Since emerging from the work of Berlyne (1963), Vitz (1966), and Day (1967), complexity has been repeatedly examined as a possible predictor of landscape preference. Kaplan and Kaplan (1989) included complexity in the well-known, two-by-two “preference matrix,” along with coherence, mystery, and legibility. Complexity is immediately inferred and encourages exploration of the two-dimensional picture plane of a scene through the presence of an abundant, diverse collection of visual characteristics, regardless of arrangement. Numerous, distinct colors, textures, shapes, and physical dimensions of foliage, flowers, path materials, topography, and structures act as identifying attributes of complexity.
Stamps (2004) indicated that “some sort of relation between complexity and preference exist,” but the magnitude and direction of correlations between preference and complexity estimations varied widely and were not replicable. He suggested that future research should relate preference ratings to the “obvious mathematical expression” of complexity, Shannon’s information entropy (Shannon and Weaver, 1949; Stamps, 2002, 2003), and has computed entropy values using attributes of complexity, like color. Thus, the first purpose of this study is to objectively compute color information entropy values and investigate potential relationships with complexity estimations and preference ratings.
The second purpose of this study is to examine whether color information entropy values, preference ratings, and subjective estimations of complexity are affected by visual changes to plant and vegetative characteristics during growth. Some plant and vegetative colors in temperate climates, like the northeastern United States, evidently change throughout the year. Seasonal and diurnal meteorological changes affect the appearance of many plant flowers, foliage, seeds, and stems. Likewise, humans may alter the visual appearance of plants and vegetation at different times through various means of maintenance like mowing, controlled burns, and chemical applications ranging from fertilizers to herbicides. Therefore, plant and vegetative color changes may influence human perception and affect preference ratings and complexity estimations. In addition, changes in plant and vegetative color may affect computations of color information entropy values that are derived from a scene.
Shannon and Weaver (1949) redefined and distinguished information entropy from physical or thermodynamic entropy to measure disorder in information. Entropy values are presented in bits. Generally, as the number of bits increase, the number of alternatives, or levels of a factor in information, double. Computing entropy requires parsing information into factors, levels, and units. Following is the equation for calculating entropy:
Employing color as a factor in information entropy computations requires acknowledging that color may be both perceived and categorized. Perceptual colors are often classified by hue, chroma, and value and examined with the use of the Munsell color system (Landa and Fairchild, 2005). In contrast, the eight chromatic categorical colors—red, orange, yellow, green, blue, purple, brown, and pink—and three achromatic colors—white, black, and gray—are not quantitatively distinguishable for standardization (Berlin and Kay, 1969). To differentiate perceptual and categorical colors simply, consider that myriad perceptual colors possessing a red hue can be created by varying chroma and value, but each could also be classified as one categorical color—red.
Identifying objective measures of perceptual and categorical colors and determining whether either affects landscape preference carries practical implications. Plant growers, scientists, designers, and installers can benefit from understanding and predicting whether people prefer landscapes containing plants and vegetation that are red, green, blue, etc., regardless of chroma or value variations (categorical colors), landscapes containing myriad variations of reds, greens, blues, etc. (perceptual colors), or neither.
Estimations of diversity—a synonym of complexity and prevalent criterion in architectural aesthetic regulations—and information entropy values have directly correlated to the (categorical) color (r = 0.97) of houses in designed stimuli (Stamps, 2002). Elsewhere, three studies contributed to a direct correlation between rated diversity and entropy of r = 0.87 (Stamps, 2003). Accordingly, the following is our first hypothesis:
- H1. Complexity estimations and color information entropy values computed from depictions of scenes will significantly and directly correlate.
Two studies suggest a positive relationship between complexity, landscape color information entropy, and preference. Respondents in one study have been positively affected by the amount and diversity of color contained within landscape simulations at establishment and maturity (Hands and Brown, 2002). In another, Jorgensen et al. (2002) surmised that different colors and combinations of flowering plants may positively affect preferences for summer depictions of an urban park having no understory, regardless of the degree of enclosure. Therefore, we present our second hypothesis:
- H2. Preference ratings, complexity estimations, and color information entropy values will correlate directly and significantly.
