Scholarly article on topic 'Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design'

Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design Academic research paper on "Biological sciences"

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{heterogeneity / complexity / "visual quality" / "ecosystem services" / "multi-functional landscapes" / resilience}

Abstract of research paper on Biological sciences, author of scientific article — Iryna Dronova

Abstract Environmental heterogeneity has recently received increased attention due to its effect on biological diversity, ecosystem services and ecological resilience to disturbance and hazards. However, its relationships with landscape complexity as an indicator of visual aesthetic quality have not been yet extensively discussed. The purpose of this paper is to review different dimensions of environmental heterogeneity and to explore their potential for bridging visual quality with provision of other ecosystem services and resilience in landscape design, management and planning. This synthesis reveals the substantial overlap between spatial and temporal indicators of heterogeneity from ecological literature and the indicators of visual complexity, diversity and variety from the studies of subjective preferences and objective scenic beauty criteria. The potential of heterogeneity is also reviewed in the context of the relationship between visual quality and ecological resilience to perturbations, an increasingly important objective in the face of the global environmental change. The limitations of heterogeneity as a design and management goal are also discussed, including links between heterogeneity and disturbance, undesirable outcomes of excessive landscape complexity and present lack of criteria for its optimal levels. The synthesis concludes by identifying the key strategies and research needs to facilitate the application of this concept towards multi-functional landscapes supporting versatile ecosystem services together with scenic priorities.

Academic research paper on topic "Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design"

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Landscape and Urban Planning

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Review Article

Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design

Iryna Dronova

Department of Landscape Architecture & Environmental Planning, 202 Wurster Hall #2000, University of California Berkeley, CA 94720-2000, USA HIGHLIGHTS

• Dimensions of environmental heterogeneity are compared with indicators of visual complexity.

• Substantial overlap between ecological and aesthetic heterogeneity indicates the potential for cross-disciplinary bridge.

• Visual and ecological complexity may be jointly used to promote resilient and multi-functional landscapes.

• Future work should develop objective, replicable indicators of complexity applicable to both disciplines.

ARTICLE INFO

Article history:

Received 4 December 2016

Received in revised form 12 March 2017

Accepted 13 March 2017

Keywords:

heterogeneity

complexity

visual quality

ecosystem services

multi-functional landscapes

resilience

ABSTRACT

Environmental heterogeneity has recently received increased attention due to its effect on biological diversity, ecosystem services and ecological resilience to disturbance and hazards. However, its relationships with landscape complexity as an indicator of visual aesthetic quality have not been yet extensively discussed. The purpose of this paper is to review different dimensions of environmental heterogeneity and to explore their potential for bridging visual quality with provision of other ecosystem services and resilience in landscape design, management and planning. This synthesis reveals the substantial overlap between spatial and temporal indicators of heterogeneity from ecological literature and the indicators of visual complexity, diversity and variety from the studies of subjective preferences and objective scenic beauty criteria. The potential of heterogeneity is also reviewed in the context of the relationship between visual quality and ecological resilience to perturbations, an increasingly important objective in the face of the global environmental change. The limitations of heterogeneity as a design and management goal are also discussed, including links between heterogeneity and disturbance, undesirable outcomes of excessive landscape complexity and present lack of criteria for its optimal levels. The synthesis concludes by identifying the key strategies and research needs to facilitate the application of this concept towards multi-functional landscapes supporting versatile ecosystem services together with scenic priorities.

© 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Contents

1. Introduction.............................................................................................................................................91

2. Background and key definitions........................................................................................................................92

2.1. Environmental heterogeneity and its relationship to ecosystem services.....................................................................92

2.2. Visual landscape quality and aesthetic proxies of heterogeneity..............................................................................93

2.3. Components of environmental heterogeneity in relation to ES and visual quality............................................................93

3. Dimensions of environmental heterogeneity and their use in visual quality evaluation..............................................................93

3.1. Spatial heterogeneity............................................................................................................................93

3.1.1. Land cover composition...............................................................................................................93

3.1.2. Vegetation heterogeneity and biodiversity...........................................................................................96

3.1.3. Physical environmental heterogeneity................................................................................................96

E-mail address: idronova@berkeley.edu http://dx.doi.Org/10.1016/j.landurbplan.2017.03.005

0169-2046/© 2017 The Author(s). Published by Elsevier B.V. This is an open access article underthe CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

3.1.4. Edges and ecotones....................................................................................................................97

3.1.5. Vertical and 3-D heterogeneity.......................................................................................................97

3.2. Temporal heterogeneity.........................................................................................................................98

3.2.1. Short-term heterogeneity.............................................................................................................98

3.2.2. Long-term heterogeneity..............................................................................................................98

4. Heterogeneity, visual quality and resilience...........................................................................................................99

5. Limits to heterogeneity................................................................................................................................100

6. Synthesis and future research needs..................................................................................................................100

Acknowledgment......................................................................................................................................101

Appendix A..............................................................................................................................................??

References.............................................................................................................................................103

1. Introduction

One of the most challenging tasks in present-day environmental planning is reconciling the long-term objectives concerning ecosystem services (ES), conservation and protection against hazards with more immediate needs to improve and maintain visual landscape quality affecting human perception and valuation of places (Allan et al., 2015; Daniel, 2001; de Groot, Alkemade, Braat, Hein, & Willemen, 2010; Gobster, Nassauer, Daniel, & Fry, 2007; Kremen, 2005; Parrott and Meyer, 2012). This task requires an in-depth understanding of how ecological functions underlying critical ES affect landscape composition, structure and dynamics contributing to visual quality. In particular, environmental heterogeneity, broadly denoting non-uniformities in physical and ecological landscape characteristics, has been shown to influence biodiversity (Cardinale et al., 2006; Mace, Norris, & Fitter, 2012; Stein, Gerstner, & Kreft, 2014; Tscharntke et al., 2012), resilience of natural and human ecosystems to stressors (Hodbod, Barreteau, Allen, & Magda, 2016; Levine et al., 2016; Oliver et al., 2015), agricultural productivity (Kremen & Miles, 2012; Ostman, Ekbom, & Bengtsson, 2001) and landscape complexity related to visual aesthetic quality and preferences (de la Fuente de Val, Atauri, & de Lucio, 2006; Hasund, Kataria, & Lagerkvist, 2011; Junge, Schuepbach, Walter, Schmid, & Lindemann-Matthies, 2015; Kaplan & Kaplan, 1989; Lindemann-Matthies, Briegel, Schupbach, &Junge, 2010). However, despite the abundant research on the links between visual quality and complexity, their ecological underpinnings have not yet been extensively discussed, and the potential of environmental heterogeneity to sustain both landscape aesthetic benefits and ecological functionality remains under-studied.

The interest in multi-functional and visually appealing landscapes has persisted since the earlier legislative frameworks (e.g., Multiple Use-Sustained Yield Act of 1960 and the National Environmental Policy Act of 1969), becoming even more evident in recent pursuits for robust, ecologically informative landscape quality indicators (Cassatella, 2011; Fry, Tveit, Ode, & Velarde, 2009; Llausas and Nogue, 2012; Ode & Miller, 2011; Sowiinska-Swierkosz & Chmielewski, 2016; Tveit, Ode, & Fry, 2006). However, ecological mechanisms and functions controlling landscape determinants of visual quality have not been frequently discussed in such a context. Furthermore, in the earlier literature, the terms "picturesque" and "functional" were sometimes considered as distinct and not always compatible landscape properties (Hull and Buhyoff, 1986; Nassauer, 1986; Ulrich, 1986), while some analysts also noted the tension between ecological and cultural underpinnings of aesthetic preference (de la Fuente de Val et al., 2006; Gobster et al., 2007; Mozingo, 1997; Tveit et al., 2006). These issues have stimulated an important ecological aesthetic discourse on to what extent the landscapes can be both functional and visually pleasing (Carlson, 2001; Gobster et al., 2007; Nassauer & Opdam, 2008), and which spatial and temporal attributes are particularly useful in connecting these objectives (de Groot et al., 2010; Fry et al., 2009; Lindemann-

Matthies et al., 2010; Tveit et al., 2006). However, the potential of ecological heterogeneity in this capacity has not yet been explicitly discussed.

Importantly, such a potential is strongly suggested by both ecological and landscape aesthetic literature (Allan et al., 2015; de la Fuente de Val et al., 2006; Nassauer, 1997). Several comprehensive recent reviews report the significance of environmental heterogeneity for biological diversity and resilience (Oliver et al., 2015; Spasojevic et al., 2016; Stein et al., 2014) with major implications for critical ES, such as agricultural food production, water and air quality, ecosystem regulation (pollination, pest control, soil quality), landscape connectivity, and more (Cardinale et al., 2012; Kremen & Miles, 2012; Tscharntke et al., 2012). Similarly, both subjective indicators of visual preference and objective scenic quality criteria have been frequently associated with landscape complexity and variety - concepts related to but not directly representing environmental heterogeneity (Herzog & Barnes, 1999; Kaplan & Kaplan, 1989; Tveit et al., 2006; Ulrich, 1986), discussed in Section 2 in more detail. However, the evidence of favorable effects of complexity on visual quality has not been uniform, showing positive, negative as well as more complex and context-dependent relationships (Coeterier, 1996; Kaplan & Kaplan, 1989; Sevenant & Antrop, 2010; Stamps, 2004). For instance, in the meta-analysis by Stamps (2004) the correlation between complexity and preference ranged from -0.11 to 0.97, precluding a singular interpretation of their association. In various studies summarized by Kaplan and Kaplan (1989), the rank of complexity among other visual criteria varied, but often was moderately important. In contrast, two studies of perception in the Dutch (Coeterier, 1996) and Belgian (Sevenant & Antrop, 2010) landscapes did not find complexity to be significant. This uncertainty parallels the gaps in ecological understanding of specific effects of heterogeneity on different ES (e.g., Balvanera et al., 2014; Cardinale et al., 2012) and underscores the need for cross-disciplinary investigations of the links between visual complexity, environmental heterogeneity and their synergistic benefits for landscape management and ES goals.

