The visual characteristics of landscapes, such as complexity, openness, and naturalness, are known to be linked to people’s perceptions and behaviors [1
]. These characteristics—expressed, e.g., by quantity and variety of visible landcover or by variations in surface elevation—have been previously analyzed from landscape photographs [3
] providing reliable, detailed information about the local survey sites [7
]. The effort to extend the analysis over larger areas has led to GIS approaches that are easy to automate, but may be viewed as less realistic [8
]. For example, viewshed analyses of areas visible from a given vantage point [10
] have been used as a way to link mapping with the visible landscape [9
]. Conceptualized as viewscape modeling, researchers are beginning to characterize visible landscape content, such as land use or surface greenness, to better understand visual connections between people and their surroundings [15
]. Analysis of viewscape composition can be extended by computing the spatial configuration of visible landscapes (e.g., pattern diversity [17
], shape complexity [18
], terrain ruggedness [19
]) using landscape metrics from the field of landscape ecology. Viewscapes based on digital surface models (DSM), rather than previously used digital terrain models (DTMs), include vertical structures, such as buildings and vegetation and are less likely to overestimate viewscape area [16
]. Where horizontal visibility is important, such as in urban settings, they may still incompletely represent the visible area and its associated characteristics (e.g., Reference [20
]). Application of viewscape models to understand the visual characteristics of finer-scale features in urban environments remains largely unexplored. In contrast to more commonly studied biomes, such as pasture and forest, that can include large vistas and fairly homogenous landcover (e.g., References [21
]), urban landscapes involve a variety of view ranges and spatial conditions—shaped through the interaction of granular landforms and heterogeneous built environments.
Limitations of spatial scale exist for both DSM and landcover data that are integral to the realistic estimation of visible content (e.g., number of visible trees and buildings), as well as for the accuracy of landscape metric analysis (reviewed in References [24
]). Publicly available landcover data are often coarse (10–30 m) and do not represent features smaller than their pixel size (e.g., buildings, sidewalks, single trees). Although advanced methods, such as object-based classification or pattern recognition, exist to generate highly detailed landcover data from satellite imagery, incorporation of such data in viewscape models has been rare [11
]. Difficulties in the accessibility of lidar data—the most common DSM data source—have caused most current viewscape models to use low-resolution DSMs or DTMs [18
]. This is in spite of the well-documented influence of spatial data resolution on the accuracy of visibility analysis [9
]. Coarser DSMs tend to overestimate visibility compared to fine-grained lidar DSMs, especially in smaller viewsheds [26
]. However, lidar-sourced DSMs may still struggle to realistically represent non-surface vegetation (Figure 1
). Specifically, raster-based DSMs represent trees as solid protrusions that entirely obscure the under-canopy and through canopy visibility [27
]. This is a major source of error for visibility estimations, particularly within the dense canopy (parks and greenways), or in leaf-off season when visibility through deciduous trees is computed. Several techniques have been proposed to overcome this issue, such as the visual permeability concept that accounts for the probability of viewing a region as determined by the spatial density and position of tree models [29
], or trunk obstruction modeling that replaces the trees with an approximated trunk model [28
]. While improvements in resolution of spatial data and vegetation obstruction modeling have separately shown promise in enhancing the accuracy of visibility analysis, to our knowledge, they have not been used together in a single study to generate a high fidelity viewscape model.
Evaluating the extent to which a viewscape model can predict perceived visual characteristics requires a comparison of the model output with human subjects’ evaluations of landscape, which could be done either in situ or through landscape photography and 3D simulations. Because in situ measurements are often time-consuming, labor-intensive and involve several confounding factors (e.g., changing weather), research has widely resorted to online or desktop surveys using photographs and 3D simulations [18
]. However, the use of digital stimuli is increasingly contested for their representation validity, with the least realism reported for photographs of heterogeneous landscapes and mixed-use urban environments [7
]. Another obstacle for verification of viewscape models is the discrepancy of view coverage between perspective photographs and visibility analysis in GIS. Perspective photographs have a limited field of view (FOV), while viewshed algorithms use 360° line-of-sight algorithms to calculate visibility for the entire horizontal and vertical FOV. Immersive virtual environments (IVEs) that immerse the observer in a virtual environment (VE) can potentially minimize the gap between modeled viewscape and in situ experience of the urban landscape. In contrast to desktop displays where FOVs are limited, immersive displays (CAVE or head-mounted displays, HMD) provide continuous visual feedback linked to the user’s head and body orientation allowing them to freely explore the entire viewshed area. Thanks to the ability of IVEs to elicit a higher sense of immersion [30
], presence [31
], and improved spatial perceptions (e.g., distance, depth) [32
]. IVEs have been widely adopted in geospatial sciences and urban planning applications, such as 3D visualization of open map data [33
], real-time 3D visualization of ecological simulations [34
], and geodesign [35
]. However, to our knowledge, IVE has not been used for human verification of visibility simulations, particularly viewscape modeling.