Kuper (2013) found that experts’ estimations of complexity were unaffected by the visual changes to plants and vegetation depicted in color photographs of landscape scenes categorized as early summer, late summer, and fall. Landscape architects’ and other environmental professionals’ preference ratings and management goals have been shown to differ or conflict with laypersons (Buhyoff et al., 1978; Daniel and Boster, 1976; Kaplan and Kaplan, 1989; Vining and Ebreo, 1991). Consequently, the effect of plant and vegetative visual changes on laypersons’ complexity estimations should be examined, and estimations for broader and distinct depictions of visual changes during plant growth are needed. Kuper (2013) did not solicit estimations for dormant scenes, and the seasonal or calendrical categorization of scene depictions may have inherently included flowering plants, for instance, in two or more seasonal depictions, which could have affected responses and confounded the nonsignificant effect. Accordingly, we solicited laypersons’ estimations of complexity for depictions of scenes that include winter dormant, foliated, flowering and senescent plants and vegetation and further hypothesize that:
- H3. Complexity estimations for foliated scenes will not differ from those of other scenes.
The results of five studies suggest that visual changes to plant and vegetative characteristics affect preference. First, generally higher scenic beauty estimations for six areas in a national forest collected after the rainy season, when compared with before (Daniel and Boster, 1976), imply that foliated, green scenes with or without colorful flowers may receive higher preference ratings than dormant or senescent scenes. Second, Kaufman and Lohr (2004) found that computer-generated generic, mature green and red trees received positive responses from participants, “purple” and “bluish purple” trees received neutral and negative responses, and “brown” and “orange-brown” trees were disliked. Third, students in Newark, DE, rated winter as colder, more dangerous, and less exciting than spring and summer, which were both rated hotter, more exciting, conducive to play, and safer than winter (Sonnenfeld, 1969). These findings suggest that scenes depicting foliated and flowering landscapes may be perceived as positive, warmer, safer, more exciting, and more conducive to play, and therefore, receive higher preference ratings than dormant or senescent scenes. Fourth, Palmer (1990) found that scenic quality ratings gathered during the seasons depicted in photographs of hardwood forest sites in the northeastern United States resulted in nonsignificant differences between full-leaf summer and fall color scene ratings; both were significantly higher than ratings for leafless spring scenes. He suggests that the “lush and intense color” of full-leaf summer and fall color scenes make them “desirable,” whereas the leafless spring condition is “colorless” and “least desirable.” Responses elicited for the same scenes in mid-March resulted in a nonsignificant difference between fall color and snow-covered winter scenes. Summer scene ratings were significantly higher than fall and winter; leafless spring scene ratings were significantly lower than summer, fall, and winter. Finally, summer depictions of forest settings containing herbaceous growth and the absence of timber harvest residue were rated as more highly preferred than were the same settings as depicted in the winter (Benson and Ullrich, 1981). Thus, the presence of foliage in foliated and flowering scenes may contribute to comparatively higher preference ratings. From these findings we devised the following hypothesis for examination:
- H4. Preference ratings for foliated scenes will be significantly higher than those for dormant or senescent scenes.
The results of one study imply that color information entropy values derived from depictions of landscapes may be affected by visual changes to plant and vegetative characteristics between flowering or foliation and senescence. Hendley and Hecht (1949) made “visual comparisons between natural objects” and Munsell color paper samples and found wider color ranges of objects in autumn, when compared with those in summer. Thus, we hypothesized the following:
- H5. Perceptual information entropy values for foliated scenes will be significantly lower than those of senescent scenes.
Two studies suggest that flowering scenes are highly preferred. Respondents have shown a preference for flowering herbs and grass along an English roadside (Akbar et al., 2003) and flowers in street plantings (Todorova et al., 2004). The following final hypothesis addresses these findings:
- H6. Preference ratings for foliated scenes will be significantly lower than flowering scenes.