Some efforts have also been made to link visual landscape quality with ecological heterogeneity and diversity, such as the Bio-philia concept referring to human affection and affinity for nature and its elements (Grinde & Patil, 2009; Van Den Born, Lenders, De Groot, & Huijsman, 2001). However, few studies have addressed specific mechanisms by which environmental and ecological heterogeneity translate into aesthetic quality (Ding, Tang, Dai, & Wei, 2014; Junge et al., 2015; Lindemann-Matthies et al., 2010; Sowiinska-Swierkosz, 2016), which could be then used to develop a stronger bridge between functional and aesthetic priorities in landscape planning (Fig. 1). The need to better understand this potential is especially important in the face of planning challenges posed by the degradation of natural resources, climate change and food security issues (Allan et al., 2015; de Groot et al., 2010; Hodbod et al., 2016; Ungaro, Haefner, Zasada, & Piorr, 2016) and worldwide homogenization of both urban and rural human-dominated

Fig. 1. Conceptual relationship between the roles of heterogeneity in ecological and landscape aesthetic disciplines.

landscapes accompanied by dramatic losses of biodiversity and ES (Cardinale et al., 2012; Groffman et al., 2014; Karp et al., 2012), in spite of high aesthetic priorities (Cook, Hall, & Larson, 2012; de Groot et al., 2010; Larson et al., 2016). Using heterogeneity may be an important strategy to remedy some of these effects while addressing aesthetic and cultural needs as win-win scenarios (Boisramei, Thompson, Collins, & Stephens, 2016; de Groot et al., 2010; Kienast, Frick, van Strien, & Hunziker, 2015; Ponisio, M'Gonigle, & Kremen, 2016). However, a more profound understanding of the dimensions, benefits and limitations of ecological and visual heterogeneity is necessary in order to bridge together ES and visual quality objectives in landscape management, planning and design.

To address this need, the goal of this paper is to examine the intersections between ecological and aesthetic interpretations of environmental heterogeneity and its potential benefits and constraints for landscape multi-functionality. Given the massive body of previous research on ecological heterogeneity and ES (e.g., Cardinale et al., 2006; Cardinale et al., 2012; Mace et al., 2012; Oliver et al., 2015; Stein et al., 2014; Tscharntke et al., 2012), selection of literature for this review had started with the search for studies on aesthetic quality using keywords such as "heterogeneity"; "complexity"; "diversity"; "variety" together with "visual quality"; "aesthetic quality"; "landscape preference"; "scenic quality" within research databases such as Thomson Reuters Web of Science and studies referenced in earlier landscape quality research. The pool of relevant publications included 7 relevant books and manuals and 92 peer-review articles and book chapters; 12 of which were theoretical studies and/or reviews of previous literature; 65 were case studies relevant to the review objectives and 15 did not explicitly discuss either heterogeneity proxies or visual quality and thus were either excluded or cited as supplementary papers. This literature was used to identify several major dimensions of heterogeneity discussed in Section 2 (Table 2); and relevant ecological studies were then incorporated for each dimension using more specialized reference searches.

Specific objectives of this synthesis were: 1) to review major dimensions of environmental heterogeneity and their relevance to key ES and heterogeneity proxies in subjective and objective criteria of visual landscape quality, and 2) to assess the potential for a bridge between visual quality and ES-oriented goals based on their links with heterogeneity and to identify key future research directions. The focus of this review is specifically on visual complexity and its relationship with environmental heterogeneity, while detailed discussions of other visual quality criteria and psychological underpinnings of individual preferences are beyond this review's scope and can be found in previous studies (Clay & Smidt, 2004; Daniel, 2001; Kaplan & Kaplan, 1989; Tveit et al., 2006; Zube, Sell, & Taylor, 1982). Similarly, to maintain its primary focus, this review does not extend to the domain of cultural values

and implications of heterogeneity - another important research direction highlighted in recent literature (Kienast et al., 2015). The synthesis starts with a condensed overview of the key concepts related to environmental heterogeneity, ES and visual quality and then discusses different spatial and temporal dimensions of their relationships and applications. Next, the potential of heterogeneity and visual quality is reviewed in the context of ecological resilience as an important management goal encompassing multiple ES, followed by a discussion of limitations and constraints of heterogeneity in practical landscape decision. The final section summarizes the reviewed evidence and identifies several strategies for the future research and applications of heterogeneity towards multi-functional landscapes.

2. Background and key definitions

2.1. Environmental heterogeneity and its relationship to ecosystem services

The central focus of this synthesis is on the environmental heterogeneity as an umbrella term covering spatial environmental heterogeneity referring to non-uniformities in land cover, vegetation, climate, soil and/or topography (Stein & Kreft, 2015; Stein et al., 2014) and temporal variability of landscapes represented by both short-term seasonality and long-term transitions of suc-cessional vegetation and land cover. Ecological interpretations of heterogeneity are extremely broad; for example, recent reviews (Stein & Kreft, 2015; Stein et al., 2014) found that spatial environmental heterogeneity alone was represented by more than 100 different terms spanning different scales. The overall potential of heterogeneity to directly affect the diversity of color, form, texture and thus aesthetic properties of landscape elements and cover types underlies its immediate relevance to both human aesthetic preferences and landscape scenic quality criteria. From this perspective, specific heterogeneity components, such as biological diversity of plants and animals, become most relevant to ecological aesthetic discourse at spatial scales closest to human perception or planning decisions affecting the latter.

Various aspects of environmental heterogeneity and biodiversity have been reported as critical to many ecosystem services (ES), or human-centric benefits of ecosystems (MA, 2005) in the context of adaptive management (Allan et al., 2015; Cardinale et al., 2012; de Groot et al., 2010; Ungaro et al., 2016) and "working landscapes" integrating human presence and land use priorities with conservation objectives (Box 1; Kremen & Miles, 2012; Tscharntke et al., 2012). Four groups of ES are distinguished (MA, 2005): provisioning (e.g., food production), regulating (e.g., controls of climate and hazards), supporting (indirectly benefiting humans via functions such as crop pollination), and cultural (spiritual, aesthetic, recreational). The importance of environmental heterogeneity and

Box 1: Glossary of major terms relevant to the key discussion points of this review.

Environmental heterogeneity - used here as an umbrella concept representing the degree of non-uniformity in land cover, vegetation and physical factors (topography, soil, climate), following Stein et al. (2014) and Stein and Kreft (2015). Ecosystem services - human-centric benefits of ecosystems broadly distinguished as provisioning, regulating, supporting and cultural services (Millennium Ecosystem Assessment, 2005).

Biological diversity - the variety of species, higher-order taxa orfunctional groups in a given ecosystem or habitat based on their number and/or relative abundance. Visual quality - used here as an umbrella term representing scenic beauty, aesthetically pleasing landscape attributes and subjective visual preferences (Lothian, 1999, 2017; Daniel, 2001).

Resilience - the ability of the system to recover its essential functions and properties following a perturbation (Holling, 1973; Oliver et al., 2015).

Resistance (robustness) - the capacity of a system to maintain its properties and function when exposed to a disturbance or stressor (D'Antonio & Thompsen, 2004; Elton 1958). Edge - a boundary between patches of different land cover, ecosystem or habitat types

Ecotone - a transitional zone between different land cover, ecosystem or habitat types

Fragmentation - breaking up of covertype or habitat patches into smaller disconnected units (Turner et al., 2001). Succession - gradual change of ecosystem biotic and abiotic components and processes over time.

Working landscape - a landscape where human economic priorities and land use objectives are integrated with ecological and conservation goals.

Phenology - the study of seasonal natural phenomena such as changes in biological cycles and activity of plants and animals.

Diversified agriculture systems - systems of farming and agricultural production that, through a combination of different practices incorporate agricultural biodiversity at multiple spatial and/or temporal scales (Kremen & Miles, 2012).

biodiversity has been frequently attributed to the first three types (Cardinale et al., 2012; Turner, Donato, & Romme, 2013), while aesthetic benefits have been discussed mainly as cultural ES (Kaplan, 1995; Lindemann-Matthies et al., 2010; Ulrich, 1986; Van Zanten et al., 2016).

2.2. Visual landscape quality and aesthetic proxies of heterogeneity

In this review, "visual quality" is also used as an umbrella term to denote scenic beauty, aesthetic quality and visual preferences as determined by both objective criteria and subjective responses in previous research (Daniel, 2001; Lothian, 1999). From the subjective view, beauty is "in the eye of the beholder" (Lothian, 1999), and preferences for landscape aesthetic qualities vary among individuals of different age, profession, background, cultural heritage, environmental expertise and other social categories (de la Fuente de Val et al., 2006; Dramstad, Tveit, Fjellstad, & Fry, 2006; Kaplan & Kaplan, 1989; Ulrich, 1986). In the objective paradigm, aesthetic criteria are determined from the underlying mechanisms of quality that may be more generally applicable to groups. These may include factors affecting human physiological and psychological responses regardless of their background (Appleton, 1975; Kaplan & Kaplan, 1989; Orians, 1986), as well as environmental characteristics affecting specific elements of visual appearance (Bureau

of Land Management, 1984; Forestry Commission, 1994; Herbst, Forster, & Kleinschmit, 2009; USDA, 1995).

In visual quality studies, heterogeneity has been represented by a suite of somewhat overlapping terms such as "complexity", "diversity", "variety", whose specific definitions vary among research contexts, spatial scales and methods (Table 1). In the studies of human preference based on the landscape photographs and visualizations, complexity often represents the overall visual richness and information content (Table 1; Gallagher, 1977; Herzog, 1989; Herzog & Barnes, 1999; Herzog et al., 1982; Kaplan & Kaplan, 1989; Zube, 1973), while diversity and variety address visual contrast, versatility of elements, colors and textures (Table 1; Clay & Smidt, 2004; de la Fuente de Val et al., 2006; Kuiper, 1998; Weinstoerffer & Girardin, 2000). In turn, studies considering broader landscape context in the form of aerial photographs, maps or those proposing objective criteria of scenic beauty often use more specific metrics (Table 2 and Table A1, Appendix A) representing compositional variety and spatial pattern of land cover (Dramstad et al., 2006; Herbst et al., 2009; Martin, Ortega, Otero, & 2016; Ode, Hagerhall, & Sang, 2010), vertical and 3-dimensional landscape structure, color diversity and other (e.g., Clay & Daniel, 2000; Hunziker, 1995; Ozkan, 2014; Polat & Akay, 2015). Ecological definition of landscape complexity by Stein and Kreft (2015) similarly emphasizes between-habitat heterogeneity that can be quantified using composition or diversity indices. However, explicit attribution of visual complexity to heterogeneity in ecosystem structure or function has only rarely been discussed, such as the effect of wildflower diversity and their habitat on scenic beauty and public perception (Lindemann-Matthies et al., 2010).

