873 232 

Weekends and 

holidays 43 18 101 48 116 56 14 9 274 76 
Table 3.-Estimated daily on-stream recreation use by kind of activity, day of week, and season of year for 

White River Drainage, 1987 

Person hours (no.) Standard 

error 

Season Day of week Fishinga Boatingb FloatingC Swimmingd Othere Total of total 

Spring Weekdays 161 5 0 15 27 207 50 

Weekends and 

holidays 838 258 4 20 349 1,469 363 

Summer Weekdays 167 14 49 249 396 874 137 

Weekends and 

holidays 469 72 294 727 838 2,401 330 

Fall Weekdays 37 0 0 0 836 873 232 

Weekends and 

holidays 50 14 0 0 21 0 274 76 

alncludes fishing from boat. 

blncludes canoeing and kayaking. 

Clncludes inner tubes and rafts. 

dlncludes swimming and wading. 

elncludes a wide range of recreational activities that occurred on the stream corridor, e.g., on rocks and islands 

surrounded by water, bridges over the water, and in the water. Activities included sunbathing, picnicking, photography, 

viewing scenery, sitting on rocks, and other stream, uses. While these are an important component of recreational use 

in total, they represent a small share of overall use individually and tend to be passive in nature, Le., unlikely sources 

of conflict. 

Table 4.-Estimated on-stream recreation use by kind of activity and season of year for White River Drainage, 

1 987 

Person hours (no.) Standard 

Season Fishing Boating Floating Swimming Other Total error 

of total 

Spring 14,874 3,231 50 687 4,996 23,838 1,286 

Summer 29,240 3,580 14,060 44,641 59,795 151,316 2,293 

All seasons 45,933 7,021 14,110 45,328 92,182 204,574 2,926 

Table 5.-Estimated on-stream recreation use per mile of stream by segment category and season of year for 

White River Drainage, 1987 

Person hours (no.) 

Main stem Tributaries Feeders Entire system 

Season 

- Season Day Season Day Season Day Season Day 

Spring 324.4 7.72 42.6 1.01 18.5 0.44 63.5 1.51 

Summer 1,986.7 17.74 378.3 3.34 46.6 0.42 403.1 3.60 

Fall 145.8 3.31 149.2 3.39 4.6 0.10 78.4 , 1.78 

Survev period 2,456.93 12.41 570.2 2.88 69.6 0.19 544.9 2.75 
When estimates of recreation use estimates are reduced 

to a per-mile basis, the preponderance of use and potential 

for conflicts within the main stem is more apparent (Table 

5). For example, recreation use in the main stem during 

the summer season approached 2,000 person-hours per 

mile, slightly less than 18 person-hours per mile of stream 

per day. Estimates of use per mile were substantially 

lower for tributaries than for the main stem except during 

the fall. Estimated daily use on feeder streams was less 

than 0.5 person-hour per mile for each season. 

Discussion 

Despite the large standard errors, the observation technique 

used in this study is a feasible means of estimating 

recreation use. Data collected during the 1987 field season 

was sufficient to provide baseline information on levels of 

use and indicate the potential for conflict among 

recreationists. Relatively light use in feeder streams and 

major tributaries does not assure an absence of conflictthese stream categories are smaller and individuals might 

be more easily disturbed by the activities of others. Even 

in the main stem during the summer season, daily recreation 

use was less than 18 person-hours per mile. However, 

use patterns were highly irregular such that conflicts might 

be serious during peak periods in loca-tions that attract 

diverse groups, e.g., fishermen vs. floaters. 

Since recreation use is heaviest and most diverse in 

summer, this is the time that conflicts would be most 

likely. For instance, swimming occurs primarily in the 

summer and in the main stem, where most boating, floating, 

and sunning also occur. Undoubtedly, not all of these 

activities are totally compatible when pursued at the same 

place and at the same time. However, total recreation 

use, even in the main stem, has yet to reach the level 

where several activities cannot occur simultaneously so 

long as participants do not demand specific locations at 

the same time. 

The White River study was designed to determine if the 

kind and extent of current recreation use could result in 

conflicts. With regard to the main stem, where the level 

of recreation is sufficient to raise concern, a more detailed 

follow-up observation study should be implemented. Also, 

the level of human use of specific salmon holding pools 

was not estimated, but the results indicate moderately 

high use of the main stem, where most salmon holding 

pools are located. And salmon are found in these pools 

mostly during July and August, which coincides with the 

heaviest overall human use. As part of a habitat survey 

of the White River Drainage by the USDl Fish and Wildlife 

Service, in conjunction with the Atlantic salmon restoration 

program, potential salmon holding pools are being identified. 

