Scenic Beauty of Forest Landscapes

Transcription

1 Forest Sci., Vol. 27, No. 1, 1981, pp Copyright 1981, by the Society of American Foresters Progress in Predicting the Perceived Scenic Beauty of Forest Landscapes HERBERT SCHROEDER TERRY C. DANIEL ABSTRACT. Statistical models were developed for predicting the perceived scenic beauty of ponderosa pine forest landscapes using forest inventory data collected in the field. Regression equations were derived for combining measures of site characteristics, including numbers of trees of different species and sizes, amount of downed wood, and amount of vegetative ground cover, into estimates of the perceived scenic quality of the sites. The models successfully predicted esthetic preferences for forest landscapes with a variety of different physical characteristics. These models were then applied to ponderosa pine landscapes in a region different from the original site samples. With minor adjustments, the models performed as well for the new landscape samples as for the original landscapes. The models are consistent with past research and with intuitive expectations about the scenic effects of various forest features. FOREST SCI. 27: ADDITIONAL KEY WORDS. Pinus ponderosa, ponderosa pine, esthetic resource measurement. IN ORDER TO EVALUATE alternative forest management options, the expected effects of each option on many resources must be determined, and trade-offs among the various gains and losses associated with each alternative must be made explicit. Effects on wood, water, forage, and other physical and biological factors can be projected with reasonable precision and accuracy. While some progress has been made in the assessment of esthetic resources (Daniel and others 1979) it has not usually been possible to predict esthetic effects of management actions with the precision or reliability of physical/biological effects. Often esthetic effects are represented by artistic renditions of expected outcomes, or by general categorical statements based upon expert judgment. Such graphic or categorical projections are not well suited for multiple-objective trade-off analyses involving quantitative projections for other important factors. Evaluations of alternative plans would be much more effective if estheti consequences could be predicted with levels of precision and confidence comparable to the projections for physical and biological effects. The Scenic Beauty Estimation method (Daniel and Boster 1976) represents one effort to better integrate esthetic concerns with other objectives of forest management. This method provides reliable and valid indices of scenic beauty, one of the forest's most important esthetic resources. Interval scale measures of perceived scenic beauty (Scenic Beauty Estimates or SBEs) are derived from esthetic The authors are with the Department of Psychology and School of Renewable Resources, University of Arizona, Tucson, AZ Dr. Schroeder is now Research Social Scientist for the North Central Forest Experiment Station, USDA Forest Service. The research reported here was supported by the Rocky Mountain Forest and Range Experiment Station, USDA Forest Service, under cooperative agreements CA and CA. The cooperation and assistance of the Coconino National Forest, Flagstaff, Arizona, and the Arapahoe/Roosevelt National Forest, Boulder, Colorado, is gratefully acknowledged. Manuscript received 20 February VOLUME 27, NUMBER 1, 1981 / 71