Akbar, K.F., Hale, W.H.G. & Headley, A.D. 2003 Assessment of scenic beauty of the roadside vegetation in northern England Landsc. Urban Plan. 63 139 144
Benson, R.E. & Ullrich, J.R. 1981 Visual impacts of forest management activities: Findings on public preferences. U.S. Dept. Agr., Intermountain Forest Range Expt. Sta., Res. Paper INT-262
Berlin, B. & Kay, P. 1969 Basic color terms. Univ. California Press, Berkeley, CA
Berlyne, D.E. 1963 Complexity and incongruity variables as determinants of exploratory choice and evaluative ratings Can. J. Psychol. 17 274 290
Beute, F. & de Kort, Y.A.W. 2013 Let the sun shine: Measuring explicit and implicit preference for environments differing in naturalness, weather type, and brightness J. Environ. Psychol. 36 162 178
Brown, T.C. & Daniel, T.C. 1984 Modeling forest scenic beauty. Concepts and application to ponderosa pine. U.S. Dept. Agr., Rocky Mountain Forest Range Expt. Sta., U.S. Forest Res. Paper RM-256
Buhyoff, G.J., Wellman, J.D., Harvey, H. & Fraser, R.A. 1978 Landscape architects’ interpretations of people’s landscape preferences J. Environ. Mgt. 6 255 262
Cohen, J. 1988 Statistical power analysis for the behavioral sciences. Erlbaum, Hillsdale, NJ
Daniel, T.C. & Boster, R.S. 1976 Measuring landscape esthetics: The scenic beauty estimation method. U.S. Dept. Agr., Rocky Mountain Forest Range Expt. Sta., U.S. Forest Res. Paper RM-167
Day, H. 1967 Evaluation of subjective complexity, pleasingness, and interestingness for a series of random polygons varying in complexity Percept. Psychophys. 2 281 286
Hall, F.C. 2001 Ground-based photographic monitoring. U.S. Dept. Agr., Pacific Northwest Res. Sta., U.S. Forest Res. Gen. Tech. Rpt. PNW-GTR-503
Hendley, C.D. & Hecht, S. 1949 The colors of natural objects and terrains, and their relation to visual color deficiency J. Opt. Soc. Amer. 39 870 873
Herzog, T.R. & Kropscott, L.S. 2004 Legibility, mystery, and visual access as predictors of preference and perceived danger in forest settings without pathways Environ. Behav. 36 659 677
Jorgensen, A., Hitchmough, J. & Calvert, T. 2002 Woodland spaces and edges: Their impact on perception of safety and preference Landsc. Urban Plan. 60 135 150
Kaplan, R. 1977 Preference and everyday nature: Method and application, p. 235–250. In: D. Stokols (ed.). Perspectives on environment and behavior: Theory, research and applications. Plenum, New York, NY
Kaplan, R. 1985a The analysis of perception via preference: A strategy for studying how the environment is experienced Landscape Planning 12 161 176
Kaplan, R. & Kaplan, S. 1989 The experience of nature. Cambridge Univ. Press, Cambridge, UK
Kuper, R. 2013 Here and gone: The visual effects of seasonal changes in plant and vegetative characteristics on landscape preference criteria Landscape J. 32 65 78
Palmer, J.F. 1990 Aesthetics of the northeastern forest: The influence of season and time since harvest, p. 185–190. In: T. More, M.P. Donnelly, D.A. Graefe, and J.J. Vaske (eds.). Proc. 1990 Northeastern Recreation Researchers Symp. U.S. Dept. Agr., Northeastern Forest Expt. Sta., Gen. Tech. Rpt. NE-145
Shannon, C.E. & Weaver, W. 1949 The mathematical theory of communication. Univ. Illinois Press, Urbana, IL
Todorova, A., Asakawa, S. & Aikoh, T. 2004 Preference for attitudes towards street flowers and trees in Sapporo, Japan Landsc. Urban Plan. 69 403 416
Vining, J. & Ebreo, A. 1991 Are you thinking what I think you are? A study of actual and estimated goal priorities and decision preferences of managers, environmentalists, and the public Soc. Nat. Resources 4 177 196
Westland, S., Ripamonti, C. & Cheung, V. 2012 Computational color science using MATLAB. Wiley, Chichester, UK