Proceedings of the 2009 northeastern recreation research symposium

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Table 2.—Linear regression estimate for scenic beauty for September 2007 Ordinary Least Squares estimates using the 16 observations Dependent variable: September Scenic Beauty Variable coeffi cient std. error t-ratio p-value const 4.6401 0.4533 10.2358 0.0000 Forest 0.0001 0.0001 0.9746 0.3490 Agriculture 0.0007 0.0008 0.7974 0.4407 Barren and Developed -0.0140 0.0343 -0.4079 0.6905 P-value(F) 0.2232 R-squared 0.2962 Adjusted R-squared 0.1203 Table 3.—Linear regression estimate for scenic beauty for October 2007 Ordinary Least Squares estimates using the 16 observations Dependent variable: October Scenic Beauty Variable coeffi cient std. error t-ratio p-value Const 5.1550 0.3301 15.6166 0.0000 Forest 0.0002 0.0001 2.3066 0.0397 Agriculture 0.0005 0.0006 0.8629 0.4051 Barren and Developed -0.0343 0.0250 -1.3721 0.1951 P-value(F) 0.0205 R-squared 0.5443 Adjusted R-squared 0.4303 cover visible from all the points is the same for both months, diversity in leaf color in October (late fall) seems to add to the perceived beauty of the area. Th is result is consistent with previous fi ndings that people prefer diversity in the landscape of forested regions (Cherem and Traweek 1977, Ribe 1986, Axelsson-Lindgren and Sorte 1987). Forests are the most visible land cover type from all points along the Highland Scenic Highway, followed by agriculture land; water and wetland are the least visible land cover types. Th e average viewshed at all 16 points is 97.6 percent forest, which means that forests have the largest infl uence on scenic beauty in the area. Th e negative relationship between scenic beauty and barren Proceedings of the 2009 Northeastern Recreation Research Symposium GTR-NRS-P-66 Figure 5.—Average value of scenic beauty for all 16 points for September and October. 185
land implies that planting vegetation on barren land and reducing clear cutting would help increase the perceived natural beauty of the area. In addition, planting trees in and around the developed areas would both benefi t the local environment and provide a better vista for tourists. Regarding the results from the viewshed analysis and the perception of scenic beauty for each location, sites with diverse land cover types and larger visible areas were generally ranked higher than sites with fewer cover types and smaller viewsheds. According to these criteria, point 9 is the most suitable for sightseeing, followed by points 16 and 11. Points 16 and 11 are existing scenic overlook spots on the HSH. All the points except 4, 5, and 15 have higher values in October than September; points 4, 5, and 15 may experience earlier leaf-off conditions than other points. Although the viewshed tool in GIS and the survey of photographs are promising in explaining the relationships between scenic beauty and the visible land cover for two diff erent months, certain limitations might have infl uenced the fi ndings. Th is study used a land cover and elevation dataset with 30-m resolution. Th is coarse resolution strongly aff ects calculations of the area of the diff erent land cover types in each viewshed. In addition, some of the sites’ viewsheds included land in Virginia, but data for Virginia were not included in the analyses. Future research needs to examine simultaneous observations of topographic variation like shapes and patterns of the features within the viewshed for a given point. Th is type of investigation will test variables that can explain more variation in scenic beauty estimates. In addition, this study examined scenic beauty for two months; a longer period of observations might help expand and refi ne the fi ndings. Finally, high resolution datasets would be extremely benefi cial in future viewshed analyses. 4.0 CONCLUSIONS AND IMPLICATIONS Forest was found to be the major land cover type providing scenic beauty in the HSH. Th ere was a signifi cant relationship between scenic beauty and visible land cover types in October but not September. Th ese results indicate that as the diversity of leaf color increases, the level of perceived scenic beauty also increases. Th e types of land cover visible from the particular locations and the area of each land cover type can be directly calculated using tools in GIS. Th is approach provides a powerful way to model scenic beauty using the diversity and extent of visible land cover types. Additionally, the negative relationship between scenic beauty and nonvegetated land suggests that basic land-use planning focused on increasing vegetated land cover could increase perceived scenic beauty in the area. Th is kind of study could be used to select locations for new overlooks or viewing towers. Th e extent of visible land cover types could be described in interpretive signage to educate visitors about how land cover types are related to scenic aesthetics. Findings from this type of analysis could also be displayed in travel guides and on tourism websites to attract prospective visitors and promote season-based tourism in the area. 5.0 CITATIONS Anderson, L.M.; Schroeder, H.W. 1983. Application of wildland scenic assessment methods to the urban landscape. Landscape Planning. 10: 219-237. Axelsson-Lindgren, C.; Sorte, G. 1987. Public response to diff erences between visually distinguishable forest stands in recreation area. Landscape and Urban Planning. 14: 211-217. Balling, J.D.; Falk, J.H. 1982. Development of visual preference for natural environments. Environment and Behavior. 14(1): 5-28. Brown, T.C.; Daniel, T.C. 1984. Modeling forest scenic beauty: concepts and application to Ponderosa Pine. Res. Pap. RM-256. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 35 p. Buhyoff , G.J.; Leuschner, W.A. 1978. Estimating psychological disutility from damaged forest stands. Forest Science. 24: 424-432. Proceedings of the 2009 Northeastern Recreation Research Symposium GTR-NRS-P-66 186