2 judgments of public observer panels. Successful applications in a number of contexts have shown the SBE to be an effective and useful gauge of the scenic effects of forest management actions (e.g., Benson and Ullrich 1981, Daniel and others 1977, Schroeder and Daniel 1980). To be helpful in the evaluation of alternative management proposals, however, scenic effects must be predicted in advance of a plan's implementation. That is, proposed changes in the features of the forest must be translated into expected scenic beauty values. One way to anticipate scenic impacts of specific management actions is to represent the relationship between physical site characteristics and perceived scenic beauty in the form of a statistical model. Given the physical characteristics of a landscape, the model produces an estimate of the scenic value of the landscape. Each alternative management plan will produce changes in the physical characteristics of the landscape, and the scenic beauty model translates these physical changes into statistical predictions of the scenic beauty of the landscape after the physical changes have taken place. Thus a manager can use the scenic beauty model to anticipate scenic impacts of management actions before the actions are actually carried out, provided that the effects of the management actions on the relevant physical characteristics of the landscape can be specified. The scenic effects of alternative plans can then be explicitly compared to effects on other environmental objectives and to economic costs and benefits. Daniel and Boster (1976) reported that subjective estimates of such features as the density of trees, the amount of downed wood, and the lushness of ground cover all showed systematic relationships to the SBE measure. In a subsequent study, Arthur (1977) had professional foresters estimate forest characteristics by inspecting color slide representations of forest areas. Foresters' estimates of the number of trees per acre in several size classes, the amount of downed wood, and several other features were combined in a multiple regression equation that accurately predicted public observers' judgments (SBEs) of the scenic beauty of the forest scenes represented by these slides. These studies, along with a number of others (e.g., Buhyoff and Wellman 1980, Schomaker 1979, Shafer and others 1969), indicate that scenic judgments are consistently related to identifiable features of the forest landscape. To date, however, these relationships have been based entirely upon measurements made from photographs--the same photographs that were judged by observer panels. In actual management or planning situations, however, forest features are represented by estimates based upon inventories and sample measurements made in the field. The current study extends previous efforts (e.g., Arthur 1977) to develop a valid and useful model for predicting perceived scenic beauty of forest landscapes. In this study, standard forest mensuration procedures were used to inventory overstory, understory, ground cover, and downed wood characteristics of forest sites. Color slides, coordinated with these field inventories, were shown to panels of observers to obtain SBE values of each site. The relationship between the physical forest features measured in the field and the SBE values served as the basis for scenic beauty prediction models. These models are suitable for predicting changes in scenic beauty resulting from management actions which change the vegetation characteristics of the landscape. METHOD Photographic and physical data were collected on square plots approximately 1 acre in area (212 feet on a side). The locations of the plots were selected with the objective of representing a wide range of physical conditions and landscape types in the ponderosa pine zone of the Coconino National Forest in northern Arizona. Sites having very prominent or unique geographic features or exhibiting buildings, 72 / FOREST SCIENCE

3 roads, or other manmade elements were excluded from this study. Through discussions with Forest Service personnel familiar with the forest, several general geographic regions were delineated. These included some areas bordering on mixed conifer and pinyon-juniper forest types. The exact locations of individual plots within the general areas were selected in the field. Sample plots ranged from mature ponderosa pine stands with 55 trees per acre greater than 20 inches in dbh and several trees greater than 28 inches in dbh, to mixed stands with ponderosa pine, fir, aspen, oak, and limber pine, each represented by size classes from seedlings to mature trees. Ground cover conditions varied from virtually bare soil (0 lbs/acre grasses, forbs, and shrubs) to rather lush conditions (1,232 lbs/ acre). Downed wood also varied widely, ranging from 2.4 cubic feet/acre to 2,670 cubic feet/acre, with many size and distribution conditions represented. At each location selected, a plot center was established. From that center a random compass heading was used to determine the orientation of the diagonals of a square sample plot. Four photographs were taken from the center of the plot, each oriented along one of the plot diagonals. Four additional photographs were taken, one from each of the plot comers oriented toward the center point. All photos depicted "in the forest" scenes, i.e., they were not distant "vista" type photographs. After the photographs were taken, timber, downed wood, and vegetative ground cover were inventorled. Eight measurements of each physical variable were made on each plot, with each measurement corresponding to one of the eight photographs. The photo sampling scheme is illustrated in Figure 1. Forest Inventory.--Trees greater than 5 inches in diameter were inventorled in 4-inch size classes using a 10-factor prism. A complete 360 ø sample was taken around the center point. This sample was subdivided into four 90 ø quadrants corresponding to the directions in which the photos were taken. At each comer point another sample was taken covering the 90 ø quadrant directed toward the interior of the plot. Seedlings and saplings were counted in 150-foot-long strips lying along the diagonals of the plot. The strips were 5 feet wide for seedlings, and 10 feet wide for saplings. Downed wood was inventoried using the method of Brown (1974). Two 50-foot sample transects were placed on each diagonal joining the center to a corner of the plot. The numbers of pieces of downed wood intersecting the line were tallied according to size. The species composition of the downed wood and a judgment about whether it was predominately natural or man-caused (slash) were also recorded. Vegetative ground cover was inventoried by placing two small circular plots (9.6 ft 2 area) close to each diagonal of the sample plot. The wet weight in grams of each species occurring within the circular plot was estimated. From the eight range plots one was randomly selected and all of the plants in that plot were clipped. These were later dried and weighed. The actual weight thus obtained was used to calculate a correction factor for converting field estimates into dry weights. Scenic Beauty Judgments.--Ninety forest plots were inventorled between June 7 and July 15, The eight color slides from each plot were shown to groups of introductory psychology students, who rated their scenic beauty on a ten-point scale. Past research (Daniel and Boster 1976, Arthur 1977) has failed to detect any reliable differences between scenic judgments of ponderosa pine landscapes made by students and judgments made by members of the general public. Therefore, it is reasonable to conclude that models derived using student judgments will adequately represent public scenic preferences. In any event, students provide a convenient and appropriate sample for the present stage of model development. Each slide was independently judged by approximately 30 observers, with a VOLUME 27, NUMBER 1, 1981 / 73