When this process is completed, the observation technique 

used in this study can be modified so that the amount 

and timing of human use can be estimated for specific 

locations. There is still some uncertainty regarding the 

likely impact of various kinds of recreation on salmon 

behavior; no information is available on the ability of 

salmon to adjust to periodic disturbances. 

With regard to the acquisition of additional access points 

to the White River by public agencies, the main stem 

appears to have the greatest need. Procurement decisions 

must take into account site-specific river attributes that 

are required to satisfy public wants, but current levels do 

suggest that demand exists. While there is posting against 

trespass for all stream categories, the relatively light 

recreation use along feeders and main tributaries, 

considered with their greater numbers of miles, suggests 

that current alternative public access may be sufficient for 

these segments. However, access and parking is more 

likely to become a problem (if it isn't already) along the 

more heavily used but shorter main stem. 
Date: 

Stream: 

Segment of stream: -- -- 

Begin point: 

EEnd point: 

Begin time: 

EZnd time: 

Clbservation time: 

Weather conditions: 

Rain Cloudy 

Snow Clear 

Fog 

Air temperature (F) 

Observer: 

Mode of transportations: -- 

Distance travelled: 

Road miles: 

Stream miles: - 

Water conditions: 

Clear Muddy 

Frozen over 

Water level: 

Flooded Low 

High Dry 

Normal 

Time 

of observation Activity 

Landmark 

(record time 

of reaching 

even if no 

Number of activities 

participants are occurring) Comments 

Figu~re 2.-Stream use data sheet. 
Literature Cited 

Cushwa, C. T.; McGinnes, 6. S. 1963. Sampling 

procedures and estimates of year-round recreation 

use on 100 square miles of the George Washington 

National Forest. In: Trefethen, James B., ed. 

Proceedings, 28th North American wildlife and natural 

resource conference; 1963 March 4-6 [Publisher name 

unknown]: 457-465. 

Echelberger, H. E.; Moeller, G. H. 1973. Toward a.better 

understanding of recreational boating in the 

Adirondack Lakes region. Water Resources Bulletin. 

9(6): 1266-1 272. 

Echelberger, H. E.; Moeller, G. H. 1977. Use and users 

of the Cranberry Backcountry in West Virginia: 

insights for eastern backcountry management. Res. 

Pap. NE-363. Upper Darby, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 

Heberlein, T. A.; Dunwiddie, P. 1979. Systematic 

observation of use levels, campsite selection, and 

visitor characteristics at High Mountain Lake. Journal 

of Leisure Research. 1 1 (4): 307-326. 

James, G. A. 1971. Inventorying recreation use. In: 

Recreation symposium proceedings. October 12-1 4; 

Syracuse, NY. Upper Darby, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station: 78-95. 

James, G. A.; Rich, J. L. 1966. Estimating recreation 

use on a complex of developed sites. Res. Note 

SE-64. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southeastern Forest Experiment 

Station. 

James, G. A.; Wingle, P. H.; Griggs, J. D. 1971. Estimating 

recreation use on large bodies of water. Res. Pap. 

SE-79. Asheville, NC: US. Department of Agriculture, 

Forest Service, Southeastern Forest Experiment 

Station. 

Kendall, M. G.; Stuart, A. 1966. The advanced theory 

of statistics. Vol. 3, design and analysis, and timeseries. New York: Hafner Publishing Co. 

Manning, R. E. 1979. Behavioral characteristics of 

fishermen and other recreationists on four Vermont 

rivers. Transactions of the American Fisheries Society. 

108: 536-541 . 

Rawhouser, D.; Charles, C. C.; Grassing, L.; Krumpe, E. 

E.; McLaughlin, W. 1989. Cooperative research for 

monitoring recreation use of the lower Salmon 

River. Journal of Park and Recreation Administration. 

7(1): 41 -57. 

Schreuder, H. T.; Tyre, G. L.; James, G. A. 1975. Instantand interval-count sampling: two new techniques 

for estimating recreation use. Forest Science l(1): 

40-44. 