2.3. Components of environmental heterogeneity in relation to ES and visual quality

To investigate the intersections among ecological and landscape aesthetic interpretations of heterogeneity and its relevance to important ES, this review considers several dimensions of the concept (Table 2). Spatial heterogeneity is the most commonly discussed aspect of landscape complexity, and also the most versatile, including diversity of land cover types, physical factors, such as topography, biological components, particularly vegetation (Stein et al., 2014) and special structural indicators, such as edges and ecotones. Vertical and 3-dimensional (3-D) complexity of vegetation canopies and man-made elements is discussed as a separate aspect of spatial heterogeneity due its unique effects on ES and aesthetic quality (Kalivoda, Vojar, Skrivanova, & Zahradnik, 2014; Stein et al., 2014; Turner et al., 2013). Finally, temporal dimension refers to various landscape dynamics controlling ES, scenic properties and landscape perceptions (Kienast et al., 2015; Tveit et al., 2006). These include both short-term variation manifested in phenology, or seasonal dynamics of natural and human-related processes, and long-term transitions such as ecological succession and land cover/land use change (Table 2).

3. Dimensions of environmental heterogeneity and their use in visual quality evaluation

3.1. Spatial heterogeneity

3.1.1. Land cover composition

Land cover heterogeneity represents compositional diversity and complexity of geometry and configuration of both natural and anthropogenic cover type patches (Table 2 and Table A1, Appendix A), all of which have been widely considered in both ecological and visual quality studies (Frank, Fuerst, Koschke, Witt, & Makeschin, 2013; Herbst et al., 2009; Ode & Miller, 2011; Ode,

Table 1

Major indicators of visual heterogeneity in the studies of aesthetic quality.

Metric

Deflnition(s)

Studies using

Complexity

Diversity

Heterogeneity Biodiversity

Texture

Pattern Variety

Color diversity and contrast

3D complexity

Relief

Number ofindependently perceived visual elements in the scene

Visual richness, the degree of scene intricateness and "how much is going on"

The amount of information or the number of elements in the immediate environment

The promise of more information if one has more time to observe from the specific point

The degree of simplicity versus complexity in the spatial structure

Presence of multiple elements with diverse forms Diversity, richness and interspersion of landscape elements at a given resolution

The amount of diversity or variety in a scene, the engaging amount of information

The perceived degree of landscape variety (from not varied to varied)

Composition, distribution, organization and variation of landscape elements contributing to visual richness and diversity

The degree of perceived visual variation among landscape elements

Visual diversity; the number and degree of image elements or different features

The diversity of landscape components "as the expression of vertical relationships between land use and abiotic features"

"Simply describes differences in nature, quality or aspect", also "the nature and relative size of the fields within the farm"

Composition, diversity and relative abundance (evenness) of landscape cover types and land uses

Grain size, visual compartmentalization and versatility within the landscape

Diversity of plants, insects or specific ecological groups relevant to scenic properties

The attribute of visual quality evaluated as smooth, medium or rough, or proportion of the landscape area covered by it

Presence of regularly repeated elements or clear patterns Scene as being varied or diverse in overall content; "diversity of colors, textures, shapes and masses, forms and spaces or other visible attributes that add a diversity or mixture of visual experiences" Variety of colors, chromatic diversity, visual contrast among available colors

Heterogeneity in tree height and vertical vegetation layers

Visual grouping, density and structuring of vegetation, thinning intensity (managed ecosystems)

Presence of specific structural vegetation forms such as tree, bush

Presence/absence & diversity of man-made elements, either overall or as modification to the landscape, sometimes as undesirable factor

Presence, amount or density of distinct borders between areas

Presence of linear edge features such as hedgerows, walls, tree lines; visual properties of field margins Edge condition

Topographic heterogeneity, variability in relief, non-uniform geomorphology, contrast between flat and sloping

Ellsworth, 1982; Gallagher, 1977; Herzog, 1989; Herzog and Barnes, 1999; Kaplan and Kaplan, 1989; Ulrich, 1986 Ellsworth, 1982; Galindo & Hidalgo, 2005; Gallagher, 1977; Herzog, 1989; Kaplan & Kaplan, 1989 Abkaret al., 2014; Herzog, 1984; Herzog et al., 1982

Herzog, 1984; Herzog, 1985; Kaplan, & Kaplan, 1982

de la Fuente de Val et al., 2006

Sevenant & Antrop, 2009, 2010 Martin et al., 2016; Tveit et al., 2006

Gimblett, 1990

van den Berg et al., 1998, Fathi & Masnavi, 2014

Dramstad et al., 2001, 2006; Fjellstad et al., 2001; Germino et al., 2001; Ode et al., 2010; Ode & Miller, 2011

Dramstad et al., 2001

de la Fuente de Val et al., 2006; Forestry Commission, 1994; Sowinska-Swierkosz and Chmielewski, 2016 Kuiper, 1998

Weinstoerffer and Girardin, 2000

Dramstad et al., 2001, 2006; Fjellstad et al., 2001; Frank et al., 2013; Germino et al., 2001; Herbst et al., 2009; Lindemann-Matthies et al., 2010; Palmer, 2004; Ode et al., 2010; Ode & Miller, 2011; Sahraoui et al., 2016; Schirpke et al., 2013; Schuepbach et al., 2016; Ungaro et al., 2016

de la Fuente de Val et al., 2006; Dramstad et al. 2001; Ode & Miller, 2011

Dramstad et al., 2001; Hasund et al., 2011; Hunziker, 1995; Lindemann-Matthies et al., 2010; Polat & Akay, 2015; van den Berg et al., 1998 Acar & Sakici, 2008; Arriaza et al., 2004

de la Fuente de Val et al., 2006 Clay & Smidt, 2004

Acar and Sakici, 2008; Arriaza et al., 2004; Clay and Daniel, 2000; de la Fuente de Val et al., 2006; Hands and Brown, 2002; Hunziker, 1995; Ozkan, 2014; Polat and Akay, 2015; Uzun & Muderrisoglu, 2011

Aminzadeh & Ghorashi, 2007; Brown & Daniel, 1986; Chen et al., 2015; Chen & Xu, 2016; Hunziker, 1995 Aminzadeh & Ghorashi, 2007; Brown & Daniel, 1986; Deng et al., 2014; Herzog & Barnes, 1999; Hull & Buhyoff, 1986; Natori & Chenoweth, 2008; Uzun & Muderrisoglu, 2011 Polat and Akay, 2015

Acaret al., 2006; Kalivoda et al., 2014; Sowinska-Swierkosz, 2016; Uzun& Muderrisoglu, 2011

Forestry Commission, 1994; Gallagher, 1977; Germino et al., 2001; Herbst et al., 2009; Ode et al., 2009, 2010; Zubeetal., 1974

Howley et al., 2012; Junge et al. 2015; Sowinska-Swierkosz & Chmielewski, 2016; Van Zanten et al., 2016 Ellsworth, 1982

Aminzadeh & Ghorashi, 2007; Angileri & Toccolini, 1993; de la Fuente de Val et al., 2006; Germino et al., 2001; Hammitt et al., 1994; Natori & Chenoweth, 2008; Polat & Akay, 2015; Uzun & Muderrisoglu, 2011

Table 1 (Continued)

Metric

Definition(s)

Studies using

Ephemera and seasonality

Time depth

Presence of elements and types of land use that change

with seasons or over time

Perception of seasonal change

Visual evidence of historical continuity and diversity,

sometimes as architectural variety and presence of

landmarks

Level of succession (in woodlands)

Junge et al., 2015, 2015; Fathi & Masnavi, 2014; Martin et al., 2016; Tveit et al., 2006 Aminzadeh & Ghorashi, 2007

Kalivoda et al., 2014; Sowinska-Swierkosz & Chmielewski, 2016

Ode et al., 2009

Table 2

Dimensions of environmental heterogeneity in relation to ecosystem services and visual quality.

Dimension

Method of assessment

Effects on ecosystem functioning & services

Contribution to visual quality

Spatial heterogeneity Landscape cover type diversity

Biological diversity, esp. vegetation

Topographic variety

Presence & amount of edges & ecotones

Heterogeneity of vertical & 3-D structure

Temporal heterogeneity Short-term: seasonality & phenology

Long-term: succession

Long-term: land cover & land use transitions

Landscape metrics of diversity, shape, aggregation

Various indices of number & abundance of species, communities or functional groups

Visual assessment or metrics from maps & digital elevation data

Landscape metrics of the amount & density of edge

Visual perception of complexity & layers

Heterogeneity of heights & vertical structure

Sky-view factor & plant canopy structure based on optical or lidar-based measurements

Observational studies Quantitative indices of changes in vegetation greenness based on field or remote sensing observations Proportion of land cover with some degree of seasonal change

Approximation using vegetation age structure & size heterogeneity Long-term monitoring of vegetation composition

Spatial & remote sensing analyses

Overall diversity of resources & ES present in the landscape

Diversity of responses to perturbations Functional redundancy contributing to stability, complementarity & resilience of ecosystems & their various services Fragmentation & losses of habitat, resources & Diversity of habitats & environments relevant to different ES

Regulation of hydrological flows, microclimates, soil properties Effects on hazards & risk factors (e.g., landslides)

Connectivity, dispersal, regulation of nutrient,

water & energy flow

Microclimate regulation

Support of biodiversity & edge species

Fragmentation & barriers to connectivity,

especially in heavily human-modified

landscapes

Transfer, capture & utilization of solar energy by plants affecting provisioning & regulating ES such as productivity Regulation of microclimate (temperature, humidity, wind) in natural & human (e.g., urban) ecosystems

Effects on risk factors (excessive vegetation overgrowth, undesirable effects of man-made structures, etc.)