4 LEGEND Photograph -'"'; Timber sample(basal area factor--io) 50 ft dawned wood transect O Range plot FIGURE 1. Schematic of sampling procedure. Each plot was one acre in size, and plot orientation was randomly determined for each plot. total of 230 observers participating in the study. The ratings were analyzed by the method of Daniel and Boster (1976) to derive independent Scenic Beauty Estimates (SBEs) for each scene. Scenic values varied from a low of -44 to a high of 123. The SBE scale represents relative differences between landscapes, that is, the location of the zero-point is arbitrary. To help forest managers interpret scenic beauty scale values, a set of color prints has been selected to illustrate varying levels of scenic beauty. For example, a view of a dense pole stand with no vegetative ground cover was rated close to the negative extreme of the scale. A mature mixed conifer and aspen stand with lush ground cover scored close to the top of the scenic beauty scale. 74 / FOIST SCIENCE

5 RESULTS AND DISCUSSION Estimates of the physical and esthetic characteristics of each of the ninety plots were made by averaging the eight observations obtained on each plot. Thus 90 cases were available for regression analysis. Plots were classified according to the estimated percent of the trees on the plot which were ponderosa pine. Models were then developed for different types of forests, ranging from predominately ponderosa pine to more diverse mixtures of species. Linear regression analysis was performed separately on plots with 90 percent or more ponderosa pine (36 cases), and on all 90 plots. Scenic beauty was the dependent variable in both analyses, and estimates of physical site characteristics based on the field inventories were used as predictors. Because of the relatively small number of cases and the large number of variables representing physical characteristics of the plots, it was not feasible to examine interaction terms. Arthur (1977) obtained excellent results using only linear terms; her model using timber cruise variables had an R 2 of Thus, although nonlinear effects are of theoretical interest, models based on linear terms seem to be adequate for practical purposes. Investigation of interaction terms and of various transformations of inventory variables awaits the availability of a larger sample of cases. An R 2 of 0.78 was obtained for the model limited to plots with 90 percent or more ponderosa pine, and the R for the model based on all plots was Overall, the best predictors seemed to be those relating to vegetative ground cover (grass and forbs) and the character of downed wood (natural or slash). The actual volume of downed wood appeared to be a less powerful predictor, but in fact, areas in which slash predominated over natural downed wood also tended to have large total volumes of downed wood. The fact that the character of the downed wood entered the model more strongly than the downed wood volume variable may indicate that there are negative esthetic effects of man-caused slash which are relatively independent of the volume of material (for example, piles or windrows, stumps, and other visible evidence of cutting). These models were very successful from a statistical point of view, but they present several problems for actual application. A large number of different predictors was used, each requiring rather detailed data. This resulted in complex and somewhat unwieldy models. Also, multicollinearity was present between some of the predictor variables, which may bias the estimates of the regression coefficients and make them difficult to interpret. A further problem is that with the large number of predictors and the relatively small number of cases, the R 's for these detailed models may be somewhat inflated, and the models may not give an accurate indication of the extent to which scenic values can be predicted in actual practice. To obtain models more suited for practical applications, two steps were taken. First, variables which did not appear to contribute to the prediction of scenic beauty, or which would not commonly be available in forest planning applications, were deleted from the model. Second, some of the variables were combined to form a less detailed but more concise set of predictors. For example, the number of seedlings per acre and the number of saplings per acre were combined to form a single variable (small trees). In this way a more manageable model can be derived, and the problem of multicollinearity can be reduced. A model for application to predominately ponderosa pine forests was developed using stepwise regression with the predictors shown in Table 1. When applied to plots with at least 90 percent ponderosa pine these variables accounted for 60 percent of the variance in SBEs. The coefficients of all predictors were in accordance with reasonable intuitive expectations of the esthetic effects of these variables. Ground cover and large trees were attractive and slash had a negative VOLUME 27, NUMBER 1, 1981 / 75