Tyre, G. L., Siderelis, C. D. 1979. Instant count sampling: 

a technique for estimating recreation use in 

municipal settings. Leisure Sciences. 2(2): 173-179. 
Appendix 

Sampling Design 

The White River Drainage of Vermont was segmented 

into sampling units prior to the field-observation season 

klsing the criteria that length of each segment be short 

enough so that an observer could survey its entire length 

in 4 hours or less, and that the boundaries of an individual 

segment could be designated clearly. Ninety-four segments 

were identified and classified by type of stream (main 

stem, tributary, or feeder stream) and mode by which 

they would be surveyed (riding, walking, or both). The 

seven occupied cells in Table 6 are referenced as "stream 

type-access" categories and serve to classify all stream 

segments. The time (to the nearest hour) for an observation 

rlun for a segment in each access class was estimated 

b~efore the actual field work. 

The strategy for sampling was to establish probabilities of 

selection in proportion to anticipated use and inversely 

proportional to anticipated cost. Using the "estimated 

average observation time" to represent cost, anticipated 

use (which was established in consultation with the staff 

of the resource management agencies) per unit length of 

stream and the total length of stream in each type-access 

category, probabilities for choosing segments of a given 

access category were computed. It is prudent to have 

probabilities sufficiently large to assure at least two 

observations in each category, so minimums were imposed 

arnd new probabilities computed. The final probabilities 

used in selecting a type-access category are shown in 

Table 7. A type-access category was selected using the 

probabilities, then one of the segments within that category 

was chosen with equal probability. The values of the 

probabilities of selection affect the optimum utilization of 

sampling resources, but, as will become clear, the values 

are not an issue in the validity of the estimates of recreation 

use. A computer program using the probabilities of selection 

in Table 2 was used to generate a list of segments to 

be sampled; this list was supplied to the field crew before 

the field season. Segments to be visited were taken in 

sequence from the list. 

The segment itself is an incomplete sample-unit specification 

since the day of the week and the work shift in which 

the sampling is to be done also must be selected. (Formally, 

all possible segments and all possible times of sampling 

would be the sampling universe, but it is not useful to 

us,e this formalization here for reasons that will become 

clear.) All weekend days and holidays were scheduled for 

observation, but the probability that a particular workday 

in the week would be chosen is lln where there are n 

workdays in the week. It was assumed that recreation 

use during the morning work shift was about one-half 

that in the two later shifts, so the morning, midday, and 

ev'ening shifts were selected with probabilities .2, .4, and 

.4, respectively, for all days. Prior to the field seasons, a 

computer program was written to select weekdays to be 

worked and the shift time to be worked for all days for 

the entire trout fishing season. 

If the object of this study was to estimate some 

characteristic of the stream system that could be expressed 

as a total for all possible visits of some variable, the 

population parameter in question could be estimated using 

an unbiased estimator, as the probability that any visit 

occurs in the sample can be calculated (see Kendall and 

Stuart 1966, Chapter 39). However, this approach is not 

relevant because the purpose of the study is to evaluate 

person-hours of recreation. This parameter was not 

observed directly and cannot be expressed as the sum 

of the observations that were rrW3~l~red on visits to the 

stream segments. However, the probability sampling 

described in the last section can be expected to provide 

cost-effective estimates of recreation use. 

The method of estimation that was used can best be 

explained by an example. Suppose we are interested in 

determining a given recreation use (e.g., fishing) for a 

specific period during a workday (e.g., 9 a.m. to noon) 

during a given season and for a particular stem type-a" *cess 

category (major tributaries accessible by car). If the 

convention is adopted that the segment "belongs" to the 

9 to 12 time slot only if most of the observer's time was 

spent in that slot (regardless of the shift the observer 

was working), then it is possible to list all samples that 

might contribute information (including that no fishing 

occurred) about the "fishing use" that we wish to estimate. 

We also make the assumption that the use is constant 

during the period in question, that is, the 9 to 12 period 

and the sampling visits are short enough that the visit 

provides a "snapshot" of the entire use on the segment 

from 9 to 12. If this is the case, then z =x/d can be 

used to measure the number of persons fishing per unit 

length of stream at the time of day on a workday, where 

d is the length of the segment and x is the number of 

persons observed fishing. Now, let Di be the total length 

of all streams in the study that contribute to the cell, ti 

the length of time interval (3 h), nii l.he number of 

contributing visits, and zij be the average number of (fishing) 

users, per unit stream length, over nij visits. Then 

is an estimate of the person-hours of fishing during the 

time period j (9 to 12) in stream typeaccess i. This type 

of estimate can be summed over other stream type-access 

categories to obtain use by stream type (main stem, say) 

and/or summed over other times of day to obtain daily 

totals. Thus, all the estimates of recreational use that are 

required can be determined. 