The purpose of this study is to develop and evaluate a high-resolution approach to measuring and modeling fine-scale viewscape characteristics of mixed-use urban environments through a novel integration of geospatial and perception elicitation techniques using photorealistic IVEs. We use high-resolution DSM and landcover data derived from lidar to account for the fine-grained structure and heterogeneous patterns of urban environments, and we improve the vegetation visibility of the DSM using trunk obstruction modeling. With these improved spatial data, we compute viewscape composition and configuration using automated GIS procedures. We uniquely evaluate the realism of the resultant viewscape model by quantifying its capacity to predict perceived visual characteristics. For this, we conduct a perception survey using IVE images captured from a set of locations across the study area. Then we compare the metrics of viewscape composition and configuration derived from the viewscape model with human subject evaluations of IVEs.
We specifically focus on three visual characteristics, namely, visual access, complexity, and naturalness that have been widely used to objectively measure visual landscape quality and have shown to be strongly linked with human psychological responses to environments [1
]. By bridging the gap between objective and subjective analysis of visual characteristics, our high-resolution viewscape modeling allows landscape designers and planners to realistically simulate aesthetic and restorative qualities of a viewscape in a spatially explicit manner.
The purpose of this study was to develop and evaluate a high-resolution approach to modeling fine-scale viewscape characteristics of mixed-use urban environments. We utilized high-resolution spatial data and improved vegetation modeling method to develop a viewscape model accounting for granularity and heterogeneity of mixed-use urban environments. Using human subject’s evaluations of IVEs taken from the study area, we assessed the capacity of the viewscape model to predict three perceived visual characteristics, namely, visual access, naturalness, and complexity. Our results show that with our proposed approach, viewscape models can reliably capture the visual characteristics of the urban park environments. Findings also confirm the relationships between landscape configuration and composition, and examined perceptions.
4.1. Predicting Perceived Visual Characteristics
Statistically, our viewscape models for perceived visual access, naturalness, and complexity provide results with good explanatory power. Regression models explain almost 65% of the variance in perceptions at best (naturalness, visual access) and as much as 45% at worst (complexity). These results are comparable to those in a similar analysis by Schirpke et al. [14
] and Sahraoui et al. [18
] that estimated perceptions of mountain regions and urban-rural fringes, respectively using viewscapes.
Regarding the metrics selected for the visual access model, the analysis shows that extent (viewshed size) and depth had a strong positive impact on the perceived visual access. This finding is in line with extant studies indicating that the observer’s distance between the obscuring elements (depth) and the amount of visible space (extent) have a strong influence on perceived visual access [41
]. Depth variation—the spatial variation of the view depth [18
]—also showed a positive impact on perceived access. This indicator is analogous with “number of perceptual rooms,” which is one of the main determinants of visual access, as found by Tveit [57
]. An interesting finding concerns the strong negative role of buildings and the positive role of deciduous trees in perceived visual accessibility, emphasizing the importance of permeability (porosity) of the obscuring elements. Indeed, in leaf-off season deciduous forests allow for more visibility through the branches compared to evergreen and mixed forests. Similarly, horizontal surfaces occupy a smaller proportion of the visible landscape, unlike buildings whose vertical development leads to significant visual salience.