6 TABLE 1. SBE model: more than 90 percent ponderosa pine. Sensi- Step Variable B Beta R R 2 tivity I Grass and forbs (lbs/acre) Slash (cu ft/acre) Ponderosa pine 5-16 inches dbh (number/acre) Shrubs (lbs/acre) Ponderosa pine >16 inches dbh (number/acre) (Constant) 3.43 N = 36, F (5, 30) = 9.00, P < influence. Examination of the correlation matrix showed that the highest intercorrelation between predictors was 0.44, so that multicollinearity was not a serious problem. Although this model is statistically less powerful than the more detailed model, it appears to be a more accurate representation of the causal factors underlying scenic judgments. The scenic beauty prediction model can be written out as an equation: SBE = GF PPL SL SH PPM where SBE = Scenic Beauty Estimate GF = Grass and forbs (lbs/acre) PPL = Ponderosa pine > 16 inches dbh (number/acre) SL = Slash (cu ft/acre) SH -- Shrubs (lbs/acre) PPM = Ponderosa pine 5-16 inches dbh (number/acre). The coefficients of the regression equation (B's) depend on the units of measurement of the predictors and therefore do not directly reflect the importance of individual predictors in the equation. The Beta coefficients (standardized regression coefficients) in Table 1 are based upon the standard deviations of the predictors' distributions, and therefore are a more pure measure of the importance of each variable for predicting scenic beauty. A high beta for a variable indicates that the prediction equation is relatively sensitive to changes in that variable. The table also presents an alternative measure of the sensitivity of the equation to changes in the predictors. The numbers in the column labeled "sensitivity" tell how much the predicted SBE will change as a result of a 10 percent increase in the predictor, evaluated at the mean value of the predictor. For example, if slash changes from 490 to 539 cu ft/acre, all other things staying the same, SBE will decrease by units. A similar model for application to landscapes with a more diverse mixture of tree species is shown in Table 2. This model is based on all 90 inventory plots, and includes information about species other than ponderosa pine. This model also accounts for 60 percent of the variance in SBEs. Multicollinearity is not a problem in this model as intercorrelations between predictors are all less than Predictions of scenic beauty from this model are made with the equation: SBE = F A PPL G SL q FI q J OK PPM q / FOREST SCIENCE

7 TABLE 2. Mixed species SBE model: all cases. Sensi- Step Variable B Beta R R e tivity 1 Forbs (lbs/acre) Aspen (number/acre) Ponderosa pine 16 inches dbh (number/acre) Grass (lbs/acre) Juniper (number/acre) Fir (number/acre) Slash (cu ft/acre) Oak (number/acre) Ponderosa pine 5-16 inches dbh (number/acre) (Constant).1.33 N = 90, F (9, 80) = 14.04, P < where A = Aspen (number/acre) FI = Fir (number/acre) J = Juniper (number/acre) OK = Gambel oak (number/acre) F = Forbs (lbs/acre) G = Grass (lbs/acre) (Other variables as defined in previous equation.) As can be seen, the coefficients of this model are consistent with those of the first model. Large ponderosa pine and ground cover have positive scenic effects. Smaller ponderosa pine and slash have negative effects. In addition, tree species other than ponderosa pine are generally positive. The predictive validity of the ponderosa pine and the mixed species models was tested by withholding a random selection of plots from the data set and deriving models from the remaining plots. These models were then used to predict scenic beauty values for the withheld plots. The correlation between predicted and actual SBEs is an index of the ability of the regression equation to predict scenic beauty. For the ponderosa pine model, 10 of the 36 plots with 90 percent or more ponderosa pine were withheld. The correlation between actual SBEs of these 10 plots and the SBEs predicted for them by the equation derived from the remaining cases was (r" = 0.65). The same procedure was followed for the mixed species model given in Table 2. A sample of 23 plots was withheld and an equation based on the remaining 67 plots was used to predict SBEs for those cases. The correlation of predicted to actual SBEs was (r" ). Both models therefore have the ability to predict the scenic beauty of the withheld plots, but the predictions are better when the analysis is restricted to plots having 90 percent or more ponderosa pine. The models reported so far were developed from data collected in a rather limited region in northern Arizona. To test the generality of the models, data was collected on forty plots in the Colorado Front Range near Boulder, Colorado. These plots were predominately ponderosa pine, with some other species in small amounts. Some features, such as slope and topography and the condition of tree stands, differed considerably from the Arizona samples. Also, several of these new plots had larger numbers of trees which had been damaged or killed by VOLUME 27, NUMBER 1, 1981 / 77