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United States Department of Agricul
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PROCEEDINGS OF THE 2009 NORTHEASTER
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OUTDOOR RECREATION—TRAILS Experie
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GIS APPLICATIONS AND RECREATION RES
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diff erent points along the spectru
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Proceedings of the 2009 Northeaster
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amount of bare rock. Camel’s Hump
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Larson, M. 2004. A Passive Approach
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THE ROLE OF IMAGINATION IN EXPERIEN
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Table 1.—Special places surveys L
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For some people, being in their spe
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CLIMATE CHANGE AND RESOURCE PLANNIN
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large geographical area (Fig. 1). T
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Divers seen = 4.4 Figure 2.—Norm
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Table 4.—Most-specialized divers
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Sarah Young Human Dimensions of Mar
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3.0 RESULTS AND DISCUSSION Th e FRR
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Table 4.—Divers’ perception of
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YOUTH AND OUTDOOR RECREATION
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A qualitative research design was u
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containers, instead of plastic bags
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WALK, RIDE AND LEARN: STUDENTS’ D
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school. Because of the large sample
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Table 8.—Observation of built str
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“I’M TOO OLD TO GO OUTSIDE!”
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Mean Eco-Affinity Score 5.0 4.5 4.0
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children. In: Klenosky, David B., F
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Cherie LeBlanc Fisher U.S Forest Se
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Table 1.—Comparative statistics f
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did some fi shing himself at each s
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On the other hand, some personal kn
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Illinois Department of Natural Reso
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Consumer behavior researchers have
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50 percent of the households were d
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y respondents indicated that these
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a bivariate approach (instead of th
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HERITAGE TOURISM DEVELOPMENT IN RUR
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Short cruise season. Th e river fre
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Lynda J. Sperazza, Ph.D. Recreation
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2.3 Leisure Participation Values Re
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Dychtwald, K. 1999. Age power: How
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With all research, the research que
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comprehensive. Cross (2005) even ar
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Stillwater, OK: Oklahoma State Univ
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EXPERIENCE USE HISTORY AND ITS RELA
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Table 1.—Comparison of experience
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5.0 DISCUSSION As mentioned previou
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HUMAN DIMENSIONS OF WILDLIFE
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2.0 METHODS Data were collected at
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0 buses 4 buses 8 buses 12 buses Fi
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4.0 DISCUSSION Visitor surveys and
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LEISURE AND HEALTH
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Second, in a world where clean drin
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Table 1.—Importance of different
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Countries also need to be careful a
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Proceedings of the 2009 Northeaster
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Table 1.—Informant profi les Info
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do something and we might think it