Biological & reproductive cycles of plants & animals affecting provisioning, regulating & supporting ES (e.g., crop production cycles & pollination)

Animal migration affecting resources & ecosystem functions (e.g., food resources, grazing)

Environmental regimes affecting various ecosystem functions & dispersal (e.g., in fluvial systems), as well as risk factors Disturbance & hazards associated with seasonal phenomena

Long-term accumulation of biomass, nutrients, biological diversity, ecosystem stability, diversification of ecosystem function, services & habitats

Disturbance regimes & ecosystem feedbacks affecting ES & their ecological regulation Changes in resources, ecosystem functions & habitat diversity

Anthropogenic effects, loss of resources & species, gradual fragmentation

Visual diversity & variety

Excessive heterogeneity, especially in

anthropogenic fragmented landscapes

Colors

Textures

Scenic wildlife Visual diversity & variety Sense of openness Visual depth

Visual diversity & variety Orderliness, legibility & focal points Enhancement of scenic value via attraction of charismatic fauna

Visual richness & complexity Visual effects associated with complex 3-D structure - scenic canopies, dappled shadows, visual layering The degree of visual openness (based on 3-D configuration & density) The sense of prospect & refuge

Seasonal variation in visual quality & landscape use

Visually attractive seasonal biological phenomena (e.g., flowering, leaf coloration, animal migration) & weather-related events (e.g., snow, fog) Seasonally variable reflective patterns of the water bodies, often in relation to surrounding landscape

Diversity of tree age as a visual factor; presence of old trees as aesthetic & cultural factor

Visual appeal of specific heterogeneous successional stages, e.g., in frequently disturbed systems Visual diversity & variety, enhancement or degradation of picturesque qualities depending on the nature of dynamics

Fry, Tveit, Messager, & Miller, 2009; Ode et al., 2010; Palmer, 2004; Turner, Gardner, & O'Neill, 2001 ). Various quantitative spatial metrics have been proposed to describe variety, relative abundance, shape, aggregation and adjacency of cover types (Hargis, Bissonette, & David, 1998; Ode and Miller, 2011; Turner et al., 2001 ), as well as landscape pattern and texture (Fjellstad, Dramstad, Strand, & Fry, 2001 ; Forman, 1995; Ozkan, 2014; Schirpke, Tasser,

& Tappeiner, 2013; Turner et al., 2001; Weinstoerffer & Girardin, 2000). Land cover heterogeneity is an extremely scale-sensitive concept, because the diversity of ecological or visual entities is only meaningful within a particular spatial extent and at a given spatial resolution (Galindo & Hidalgo, 2005; Ode et al., 2010), or from a specific vantage point and distance of the view (Dramstad et al.,

2006; Germino, Reiners, Blasko, McLeod, & Bastian, 2001; Martin et al., 2016; Ode et al., 2010; Schirpke et al., 2013; Ulrich, 1986).

In ecological literature, cost-effective spatial metrics derived from maps and remote sensing data have been instrumental for characterizing various ES associated with the diversity of species, habitats and resources (Colwell & Lees, 2000; Frank et al., 2013; Kumar, Stohlgren, &Chong, 2006; Leitao &Ahern, 2002; McGarigal & McComb, 1995; Uuemaa, Mander, &Marja, 2013; Wu, Shen, Sun, & Tueller, 2002) and assessing losses of ES and connectivity following human-driven landscape fragmentation (Hargis et al., 1998). Frequently reported positive association between spatial heterogeneity and biodiversity (Fahrig et al., 2011; Loreau, Mouquet, & Holt, 2003; Stein et al., 2014; Tscharntke et al., 2012) represents beneficial ecological feedbacks resulting from: 1) the overall diversity of resources and ES; 2) the diversity of responses to disturbance and hazards; and 3) functional redundancy which promotes ecological stability (Cardinale et al., 2012; Oliver et al., 2015; Tilman, Wedin, & Knops, 1996; Wang & Loreau, 2014). These feedbacks play critical roles in both provisioning and regulating ES at different scales. Even in highly fragmented human-dominated landscapes, heterogeneity in the form of remnant habitats and protected areas enhances biodiversity refugia, landscape connectivity and maintenance of pre-existing ES such as regulation of nutrient and water cycling (Loreau et al., 2003; Thiere et al., 2009; Ziter, Bennett, & Gonzalez, 2013).

Visual quality has also been positively associated with spatial indicators of heterogeneity such as land cover type diversity and evenness (de la Fuente de Val et al., 2006; Frank et al., 2013; Herbst et al., 2009; Schirpke et al., 2013; Schuepbach, Junge, Lindemann-Matthies, & Walter, 2016) or patch shape complexity (Frank et al., 2013; Schirpke et al., 2013). One study using simulated landscapes, however, reported negative relationship of preference with diversity and aggregation metrics (Ode & Miller, 2011), suggesting that the effects of landscape structure on visual quality may be highly context-specific (Frank et al., 2013; Palmer, 2004). For instance, landscape fragmentation indices may be used to reveal negative effects on visual preference from factors such as intensive urban development (Palmer, 2004; Sahraoui, Clauzel, & Foltete, 2016). Multiple studies also used land cover heterogeneity metrics to link the perceptions of local, constrained vistas with their broader spatial context, such as in the analyses of agricultural landscapes in Norway (Dramstad et al., 2001, 2006), Switzerland (Schuepbach et al., 2016) and even computer-simulated landscapes (Ode et al., 2009). Such multi-scale assessments are particularly important to elucidate specific combinations of environmental factors, ecological interactions and connectivity underlying scenic properties of complex vistas. Furthermore, subjective perceptions of landscapes by different social groups may also depend on their broader knowledge of resource diversity, agricultural, recreational value and environmental awareness (Buhyoff, Wellman, & Daniel, 1982; Dramstad et al., 2001; Lindemann-Matthies et al., 2010; Rogge, Nevens, & Gulinck, 2007; van den Berg, Vlek, & Coeterier, 1998).

3.1.2. Vegetation heterogeneity and biodiversity

Heterogeneity and diversity of vegetation is a critical determinant of both the overall complexity of ecosystems, including animal diversity and food-energy flow networks (Dramstad et al., 2001; Kumar et al., 2006; Stein et al., 2014), and their aesthetic quality at different scales (Acar & Sakici, 2008; Clay & Daniel, 2000; Hands & Brown, 2002; Hunziker, 1995; Lindemann-Matthies et al., 2010; Wong and Domroes, 2005; Zube, 1973). Similar to land cover composition, vegetation diversity is typically measured as simple richness, evenness or more complex mathematical indices accounting for number and relative abundances of different species, higher taxonomic units or functional groups composed of different species with similar traits (Table A1, Appendix A). Plant diversity

directly affects critical ecosystem functions such as production and retention of biomass, nutrient cycling, resistance to pathogens and invasions and resilience following disturbance (Cardinale et al., 2006; Oliver et al., 2015; Tilman et al., 1996), which are fundamental to all four important ES categories (Cardinale et al., 2012; Mace et al., 2012). Both structural and compositional heterogeneity of vegetation have major influence on the complexity of animal habitats (Stein & Kreft, 2015) and thus contribute to ES associated with animal activity, such as nutrient cycling, pollination, plant dispersal, pest control and other.

At the same time, morphological and physiological diversity of plant adaptations to environmental conditions and stressors (Westoby, Falster, Moles, Vesk, & Wright, 2002) produces versatile forms, textures, life habits and colors affecting visual quality (Clay & Daniel, 2000; de la Fuente de Val et al., 2006; Hands & Brown, 2002; Polat & Akay, 2015). Examples of such effects include the aesthetic appeal of species-rich wildflower meadows in Norway (Lindemann-Matthies et al., 2010), southern Utah, USA (Clay & Daniel, 2000) and ecological rehabilitation sites in Canada (Hands & Brown, 2002); visual variety of contrasting vegetation types as "the mosaic of fields, closed canopy forests and open woods" in Hunziker (1995); positive effect of plant species and form diversity on visual preferences for urban recreation areas in a Turkish city (Polat and Akay, 2015), to name a few. Physiological stress, such as pest effects in Buhyoff et al. (1982), may also contribute to visual heterogeneity either favorably or negatively, depending on the viewer's interpretation. Finally, animal diversity associated with heterogeneity of vegetation and habitat has been also recognized as the component of aesthetic quality such as bird and butterfly diversity in Dramstad et al. (2001) or attractive wildlife (Frederick, Gawlik, Ogden, Cook, & Lusk, 2009).

3.1.3. Physical environmental heterogeneity

Heterogeneity in topographic, climatic and soil properties underlies multiple aspects of land cover and vegetation diversity (Godfree et al., 2011) and strongly affects both availability and accessibility of different ES as well as landscape stressors. Among various physical determinants of heterogeneity, topography and geomorphology most directly affect visual quality based on the appearance of terrain and landscape elements (Acar & Sakici, 2008; Germino et al., 2001). Topographic heterogeneity ranges from micro-scale structural features to broad-scale relief and physiographic forms (Stein et al., 2014) and can be measured from spatially explicit elevation data such as contour maps, terrain and digital elevation models (DEMs) and data from active remote sensing radar and lidar instruments (Chen, Xua, & Gao, 2015; Germino et al., 2001; Martin et al., 2016). Topographic variety affects soil and microclimate heterogeneity and key environmental processes such as hydrological flows, which together may have reciprocal relationships with composition and function of vegetation and associated ES (Barnes, Zak, Denton, & Spurr, 1997). Specific topographic properties, e.g., slope steepness, may facilitate or inhibit important ES by affecting erosion or accumulation of soil, retention of nutrients and the risk of hazards such as landslides.

Studies from different regions have acknowledged the contributions of topographic variety to visual richness and morphological versatility (Acar & Sakici, 2008; Germino et al., 2001; Hammitt, Patterson, & Noe, 1994; Kuiper, 2000; Sahraoui et al., 2016; Schirpke et al., 2013). Topographic contrasts also affect the perceived sense of depth, an important aspect of visual quality based on the viewable, unobstructed area from a given vantage point (Germino et al., 2001; Martin et al., 2016). Assessments of topographic variety in visual quality studies have used both quantitative metrics (e.g., Germino et al., 2001; Table A1, Appendix A) and more subjective, perception-based degree of terrain and architectural complexity (e.g., Aminzadeh & Ghorashi, 2007; Angileri &Toccolini,

1993; Polat & Akay, 2015; Uzun & Muderrisoglu, 2011; Table 1). In the latter case, topographic effects on visual quality have often been integral with presence, diversity and arrangement of other elements, including vegetation and man-made structures.