8 TABLE 3. Combined SBE model for Arizona and Colorado plots. Sensi- Step Variable B Beta R R e tivity 1 Forbs (lbs/acre) Slash (cu ft/acre) Live ponderosa pine > 16 inches dbh (number/acre) Aspen (number/acre) Grass (lbs/acre) Fir (number/acre) Juniper (number/acre) Shrubs (lbs/acre) Live ponderosa pine <5 inches dbh (number/acre) Dead ponderosa pine 5-16 inches dbh (number/acre) Live ponderosa pine 5-16 inches dbh (number/acre) Oak (number/acre) (Constant) N = 130, F (12, 117) = 9.75, P < mountain pine beetles. Four photographs and four measurements of each physical variable were made on each of the Colorado plots. The sampling scheme at each plot was essentially as shown for the plot center in Figure 1. The four values for each variable were averaged to obtain a single estimate for each variable for each plot. A model was derived for these forty plots, using the same variables as the earlier models. The model accounted for 55 percent of the variance in SBEs, and was highly significant (P < 0.01). The equation for the model is SBE = SL PPM F PPS PPL L G SH A FI J where PPS = Ponderosa pine < 5 inches dbh (number/acre) L = Limber l ine (number/acre) (Other variables defined as in previous equations.) The coefficients in this equation are similar to those in the mixed species model developed for the Coconino National Forest. It appears that the scenic beauty prediction model requires only minor adjustments for application to the new plots in Colorado. To assess the visual impacts of beetle-killed trees, a new model was developed which distinguished between live and dead ponderosa pine on the forty Colorado plots. The equation for this model is SBE = SL F DPM PPS PPL PPM DPS L SH G DPL FI A J where DPS = Dead ponderosa pine < 5 inches dbh (number/acre) DPM = Dead ponderosa pine 5-16 inches dbh (number/acre) DPL = Dead ponderosa pine > 16 inches dbh (number/acre) (Other variables defined as before.) 78 / FOREST SCIENCE

9 This model accounted for 60 percent of the variance in SBEs (P < 0.05). Distinguishing between live and dead ponderosa pine, therefore, improved the power of the model. As one would expect, standing dead trees had generally negative scenic effects. Finally, a general model for application to forests in both Arizona and the Colorado Front Range was developed (Table 3) by combining the forty Colorado plots with the ninety plots from the Coconino National Forest. The regression model for all these cases accounted for 49 percent of the variance in SBEs (P < 0.001). The equation is SBE = F SL PPM FI G DPM J PPL A OK PPS SH (All variables defined as before.) This model does not perform quite as well as the other models reported. This could be due to differences in terrain, vegetation, and other characteristics in the two regions. For example a number of shrubs that were common in the Colorado plots did not occur in the Coconino plots. The ground cover variables may have had somewhat different visual effects in the two regions, so that a single model could not perform as successfully as the separate models. Still, the great similarity in the separate models suggests that the basic forest features affecting scenic beauty judgments are the same for both areas. CONCLUSIONS The scenic beauty prediction models reported above are, of course, subject to a number of limitations. First, they are intended to apply to forest conditions typical of the western ponderosa pine zone. Special or unusual features, such as waterfalls, lakes, or dramatic geological formations, have not been considered in the models presented here. Roads, buildings, and other human developments were also excluded from the samples and the models at this stage of development. Scenic beauty estimates apply to views from within the forest, rather than to sweeping vistas or views from outside and above the site. This "on-the-ground" perspective is typical of the way hikers, campers, and automobile touring visitors experience much of the ponderosa pine region. As with any mathematical model, application to a particular setting requires that certain qualifications be met. If prediction equations are applied to areas which are very different from the ones used to develop the models, inaccurate predictions may result. For example, the simple ponderosa pine model (Table 1) would not work well if applied to areas with substantial numbers of other tree species. It is encouraging, however, that the basic models reported in this paper worked well for forest landscapes from such distinct areas as northern Arizona and the Colorado Front Range. In any event, the predictions arising from these equations are statistical estimates, and deviations from predicted values are to be expected. A scenic beauty prediction should be interpreted as indicating a range of likely outcomes, rather than as a single, precise prediction. Most of the restrictions described above can be overcome by further model development efforts. Efforts are already underway to develop comparable models for vista-type views. Special feature and human development effects could be incorporated into future models, or special models may have to be developed to deal with these factors. In spite of the current limitations, however, the scenic beauty prediction models are appropriate for predicting the scenic consequences VOLUME 27, NUMBER 1, 1981 / 79