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Th ese relational interactions enab
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EXPLORING ELEMENTS THAT INFLUENCE S
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trails, and asking others to stay o
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time/fi nances” in dune protectio
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In summary, this study explored the
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Jinhee Jun Texas A&M University jju
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Table 1.—Sample demographics Indi
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Grayson 1995). Th e fi t of this mo
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Stets, J.E.; Burke, P.J. 2003. A so
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Jiaying Lu Texas A& M University Ji
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Table 1.—Demographic and particip
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Table 3.—Factor analysis of motiv
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Page 145 and 146: Table 1.—Descriptions of key info
Page 147 and 148: sharing the place with others and a
Page 149 and 150: Manzo, L.C. 2005. For better or wor
Page 151 and 152: NORMATIVE TOLERANCES FOR SCUBA DIVE
Page 153 and 154: neutral point. Bubbles at the top o
Page 155 and 156: Table 1.—Snorkeler evaluations of
Page 157 and 158: Davis, D.; Tisdell, C. 1995. Recrea
Page 159 and 160: William Valliere University of Verm
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Page 163 and 164: Manning, R. 2007. Parks and carryin
Page 165 and 166: mission is “...to promote and reg
Page 167 and 168: Figure 1.—Pedestrian walkway LOS
Page 169 and 170: Acceptability 4 3 2 1 0 -1 -2 -3 -4
Page 171 and 172: TOURISM TRENDS AND CHALLENGES
Page 173 and 174: the rankings of 12 attendance motiv
Page 175 and 176: Table 3.—Comparison of socio-econ
Page 177 and 178: Table 6.—Entertainment Seekers’
Page 179 and 180: PLACE MEANINGS
Page 181 and 182: Stedman (1999) posited that sense o
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Page 187 and 188: Ishwar Dhami Division of Forestry a
Page 189 and 190: Figure 2.—Model for the calculati
Page 191: Table 1.—Viewshed results for all
Page 195 and 196: Shafer, E.L.; Hamilton, J.F.; Schmi
Page 197 and 198: a setting’s ability to facilitate
Page 199 and 200: EUH Place Satisfaction Figure 1.—
Page 201 and 202: Table 4.—Regression coefficients
Page 203 and 204: Jőreskog, K.G.; Sőrbom, D. 2004.
Page 205 and 206: OUTDOOR RECREATION II
Page 207 and 208: Th e primary objective of the propo
Page 209 and 210: Question 10. Do you believe that SU
Page 211 and 212: Rodney B. Warnick Tom Stevens Unive
Page 213 and 214: Table 1.—Estimated visitation to
Page 215 and 216: Table 2.—Estimated national park
Page 217 and 218: For this section of the trend analy
Page 219 and 220: 5.0 DISCUSSION, CONCLUSIONS, AND IM
Page 221 and 222: RECREATION TRAILS IN MAINE AND NEW
Page 223 and 224: Table 2.—Average cross-sectional
Page 225 and 226: Table 5.—The percentage of stream
Page 227 and 228: Demrow, C.C. 2002. Maine trails man
Page 229 and 230: Turner, J.M. 2002. From woodcraft t
Page 231 and 232: Chad P. Dawson, Professor State Uni
Page 233 and 234: 80 70 60 50 40 30 20 10 0 Very diss
Page 235 and 236: Table 4.—The number of campers in
Page 237 and 238: Fuller, C.; Dawson, C. 1999. A comp
Page 239 and 240: Table 1.—Modal response rates Ini
Page 241 and 242: Smee, A.; Brennan, M. 2000. Electro
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Table 1.—Characteristics of parti
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Table 3.—Results of discriminant
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5.0 CITATIONS Babbie, E. 2001. Th e
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Bernardita Silva Graduate Assistant
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Table 1.—Expenditures by 2008 Ame
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6.0 CITATIONS Della Bitta, A.; Lond
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the economic impact of travel, and
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the air show. Th e second most popu
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not displace other local spending.
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6.0 CITATIONS Adams, P.D.; Parmente
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MANAGEMENT AND USE OF PARK, RECREAT
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which suggests that diff erent type
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Table 2.—Between-groups ANOVA to
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forest settings (means = 4.4 and 4.
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LEISURE RESEARCH
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communities together, and a decline
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Table 2.—Life satisfaction scale
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onds within a social group is a mec
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“I JUST WANTED TO GET AWAY”: AN
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Th e importance of “getting away
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Gerlach, J. 1989. Spring Break at P
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www.nrs.fs.fed.us The U.S. Departme
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