3.1.4. Edges and ecotones

Edges, or borders between cover types, serve as important indicators of landscape complexity and fragmentation (Hargis et al., 1998; Turner et al., 2001) that often differ in their ecological and biophysical properties from patch interior (Ries & Sisk, 2010; Turner et al., 2001). The term "ecotone" is synonymous; however, it more specifically denotes the transitional zone between distinct environments or habitats (Walker et al., 2003), such as wetland zones between land and open water (Ellsworth, 1982; Ito, Ito, Matsui, & Marutani, 2006; Jiang, Gao, & DeAngelis, 2012; McBride & Strahan, 1984; Ward, Tockner, & Schiemer, 1999). Length and relative amount of such features can be assessed by several common metrics (Table 2; Table A1, Appendix A). By their nature, edges and ecotones represent directional gradients of moisture, climatic factors and vegetation structure (Davies-Colley, Payne, & van Elswijk, 2000) affecting landscape connectivity for nutrients, water, organisms, nutrients and seeds (Cadenasso & Pickett, 2001; Haynes & Cronin, 2006; Manson, Ostfeld, & Canham, 1999; Morandin, Long, & Kremen, 2016; Walker et al., 2003; Ward et al., 1999). They also mediate ecological responses to disturbance such as wind and flooding (Jiang et al., 2012; Kondolf, 2011) and promote high biological diversity, including species uniquely adapted to transitional conditions (Baker, French, & Whelan, 2002; Lidicker, 1999; Ries and Sisk, 2010). However, within fragmented and human-modified landscapes, edges may also signify losses of biodiversity, habitat and ES of pre-existing ecosystems (Ewers, Thorpe, & Didham, 2007) and physical barriers to connectivity and dispersal.

Multiple studies have used edge density and shape as criteria for scenic beauty or its perception (Dramstad et al., 2006; Forestry Commission, 1994; Herbst et al., 2009; Ode et al., 2009), although specific effects have varied. In a study with simulated landscapes (Ode et al., 2009), edge shape index had a significant but weak effect on preference scores, with higher preference for low to medium values. Germino (2001) considered the amount of edge as a complexity indicator and found it to be a significant, though not too strong, predictor of landscape diversity in the U.S. Rocky Mountains region. In Gallagher (1977), edge in landscape photographs was a moderately strong standalone predictor of preference and weak when considered with other factors. In contrast, scenic preferences in the southern Connecticut River Valley in Zube (1973) favored the extremes of high and low edge but not the middle range. Generally, however, features such as hedges, tree rows, river banks, species-rich field margins and forest edges have been seen as beneficial for scenic quality due to their structure (Forestry Commission, 1994; Herbst et al., 2009; Junge et al., 2015; Lindemann-Matthies et al., 2010; Schirpke et al., 2013; USDA, 1995; Weinstoerffer & Girardin, 2000) or ability to attract charismatic wildlife (Frederick et al., 2009). As such, edges offer special, yet still under-explored opportunities to link scenic priorities with other ES, such as promoting biodiversity and nutrient retention with small wetlands in anthropogenic landscapes (Thiere et al., 2009) or facilitating pollinators, biological pest control and thus productivity and resilience of crops with hedgerows and remnant woodland edges in diversified agriculture systems (Kremen & Miles, 2012; Morandin& Kremen, 2013; Morandin et al., 2016; Ponisio, M'Gonigle et al., 2016).

3.1.5. Vertical and 3-D heterogeneity

Vertical and 3-D complexity refers to composition and configuration of natural and man-made elements above the ground surface. Ecosystem 3-D structure can be characterized with various metrics, many of which represent vegetation (Table 2; Table A1,

Appendix A), such as popular canopy leaf area index (LAI; Table A1, Appendix A) and foliage height diversity as the indicator of structural complexity (Stein & Kreft, 2015). Many such metrics have been traditionally measured using field techniques, such as destructive sampling, or indirect measurements of canopy to sky ratio based on above-looking photographs or light attenuation records (Chen & Cihlar, 1995; Dronova, Bergen, & Ellsworth, 2011; Ellsworth & Reich, 1993; Hardiman, Bohrer, Gough, Vogel, & Curtis, 2011). In recent decades, such metrics have been increasingly derived via remote sensing surveys of sites and broader regions, particularly with lidar instruments delivering high-resolution, spatially explicit information on horizontal and vertical distributions of landscape elements (Chen &Xu, 2016; Chen et al., 2015). Other metrics consider both vegetated and non-vegetated elements (e.g., buildings). For instance, the sky-view factor showing the degree of sky obstruction (Table A1, Appendix A) has been used by the studies of ES related to urban microclimate and heat regulation (Eliasson, 1996) and more recently discussed with respect to visual quality (Pardo-Garcia & Merida-Rodriguez, 2017).

The 3-D structure of ecosystems controls many of their important services, including transfer and interception of solar radiation, productivity, nutrient cycling and sequestration of atmospheric CO2 (Dronova et al., 2011; Ellsworth & Reich, 1993; Hardiman et al., 2011), regulation of hydrological runoff by evapotranspiration and interception of precipitation (e.g., Wigmosta, Vail, & Lettenmaier, 1994) and effects on atmospheric movement and microclimate (Bowler, Buyung-Ali, Knight, & Pullin, 2010; Davies-Colley et al., 2000). These services are pivotal in the performance ofagricultural food systems, global climate and greenhouse gas regulation and management of thermal phenomena and comfort in the expanding urban environments where buildings and man-made objects often prevail in the vertical structure. 3-D complexity also affects biodiversity via vertical stratification and differential tolerance of plant species to light limitations (Barnes et al., 1997; Reich, 2012; Pastor & Bockheim, 1984). Such stratification, in turn, determines the diversity and distribution of canopy-dwelling insects (Schulze, Linsenmair, & Fiedler, 2001; Su & Woods, 2001), birds (Bakermans, Rodewald, & Vitz, 2012; Jones, Arcese, Sharma, & Coops, 2013; Keller, Richmond, & Smith, 2003; Rodewald & Smith, 1998) and mammals (Vieira & Monteiro, 2003). Such effects have been found not only in structurally complex forests, but also in grasslands (Sietman, Fothergill, & Finck, 1994) and wetlands (Bias & Morrison, 2006), highlighting general importance of 3-D structure for ecological food chains, grazing, pollination and other critical ES.

Visual quality is strongly related to 3-D complexity in both natural and human-dominated regions. For instance, adaptations of plant species to different light environments include aesthetically relevant traits such as shape, form, growth habit, color and geometry of leaves (Barnes et al., 1997; Reich, 2012). Complex, com-positionally diverse canopies may promote various visual stimuli relevant to scenic qualities including variable colors, shadows and dappled light, canopy layers, and heterogeneity of building forms (Aminzadeh& Ghorashi, 2007; Blicharska& Mikusinski, 2014; Chen & Xu, 2016). Studies of aesthetic preference in woody landscapes reported several structural properties as contributors to visual quality: relative amount of tree and bush forms (Polat & Akay, 2015); presence of multiple visually distinct vertical canopy layers and tree heights (Aminzadeh & Ghorashi, 2007; Brown & Daniel, 1986; Chen & Xu, 2016; Hunziker, 1995); evidence of groupings and structure in vegetation arrangement in contrast to "unstructured openness" (Brown & Daniel, 1986; Herzog & Barnes, 1999; Hull & Buhyoff, 1986; Natori & Chenoweth, 2008; Pinto-Correia, Barroso, Surova, & Menezes, 2011), though in some cases presence of dense understory was less desirable (Palmer & Sena, 1993; Ulrich, 1986). Reliance on visual, subjective criteria to characterize the degree of 3-D complexity has somewhat limited the interpre-

tation of its significance for aesthetic quality; however, this can be remedied by the use of quantitative indicators from ecosystem studies. For example, canopy LAI was used to develop the metric of green space quality representing environmental, aesthetic and recreational benefits of urban vegetation (Ong, 2003). Similarly, recent analyses of visual preferences in a Chinese urban park (Chen &Xu, 2016; Chen et al., 2015) derived a number of objective and reproducible metrics of tree and building height heterogeneity from lidar remote sensing data.

3.2. Temporal heterogeneity

3.2.1. Short-term heterogeneity

Short-term heterogeneity refers to seasonal and inter-annual cyclic phenomena, or phenological variation, which strongly depends on climate and geographic setting as well as the interactions among biotic and abiotic seasonal factors. For instance, fall changes in leaf coloration of deciduous woody plant species are predominant in climates where deciduousness is a beneficial ecological strategy. Seasonality may be also manifested in the nonuniform hydrological regimes supporting ephemeral aquatic and wetland ecosystems, such as vernal pools with high aesthetic quality at the peak abundance of heterogeneous vegetation (Jansujwicz, Calhoun, Leahy, & Lilieholm, 2013). Phenological variation can be detected by repeated landscape observations both as either basic field surveys or quantitative analyses of changes in solar radiation, sky-view factor or vegetation greenness from the time series of ground or satellite-based remote sensing images (e.g., Sonnentag, Hufkens, Teshera-Sterne, & Richardson, 2012).

Phenological variation has major effects on various ES; for example, diverse seasonal schedules of crops within agricultural landscapes affect insect pollinators, pests and predators and thus ecological regulation of food production (Vasseur et al., 2013). Because physiological and reproductive cycles of plants are often triggered by climatic variation, their shifts provide useful early warning signals of global climate change (Cleland, Chuine, Menzel, Mooney, & Schwartz, 2007). Plant flowering, fruiting and leaf changes are critical to many provisioning and regulating services associated with biomass production, while color and texture effects accompanying these cycles become important in landscape management and design for aesthetic quality (Junge et al., 2015; Palmer & Sena, 1993; Schuepbach et al., 2016). In fact, many visual effects resulting from seasonal phenomena are strongly grounded in underlying ecological feedbacks; for instance, differential sea-sonality in forest understory wildflowers was shown to reflect their unique adaptations to solar radiation changes at different stages of canopy leaf coverage (Rothstein & Zak, 2001).