10 of a great many harvest, silvicultural, and other management actions in ponderosa pine forest types. The results of the studies reported here confirm that models such as those developed by Arthur (1977) perform well when physical site characteristics are obtained from field inventories of forest features. The specific models presented exhibit considerable reliability and predictive validity. Comparisons among these models and the models presented by Arthur (1977) reveal a high degree of consistency in the basic form and content of predictive relationships. Timber, downed wood, and ground cover characteristics had very similar effects on scenic beauty judgments in each case. Further refinement and testing of the models is being carried out. Additional data now being collected will furnish enough cases for development of nonlinear models. While linear models have performed quite well, preliminary experiments suggesthat second-degree terms (squares and products) in a polynomial regression may somewhat improve the precision of the models. Nonlinear transformations of the predictors may also be appropriate (Buhyoff and Wellman 1980). A timber harvest in progress on the Coconino National Forest will provide an opportunity to test the utility of the models for predicting scenic consequences of timber management plans. Models will be used to predict the scenic impacts of harvest prescriptions for specific stands. Following the harvest, actual scenic impacts will be assessed from judgments of photographs of the harvested stands, and comparisons of predictions to actual scenic impacts will be made. The availability of tested models for predicting harvest impacts on scenic beauty should greatly facilitate the evaluation of trade-offs between scenic resources and other environmental and economic objectives. LITERATURE CITED ARTHUR, L. M Predicting scenic beauty of forest environments: some empirical tests. Forest Sci 23: BœNSON, R. E., and J. R. ULI mch Visual impacts of forest management activities: interim findings on public preferences. USDA Forest Serv Res Pap INT-262:(in press). BROWN, J. K Handbook for inventorying downed woody material. USDA Forest Serv Gen Tech Rep INT-16, 24 p. Intermt Forest and Range Exp Stn, Ogden, Utah. BVH¾OVV, G. J., and J. D. WœLI a^n The specification of a non-linear psychophysical function for visual landscape dimensions. J Leisure Res 12(3): D^Nmi, T. C., and R. S. BOSTER Measuring landscape esthetics: the scenic beauty estimation method. USDA Forest Serv Res Pap RM-167, 66 p. Rocky Mt Forest and Range Exp Stn, Fort Collins, Colo. D^NmI, T. C., L. M. AN )œason, H. W. ScHaoœ )œa, and L. WHœœ œa III Mapping the scenic beauty of forest landscapes. Leisure $ci 1(1): D^NmL, T. C., E. H. ZvRœ, and B. L. Dmvœa, Technical Coordinators Assessing amenity resource values. USDA Forest Serv Gen Tech Rep RM-68, 70 p. Rocky Mt Forest and Range Exp $tn, Fort Collins, Colo. ScHo a^i œa, J. H Measurement of preferences for proposed landscape modifications. In Assessing amenity resource values (T. C. Daniel, E. H. Zube, and B. L. Driver, Tech Coordinators), p USDA Forest Serv Gen Tech Rep RM-68, 70 p. Rocky Mt Forest and Range Exp $tn, Fort Collins, Colo. ScHaoœ )œa, H. W., and T. C. D^NmI Predicting the scenic quality of forest road corridors. Environ and Behav 12: $HAFER, E. L., J. F. HAMILTON, and E. A. SCHMIDT Natural landscape preferences--a predictive model. J Leisure Res 1(1): / FOREST SCIENCE