What makes this method work and why aren't the sampling 

probabilities specifically used? First, the sampling probability 

for the work shift does not come into play since we have 

assumed that the use at a particular time and place 

would be the same regardless of what shift the observer 

was working, and because of the assumption that any 

observation on a particular segment in the 9 to 12 time 

period would have yielded the same information. This 

assumption of "uniformity of use" on a segment during 
any time period also is what justifies the calculation of 

zij, the average person per unit length of stream, for a 

particular period and stream type-access category, and its 

conversion to person-hours of use, Sij. If the observer 

saw 10 anglers on a 2-mile stream between 9 and 12, 

the estimate of (1 0/2)3 = 15 angler-hours per mile of stream 

holds whether 10 stayed the entire 3 hours or 30 took 

turns so that there were 10 on the segment at any time. 

The reason that probabilities of selection for a segment 

do not come into play in the example is that fishing use 

was calculated for a particular type-access category, and 

all segments within this category have an equal probability 

of being selected. The qj in the formula are averages of 

random variables (users per mile) with standard errors 

that may be estimated within a type-access category. The 

fact that the stream length and the length of the time 

interval, ti, are known values (i.e., not random variables) 

makes Sij a random variable measuring use for the 

segment, and the mean and standard errors of the zij 

provide the desired unbiased estimate and error estimate 

of use under the assumptions stated. Although the typeaccess categories were sampled with different probabilities, 

they were sampled independently so that estimates and 

standard errors for combinations of these categories are 

easily computed. The only difficulty is that the ti change 

with the season when they represent times after sunrise 

and before sunset. The' solution was to use average time 

over each day in any season. 

In summary, the stratified sampling probabilities were 

fabricated to make the field work cost-efficient in terms 

of the standard errors of the results. The usual stratified 

estimation methods could not be used since the population parameters of interest could not be expressed as 

sums of variables that were directly measured on a 

segment. Assumptions are specified that underly the 

estimation method used to obtain person-hours spent in 

various types of recreation activity in the study area at 

various times. 

Table 6.-Number of stream segments, estimated observation time, and anticipated use by stream type-access 

category 

Ride Walk Ride and walk 

Expected Expected Expected 

Stream No. of observation No. of observation No. of observation Anticipated 

category segments time segments time segments time relative use 

Hours Hours Hours 

Main stem 4 1 0 -- 2 3 100 

Tributaries 5 2 0 -- 11 4 50 

Feeders 8 1 26 4 38 3 1 

Table 7.-Probabilities of segment selection by stream type-access category and assessibility -- - - 

Ride Waik Ride and walk 

Stream Probability Miles Probability Miles Probability Miles 

category of selection of stream of selection of stream of selection of stream 

Number Number Number 

Main stem .426 29.6 -- -- .088 13.9 

Tributaries .I33 46.9 -- -- .I47 102.1 

Feeders ,060 27.7 .086 50.1 .060 105.1 
Glass, Ronald J.; Walton, Gerald; Echelberger, Herbert E. 1991. Estimates 

of recreational stream use in the White River drainage, Vermont. 

Res. Pap. NE-658. Radnor, PA: US. Department of Agriculture, Forest 

Service, Northeastern Forest Experiment Station. 10 p. 

An observation technique that incorporates Godified, stratified sampling 

was used to estimate in-stream recreation use in the White River Drainage 

in Vermont. Results were reported by season, day of week, time of day, 

kind of activity, and portion of stream. Summer had the highest use followed 

by spring and fall. Except in fall, weekends and holidays received substantially 

more recreation use than weekdays. Midmorning and early afternoon had 

the greatest use. Fishing was the predominant use in the spring, while 

swimming was the single most popular activity in summer. The main stem 

received the greatest use followed by tributaries and feeders. 

ODC 907.2 (743) 

Keywords: Stratified sampling, resource management 

I?r U.S. GOVERNMENT PRINTING OFFICE: 1991-448-139140.007 
Headquarters of the Northeastern Forest Experiment Station is in Radnor, Pennsylvania. Field laboratories are maintained at: 

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Orono, Maine, in cooperation with the University of Maine 

Parsons, West Virginia 

Princeton, West Virginia 

Syracuse, New York, in cooperation with the State University of New York, 

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University Park, ~enns~lvania, in cooperation with The Pennsylvania State University 

Warren, Pennsylvania 

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