The importance of seasonal variation has been acknowledged in studies of visual quality, although not as often as spatial heterogeneity. The searches for robust indicators of visual quality emphasized ephemera as seasonal phenomena integral to scenic beauty (Fry et al., 2009; Martin et al., 2016; Tveit et al., 2006). Kuiper (2000) listed presence of seasons and growth cycles in the organic farmlands as one of the criteria for the quality of the cultural environment. In the study of agrarian landscapes in Switzerland, Schuepbach et al. (2016) noted the importance of seasonality even in landscapes than may seem monotone at a given season, but also acknowledged the challenges to interpret such more complex definitions of landscape properties. Another study in a similar setting (Junge et al., 2015) found that seasonal landscape diversity increased aesthetic quality in addition to species and spatial diversity. Forest scenic value and relative strength of its indicators was similarly reported to vary with seasons (Palmer & Sena, 1993). Sea-sonality in physical factors, such as weather events (snow, fog) or changing reflective patterns of water bodies may also strongly contribute to such scenic displays (Tveit et al., 2006). Even seasonal

cycles in animal activity may contribute to aesthetic management, such as colorful plant-pollinator gardens promoting additional ES such as rainwater regulation and education opportunities (Betts, 2015; Wojcik, Frankie, Thorp, & Hernandez, 2008) and ecosystems attractive due to their support of massive bird migrations (Frederick et al., 2009; Steven, Morrison, & Castley, 2015). Thus, seasonality represents a particularly important aspect of visual quality and a potential pathway towards landscape multi-functionality, yet it needs to be better understood to ensure the stability and predictability of the desired outcomes for aesthetic and ES objectives.

3.2.2. Long-term heterogeneity

Long-term heterogeneity here refers to the dynamics of landscape composition over decadal scales including succession of vegetation, physical changes in man-made structures and broader-scale land cover and land use transitions (Kalivoda et al., 2014; Nassauer, 1986; Parrot & Meyer, 2014; Sowiinska-Swierkosz & Chmielewski, 2016). Such processes may be difficult to embrace with narrow time frames of management decisions, yet, they are critical for envisioning future trajectories and maintenance of a given design or planning effort. This has been recognized in both ecological and landscape aesthetic literature; instance, Hull and Buhyoff (1986) emphasized that forest management occurs in non-static ecosystems and acknowledged the need to address the "dynamic nature of scenic beauty" in management decisions. Assessing such dynamics ideally requires long-term monitoring of landscape properties and vegetation using, e.g., permanent field plots or transects or time series of landscape-level maps or remote sensing data (Barnes et al., 1997; Parrot & Meyer, 2014). However, such information is not always available for a given location, and long-term heterogeneity may be instead represented by snapshot characteristics such as age and size diversity of vegetation (Deng et al., 2014; Ode et al., 2009) and diversity of architectural styles and age among man-made structures in human-dominated landscapes (Angileri & Toccolini, 1993; Kalivoda et al., 2014).

Ecological succession is an especially important long-term determinant of changes in various ES and land cover/land use. It is typically initiated by the availability of abiotic physical substrates (primary succession) or post-disturbance clearing (secondary succession) that gradually become colonized and modified by microorganisms, vegetation and other biota (Barnes et al., 1997; McBride & Strahan, 1984). Succession affects heterogeneity both vertically, due to the development of canopy structure, and horizontally, due to colonization of disturbance-created gaps by the pioneer species in forests and formation of vegetation zones with different flood tolerance in aquatic systems transforming into wetlands. Although biodiversity, vegetation biomass and associated ES tend to increase towards the later successional stages, early phases of plant colonization may also show temporarily high species diversity prior to their "sorting out" by competition. In the environments with frequent disturbance, this heterogeneous phase becomes part of their ecological identity and contributes to scenic beauty, such as in sand dunes (Jenks, 2007; Povilanskas, Baziuke, Ducinskas, & Urbis, 2016; Pries, Miller, & Branch, 2008; Zhang, Qu, Niu, Jing, & An, 2013) and fluvial ecosystems (Ellsworth, 1982; Ito et al., 2006; McBride & Strahan, 1984; Ward et al., 1999). In turn, land cover changes may be driven by both succession and human modifications of landscapes (Turner et al., 2013; Parrot & Meyer, 2014). While shifting land use priorities are often aimed at enhancing specific ES, such as agricultural productivity via expanding cropland, they may be accompanied by losses of pre-existing ES, especially biodiversity and regulating or supporting ecosystem functions (Cardinale et al., 2012; Tscharntke et al., 2012).

In landscape aesthetic studies, long-term variability has received less attention than other dimensions of heterogeneity; nevertheless, several contributors to visual quality were discussed.

In wooded landscapes, scenic value or preferences were reported to benefit from successional characteristics such as higher overall tree age (Gundersen & Frivold, 2008), diversity of tree age classes (USDA, 1995), the "level of succession" representing the degree of agricultural conversion into natural ecosystems (Ode et al., 2009) and vertical heterogeneity of tree heights which often results from a particular successional stage (Aminzadeh & Ghorashi, 2007; Chen et al., 2015; Ozkan, 2014). Presence of old trees in particular has been regarded as beneficial for biodiversity (Lindenmayer et al., 2014; Orlowski & Nowak, 2007), visual and cultural ES (Blicharska & Mikusinski, 2014; Gallagher, 1977; Gundersen & Frivold, 2008) and residential property market value (Dombrow, Rodriguez, & Sirmans, 2000). In human-dominated regions, however, elements of long-term heterogeneity such as land cover and land use changes have more controversial and context-specific effects on visual quality. Rural landscapes with diverse composition and presence of historical buildings have often been regarded as highly scenic (Angileri&Toccolini, 1993; Nassauer, 1986; Ulrich, 1986); however, urbanization and intensification of human land use may produce the opposite effect. For instance, Palmer (2004) found a decrease in scenic quality following the 20-year landscape shift from predominantly forested to predominantly developed in a Massachusetts, USA region. Similarly, indicators of landscape disharmony by Sowrnska-Swierkosz (2016) were based on presence and visual impact of non-natural, anthropogenic objects and land cover forms. Together, this evidence shows that both the nature of long-term landscape dynamics and their imprint in the static composition and structure are important players in visual quality and ecological functionality and thus require a dynamic perspective in planning and management scenarios (Parrott & Meyer, 2012).

4. Heterogeneity, visual quality and resilience

Ecological resilience and resistance represent desirable characteristics of ES and increasingly important planning and management objectives (Oliver et al., 2015; Parrott & Meyer, 2012) that in the face of massive environmental hazards may trigger decisions favoring ecological functionality over aesthetic attractiveness. Thus, more profound understanding of the links between resilient capacities of different ES and visual quality is critical to reconcile potentially conflicting landscape objectives and enable ecological multi-functionality in planning strategies. Resilience has been defined in the earlier literature as the capacity of ecological systems and functions to recover from perturbations (Holling, 1973); it is increasingly discussed as ecosystem adaptive capacity and the ability to return to and maintain pre-disturbance states (Parrott & Meyer, 2012; Pickett, Cadenasso, & Grove, 2004). Resistance, or robustness, denotes the capacity of systems to withstand disturbance without changing essential functions (D'Antonio & Thomsen, 2004; Elton, 1958). Discussions of resilience and resistance in the context of global environmental change increasingly include socio-ecological feedbacks and governance, in addition to their underlying ecological mechanisms (Benson & Garmestani, 2011; Pickett et al., 2004).

The contribution of environmental heterogeneity to ecological resilience and robustness has been increasingly reported in various global regions, including tropical forests (Levine et al., 2016; Virah-Sawmy, Gillson, & Willis, 2009) montane ecosystems (Buma & Wessman, 2012), grasslands (Janssen, Anderies, Smith, & Walker, 2002; Tilman et al., 1996) and wetlands (Jiang et al., 2012), suggesting several common roles of heterogeneity, also relevant to visual quality. First, greater compositional variety may result in greater diversity of ecological responses to disturbance, reducing

the change of equally detrimental effects throughout the landscape (Oliver et al., 2015). Second, presence of multiple species with similar ecological performance may imply the useful redundancy in ecological function and services that would be less likely to disappear completely following a disturbance (Cardinale et al., 2006, 2012). Finally, greater diversity and complementarity of species, functions and ecosystems within heterogeneous regions may facilitate persistence of individual species by providing resources and microclimatic or habitat refugia (Godfree et al., 2011; Kindvall, 1996; Oliver et al., 2015; Piha, Luoto, Piha, & Merila, 2007).

These ideas have strong implications for ecosystem management and design, suggesting the potential to promote visual quality using resilience- and resistance-supporting ecological principles. Such a potential becomes evident, for example, in the case of the southwestern North American forests, where historically natural fire regimes have been important precursors of resilience and ecosystem health (Boisramei et al., 2016; Dunbar-Irwin & Safford, 2016; Rivera-Huerta, Safford, & Miller, 2016). Forests managed based on the policies of fire prevention and suppression tend to accumulate large amounts of fuels and homogenize composi-tionally and structurally, which increases the risk of fires with catastrophic and large-footprint outcomes. In contrasts, regions where management (or lack of thereof) allow for less intensive fires to recur develop heterogeneous ecosystem mosaics of different successional stages and thus greater diversity of potential responses to new fire events (Boisramei et al., 2016; Collins et al., 2015; Dunbar-Irwin and Safford, 2016; Rivera-Huerta et al., 2016). Similar mechanisms have been reported in grasslands where disturbance may encourage both compositional and functional diversity contributing to longer-term stability (e.g., Bowler et al., 2010; Larkin et al., 2015; Palmquist, Peet, & Weakley, 2014) and facilitation of pollinators and associated ES (Ponisio, Wilkin et al., 2016). Diversified agriculture systems with greater floral diversity promote ecological resilience by attracting a greater diversity of pollinators and pest-controlling agents and enhancing other ES (Kremen & Miles, 2012; Morandin & Kremen, 2013; Ponisio, M'Gonigle et al., 2016). Recent evidence from coastal marsh studies also corroborates the potential importance of their spatial heterogeneity and biological diversity in response to the storm disturbance with critical implications for coastal resilience and sea level rise risks (Ford, Garbutt, Ladd, Malarkey, & Skov, 2016; Jiang et al., 2012).

All these examples highlight the importance of reciprocal ecological feedbacks among heterogeneity, disturbance and resilience. Such feedbacks may allow to not only promote versatile ES, but also to enhance visual quality and aesthetic value while pursuing resilience-oriented goals. For instance, heterogeneous forest mosaics shaped by recurring non-catastrophic fires are often diverse in ecosystem composition, successional stages, tree age groups and structure (Collins et al., 2015) which satisfy multiple criteria of forest scenic beauty (Bureau of Land Management, 1984; Forestry Commission, 1994; USDA, 1995). A trail through such a landscape would encounter a variety of habitats, vegetation forms and wildlife observation opportunities and thus may provide a diverse aesthetic and educational experience. Similarly, greater spatial and biological diversity of coastal marshes important for protection against flooding may promote richer wildlife habitats, greater visual diversity of vegetation types and colors and scenic complexity contributing to aesthetic quality and recreational opportunities. Clearly, however, more research is needed to better understand specific ecological contributions of heterogeneity to resistance and resilience in different regions as well as the relevance and importance of resilient heterogeneous systems to the visual experience and landscape perception by different social groups.

5. Limits to heterogeneity

The evidence discussed above places heterogeneity as a useful target for achieving multi-faceted ecological and human-centric benefits in management, planning and design. It is, however, important to recognize that heterogeneity is not a universal objective, nor can it always be a central focus in landscape decisions. First, ecological implications of heterogeneity vary and may sometimes represent risk factors in addition to societal benefits. Heterogeneity in land cover, topography or vegetation may signal higher diversity of ecosystems and ES in less developed or rural areas (Dramstad et al., 2001; Howley, Donoghue, & Hynes, 2012; Junge et al., 2015; Nassauer, 1986; Rogge et al., 2007), but also losses of ES and visual quality in highly fragmented and urbanized human-modified landscapes (Arriaza, Canas-Ortega, Canas-Madueno, & Ruiz-Aviles, 2004; Palmer, 2004; Sahraoui et al., 2016). High spatio-temporal complexity of disturbance-prone ecosystems often signals the recurrence of fire, flooding or extreme weather that shape important functions and ES of these systems yet may be hazardous to humans (Boisramei et al., 2016; Collins et al., 2015; McBride & Strahan, 1984). For instance, restoring and maintaining ES of aquatic systems may require high degree of hydrological variation with inevitable flood risks to adjacent land uses (Bond et al., 2014; Kondolf, 2011). Such issues pose major planning challenges for reconciling multiple stakeholders' goals and concerns in land use decisions and for communicating controversial yet important measures to the public (Acreman et al., 2014; Bond et al., 2014; Gobster et al., 2007).

Second, low variability or homogeneity may be desirable in their own right for effects such as the sense of openness favorably affecting locomotion and visual harmony (Appleton, 1975; Hull & Buhyoff, 1986; Kaplan & Kaplan, 1989; Sahraoui et al., 2016; Ulrich, 1986). A survey of landscape preferences in eastern Germany (Frank et al., 2013) also found that less complex areas were sometimes preferred due to their "naturalness". The overall evidence from previous studies, however, is contrasting - while some analyses reported preference for moderate to high levels of complexity (Kaplan & Kaplan, 1989; Lindemann-Matthies et al., 2010; Ode et al., 2009; Ulrich, 1986), a meta-analysis by Stamps (2004) found a wide range of effects between complexity and preference which precluded a straightforward interpretation. This latter study raised a point that entropy associated with complexity likely limits the information processing by humans, which may explain the lack of preference for excessive heterogeneity. Consistent with this notion, some of the scenic beauty management criteria caution against "too much variety" and "excessive diversity" which may lead to a "restless confusion" (Forestry Commission, 1994).

Together, these issues raise an important question of how much heterogeneity is beneficial and sufficient to achieve specific goals for visual quality and other ES. A study of visual quality in Mediterranean-climate landscapes (de la Fuente de Val et al., 2006) discussed intermediate complexity as a likely state of environment corresponding to the peak in the range of resources, which should be preferable to simpler or more complex settings. This idea is consistent with the assertion that added complexity in landscape structure does not automatically increase scenic or ecological benefits, particularly when driven by intensive anthropogenic development (Palmer, 2004). Similarly, the review of preferences for the Scandinavian forested landscapes (Gundersen & Frivold, 2008) noted that the public favored both heterogeneous stands with different-sized tree mixtures and the areas providing sense of accessibility and view. Such a combination also implies an intermediate level of complexity which concurs with the prospect-refuge theory on the evolutionary basis for aesthetic preference (Appleton, 1975).

To determine potentially beneficial levels of complexity in practical decisions, several strategies follow from previous research. First, the effect of heterogeneity on visual preference or other ES may be easier to interpret by focusing on specific ecological or perception-related criteria and metrics, rather than complexity as a whole (Acar & Sakici, 2008; de la Fuente de Val et al., 2006; Hands & Brown, 2002; Ode et al., 2010). Second, important guidance may be derived from the relationship of complexity with other aspects of scenic quality, particularly, coherence, orderliness, continuity, which enhance the sense of organization, structure and interpretability of the landscape (Abkar et al., 2014; Bureau of Land Management, 1984; Gimblett, 1990; Herzog & Barnes, 1999; Kuiper, 1998, 2000; Ulrich, 1986; USDA, 1995). Finally, measures to promote heterogeneity can be guided by ES-oriented goals that expand beyond visual quality yet automatically require the diversity of species, ecological functions and thus colors, forms, configurations and other scenic qualities (Junge et al., 2015; Kremen & Miles, 2012; Lindemann-Matthies et al., 2010).

6. Synthesis and future research needs

Combined evidence from ecological and landscape aesthetic studies reveals a substantial overlap in the meaning and significance of heterogeneity and a number of strategies by which it could be used to bridge together ES and visual quality objectives. Many determinants of visual quality arise from complex interactions among physical environment, biotic factors and human decisions affecting the amount, diversity and composition of landscape elements (de la Fuente de Val et al., 2006; Forman, 1995; Junge et al., 2015; Rogge et al., 2007; Turner et al., 2001; USDA, 1995). For instance, diversity of land cover and vegetation promotes versatility of resources, habitats and functional mechanisms for nutrient and energy cycling while also producing visual variety of colors, textures and perceivable landscape elements. Environmental heterogeneity frequently underlies higher biological diversity recognized as a direct visual quality facilitator in both subjective preferences and objective assessments. In plant canopies, vertical variety in tree height and successional status appreciated for visual richness and layering translates into ecosystem efficiency at capturing solar energy, nutrients and accumulating biomass, as well as greater diversity of canopy-based habitats. Similarly, vertical heterogeneity of urban ecosystems affects both their aesthetic appeal and regulation of thermal environment via solar energy transfer, heat balance and effects on atmospheric movement. Finally, ecosystem mechanisms behind stability and resilience to stressors are often associated with spatio-temporal diversity and thus higher scenic quality promise due to richer compositional mosaics, textures, edges & species pools (Bureau of Land Management, 1984; Forestry Commission, 1994; USDA, 1995).

The potential of heterogeneity to link multiple ES objectives including (rather than ignoring) aesthetic priorities also concurs with evolutionary theories on human landscape perception, such as preference for complexity as the proxy of natural resource richness, availability of prospect and refuge (Appleton, 1975; de la Fuente de Val et al., 2006; Kaplan and Kaplan, 1989; Orians, 1986), and with the premise that awareness of environmental benefits contributing to landscape sustainability may promote favorable aesthetic perception (Carlson, 2001). Direct relevance of ecological heterogeneity to critical ES including resilience (Junge et al., 2015; Kremen & Miles, 2012; Lindemann-Matthies et al., 2010; Oliver et al., 2015; Stein et al., 2014) further advocates for its use as a management and planning tool towards multi-functional and visually appealing working landscapes where active human land uses and activities are aligned with ecological and conservation priorities. Together, these effects underscore a rich potential of

using visual quality to communicate the importance of other ES and management strategies to broader public (Frank et al., 2013; Junge et al., 2015; Lim, Innes, & Meitner, 2015; Zoderer, Tasser, Erb, Stanghellini, & Tappeiner, 2016).

However, breadth of various interpretations in both ecological (Stein & Kreft, 2015) and landscape aesthetic literature (Table 1) remains a challenge for strategic application of heterogeneity in management, planning and design. Broad and vague interpretations of complexity likely contribute to previously discussed disjunctures and controversies among ecological management, conservation and design priorities (Daniel, 2001; Gobster et al., 2007; Lindemann-Matthies et al., 2010; Mozingo, 1997). In part, this challenge stems from the differences in research methods and metrics among these fields (Fry et al., 2009; Kaplan & Kaplan, 1989; Stamps, 2004; Tveit et al., 2006). Yet, the potential for a bridge is strongly suggested by the links between ecological and aesthetic landscape properties (Fry et al., 2009; Tveit et al., 2006) and evidence for human recognition of ES beyond the aesthetic appeal alone (Dramstad et al., 2006; Gundersen & Frivold, 2008; Junge et al., 2015; Lindemann-Matthies et al., 2010).

Recent research further suggests several strategies by which landscape heterogeneity may be used to strengthen the bridge between aesthetic goals and other ES. First, useful guidance can be gained from applied ecological studies of specific mechanisms by which heterogeneity affects ecosystem services and visual quality in both natural and human-dominated landscapes (Cardinale et al., 2006; Kremen & Miles, 2012; Stein et al., 2014). Such knowledge is especially critical to inform and refine the objective criteria of visual quality, which have been earlier criticized for the lack of the theoretical framework (Lothian, 1999) and do not always explicitly articulate their ecological underpinnings and other goals beyond scenic beauty (Forestry Commission, 1994; USDA, 1995).

Second, promoting and evaluating heterogeneity in practice can greatly benefit from the use of quantitative, objective metrics relevant to both ecological and aesthetic fields (Frank et al., 2013; Pardo-Garcia & Merida-Rodriguez, 2017). Such metrics can help to divide broader complexity into specific measurable components that may be more easily compared among different landscapes, research problems or management scenarios. The need for such objective criteria has been increasingly recognized (Ode et al., 2010), and a number of them have been already proposed as indicators of spatial heterogeneity (Dramstad et al., 2006; Herbst et al., 2009; Martin et al., 2016; Ode & Miller, 2011), vertical and 3-D structure (Chen &Xu, 2016; Chen et al., 2015) or even economic valuation criteria (de Groot et al., 2010; Hasund et al., 2011; Van Zanten et al., 2016). However, some important dimensions of complexity are still lacking such indicators, e.g., seasonal heterogeneity criteria (Table A1, Appendix A). Finally, it should be recognized that visual quality is also deeply linked with cultural dimensions landscape perception and use which ultimately affect specific values, role and appreciation of heterogeneity (Allan et al., 2015; Dramstad et al.,

2006; Junge et al., 2015; Kienast et al., 2015). Although such cultural underpinnings of heterogeneity and landscape perception are outside of this review's scope, they represent an important direction for the future research.

Addressing the remaining gaps in the understanding of landscape complexity's potential may greatly benefit from initiatives that directly promote environmental and visual heterogeneity and thus offer rich experimental ground for testing multi-functional design and management ideas. For example, diversified farming systems are actively explored as opportunities to sustain biodiversity together with critical ES related to crop production and food security (Hodbod et al., 2016; Kremen & Miles, 2012; Pinto-Correia et al., 2011); such systems by definition rely on landscape heterogeneity and presence of non-crop vegetation supporting local biodiversity and agricultural ES. Similarly, restoration and management efforts using disturbance-heterogeneity feedbacks also offer ample opportunities to increase visual quality through complexity. Such efforts may be greatly assisted by cost-effective computer-based tools to test large numbers of scenarios, such as the HERCULES model for urban spatial heterogeneity (Cadenasso, Pickett, & Schwarz, 2007) or the RASCAL model simulating spatiotemporal complexityin shallow aquatic ecosystems (Larsen & Harvey, 2010). Such initiatives provide rich foundation not only for advancing the research on ecological importance of heterogeneity, but also for testing its compatibility with aesthetic criteria (Pardo-Garcia & Merida-Rodriguez, 2017).

In summary, the diverse evidence from ecological and landscape aesthetic studies suggests that environmental heterogeneity provides a powerful basis for achieving visual quality together with other ES within both natural and human-dominated regions. This potential is especially critical in the face of complex environmental issues which profoundly impact local and regional planning decisions and underscore the paramount importance of landscape multi-functionality and resilient capacity. The appeal of heterogeneity as a landscape objective lies not only in its connections with important ecological benefits and functions, but also in its promise to contribute to visual quality and use the latter for communicating landscape decisions to different public groups. Such a strategy may represent an important step towards the goal of multi-functional working landscapes that satisfy a variety of human priorities, ecological and conservation objectives while allowing for rich, beneficial and holistic aesthetic experience.

Acknowledgment

I thank three anonymous reviewers of this manuscript whose suggestions have greatly helped to improve the focus, organization and discussion of the review.

Appendix A.

Table A1

Examples of specific quantitative metrics to assess landscape complexity in the studies of visual quality and preference.

Metric

Definition

Example of visual quality & preference studies using as a metric

Diversity metrics used for accounting of spatial or ecological entities

Simple diversity: richness S evenness

Shannon diversity index

Shannon (Pielou) evenness index

Simpson diversity index (multiple

versions exist) Simpson evenness index

Margalef index of species diversity

Number & diversity of man-made elements

Complexity of patch shape and geometry Shape indices

Perimeter to area ratio Length of the overall edge or specific edge types or edge density Number of patches or patch density

Spatial variation in patch size and/or its distribution

Fractal dimension

Number (richness) and/or relative abundance (evenness) of cover types, ecological categories, species, or functional groups, sometimes expressed as density (count per unit landscape area)

Negative sum of the proportions of each species or cover types relative to the total pool multiplied by their respective natural logarithm values

The ratio of Shannon diversity value to the natural logarithm of the total number of species or cover types

One minus or inverse of the sum of squared ratios of each species' or cover type's patch count to their total count The ratio of the Simpson diversity value to the total number of species or cover types

The ratio oftotal number ofspecies or cover types minus one to the natural log of the total number of individuals or patches

An ordinal score ranging from 1 (none) to 4 (3 or more)

Various indices of patch shape complexity as deviation from an standard figure such as circle or square

The ratio of patch perimeter to its area

Total length of the edge features, sometimes expressed per

unit ground area (edge density)

Total number of patches either as a count or as ratio to the extent of the study area (patch density)

Simple or area-weighted metrics representing mean, standard deviation, range of patch areas and/or their statistical distributions

Logarithm of patch perimeter multiplied by 2 and divided by the logarithm of patch area; sometimes as simple or area-weighted mean, range or standard deviation across multiple units

Proximity & aggregation as spatial pattern contributing to heterogeneity

Contagion Aggregation index

Interspersion

Metrics of vertical&3-D structure Plant (tree) stand density

Plant (tree) grouping

Age or size class diversity

Overstory crown cover or crown

density Leaf area index Sky-view factor Area weighted mean height deviation within individual trees or buildings or within tree patches

Height deviation between trees or

buildings Height range of trees

Mean ratio of building height to mean height of its surrounding trees

Topographic complexity Heterogeneity index

Topographic variety

The degree of adjacency among cells of the same cover type, high values indicate high aggregation Level of aggregation weighted by the proportions of cover types in the landscape

The degree to which different patch types are equally adjacent; high values indicate high interspersion (^uniform adjacency)

Number of plants or trees per unit area or within a specific stratum (e.g., understory)

Ordinal score variables ranging from little or no grouping to high grouping with interlocking crowns (in trees) Number of trees in different age or size classes per unit ground area

Percent crown canopy cover for a given area or vegetation type

One-sided projected canopy leaf area per unit ground area The degree of sky obstruction by landscape elements Standard deviations of the lidar-based elevation pixel values within individual tree or building objects or within tree patches

Standard deviations of the heights of all trees or buildings within the study area

Difference between maximum and minimum heights divided by maximum height

The ratio of a building height to the mean height of its surrounding trees

The proportion of point pairs from the landscape grid that fall on different land types

Standard deviation of elevation values in the view (subdivided by foreground, midground and background)

Fjellstad et al., 2001; Kuiper, 2000; Palmer, 2004

Dramstad et al., 2001, 2006; Fjellstad et al., 2001; Frank et al., 2013; Germino et al., 2001; Herbst et al., 2009; Ode et al., 2010; Sahraoui et al., 2016; Ode, 2011; Sahraoui et al., 2016; Schirpke et al. 2013; Schuepbach et al., 2016 Deng et al., 2014; Ode et al., 2010; Ode 2011, Germino et al., 2001; Palmer, 2004; Sahraoui et al., 2016; Schirpke et al. 2013

de la Fuente de Val et al., 2006; Germino et al., 2001; Schirpke et al. 2013; Schuepbach et al., 2016 de la Fuente de Val et al., 2006; Schirpke et al. 2013

Deng et al., 2014

Acar et al., 2006; Polat S Akay, 2015

Frank et al., 2013; Ode et al., 2009, 2010; Ode, 2011; Palmer, 2004; Schirpke et al. 2013; Weinstoerffer& Girardin,2000

Martin et al., 2016; Schirpke et al. 2013 Dramstad et al., 2001, 2006; Germino et al., 2001; Herbst et al., 2009; Ode et al., 2010; Schirpke et al., 2013; Ode, 2011; Schirpke etal. 2013

de la Fuente de Val et al., 2006; Dramstad et al., 2006; Ode et al., 2009, 2010; Palmer, 2004; Ode, 2011; Palmer, 2004; Schirpke et al. 2013 Ode 2011; Schirpke et al., 2013

Ode et al., 2010; de la Fuente de Val et al., 2006; Schirpke et al., 2013

de la Fuente de Val et al., 2006; Ode et al., 2010; Ode, 2011; Sahraoui et al., 2016; Schirpke et al., 2013 Ode et al., 2010; Ode & Miller, 2011

de la Fuente de Val et al., 2006; Sahraoui et al., 2016

Hull & Buhyoff, 1986; Deng et al., 2014; Natori & Chenoweth, 2008; Palmer & Sena, 1993; Ribe, 2009 Brown and Daniel, 1986; Polat & Akay, 2015

Brown and Daniel, 1986

Brown & Daniel, 1986; Deng et al., 2014; Palmer & Sena, 1993

Ong, 2003

Pardo-Garcia and Merida-Rodriguez, 2017 Chen and Xu, 2016; Chen et al., 2015

Chen and Xu, 2016; Chen et al., 2015 Chen and Xu, 2016; Chen et al., 2015 Chen and Xu, 2016; Chen et al., 2015

Dramstad et al., 2001, 2006; Fjellstad et al., 2001 Germino et al., 2001

Table A1 (Continued)

Metric

Definition

Example of visual quality & preference studies using as a metric

Spatial variation in the view depth Shape complexity of the viewshed Shape of the skyline

Visual and color heterogeneity Spatial variation in color

Color diversity of natural or man-made elements

Form and color disharmony index

Shape disharmony index

Land type disharmony index

Temporal metrics Landscape ephemera

Shannon diversity index for the numbers of sight lines in different length classes

Ratio of the length of the viewshed contour to the perimeter of a circle with the identical area The ratio of the total length of the horizon to the length of the straight line width of the view

First- and second-order measures of remote sensing image texture calculated as variation in pixel grey level values within image objects or pixel neighborhoods Ordinal score variable ranging from single-color (monochrome) to the categories representing larger number of colors

Mathematical index based on the number of anthropogenic objects disharmonious in form or color A measure of shape disharmony based on the relative extents and fractal dimensions of natural and semi-natural or anthropogenic land cover forms The average proportion of areas developed contrary to natural and local cultural-historical tradition

Proportion of area with seasonally changing land uses within the viewshed area

Sahraoui et al., 2016 Sahraoui et al., 2016 Sahraoui et al., 2016

Ozkan, 2014

Acar et al. 2006; Arriaza et al., 2004; Polat & Akay, 2015

Sowiüska-Swierkosz, 2016 Sowiüska-Swierkosz, 2016

Sowiüska-Swierkosz, 2016 Martinet al., 2016

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