For perceived naturalness, we found a positive role played by green spaces and natural groundcover, such as grasslands and herbaceous landcover, which is consistent with what is generally reported in the literature. In contrast to previous studies that combined all forest typologies as a single forest landcover, incorporation of fine-grained landcover enabled our model to discriminate between forest types and revealed perception differences among them. Mixed forests consisting of more than two stand types and abundantly covered by mosses and lichens, were perceived as more natural than the deciduous and evergreen specimens, which parallels previous studies suggesting that less maintained and varied representation of vegetation positively impact perceived naturalness [2
]. Also, as expected, human-made elements, such as residential or administrative buildings, had a negative effect on naturalness judgments. We also found a strong impact of Relief, indicating that viewscapes with a higher vertical variation or rugged terrains were perceived as more natural. Although several studies have confirmed positive contribution of Relief to aesthetic preferences, there is no prior evidence regarding relationships with perceived naturalness as a basis for comparison.
Contrary to our expectations with regard to the literature on visual landscape characteristics [2
], shape index, and number of visible patches had a positive association with perceived naturalness. It is generally suggested that a more varied patch shape may be perceived as more natural compared to a straight edge [40
], and landscapes consisting of small, fragmented patches may be interpreted as less natural, compared to those with one large woodland patch. We speculate that in case of metrics, such as shape and edge index, viewsheds introduce geometry artifacts. In other words, shape index (SI) may be more indicative of the shape irregularity of the viewshed than that of landscape patches seen in the view, and respondents may not necessarily treat viewshed boundaries as being relevant to naturalness. This is further exacerbated by the fragmented areas and “holes” produced by viewshed analysis.
Turning to the perceived complexity model, landcover heterogeneity (SDI), edge density (ED) and number of visible patches ( Nump) had the strongest impact, confirming what is generally reported in environmental psychology oriented work suggesting that number (richness) and/or diversity (arrangement) of the visible landscape have a strong influence on perceived complexity and aesthetic preferences [59
]. Previous studies using landscape metrics to compute complexity, generally assumed landscape as a planimetric surface and focused on horizontal (landcover) heterogeneity. We dissected the viewscape into the surface and above surface elements to compute two vertical heterogeneity factors, relief and skyline variability—features that play a key role in human perception and preferences. Our results indicate a positive impact of relief on complexity, suggesting that participants perceived rolling terrains more complex than flat ones. Skyline variability was omitted from all three visual characteristic models, due to strong collinearity with relief. This variable deserves further exploration as it reveals the complexity of horizon, such as its smoothness and the number of times the horizon is broken, which are shown to impact perceived complexity.
The complexity of the view, as represented by elements distributed in a panoramic image, may not be readily transferable to the spatial distribution of these elements across a landscape’s surface, even less so as represented in 2D spatial data [8
]. Information, such as the shape and color of buildings, presence of cars and people, and even the fractal dimension of tree branches can influence the perceived complexity of images—but are not captured in spatial data. To supplement this study, it would be instructive to further test the validity of viewscape models by using image-based analysis complexity, such as attention-based entropy measures (e.g., Reference [61
]), object counts (e.g., References [62
]), image compression algorithms [64
], landscape metrics analysis [60
], and fractal dimension [60
]. We should also note that a single survey item for complexity might have not reliably captured the perception of complexity. Complexity is an intricate and multi-faceted notion, and different participants may have interpreted it differently [67
]. Recommendation for future analysis include using multiple-item survey, or if not applicable, briefing participants with a distinct definition of complexity to acquire a more homogenous baseline understanding of the concept.
4.2. Methodological Considerations for Modeling Viewscapes
We used tree delineation and trunk modeling to leverage vegetation structural data (height and stem position) derived from lidar as obstructions in the visibility analysis. The partial vegetation treatment, to our knowledge, has not been previously incorporated into viewscape models. However, this technique is most effective in leaf-off season where the canopy has a small impact on visibility, whereas in leaf-on season it may lead to overestimation of visibility. It is worth mentioning that we did not consider the height of the crown bottom in our assessment of visibility through trees. To improve vegetation modeling, especially for areas with dispersed trees and elevated crown bottoms (e.g., redwood forests), the height of the crown bottom should be factored in visibility assessment and trunk modeling. Moreover, we assumed a binary occlusion system in which trees either completely obstruct visibility or not at all, whereas in reality tree canopy may not be entirely opaque, depending on the foliage type and density. Alternatively, more nuanced methods, such as the use of volumetric (voxel-based) 3D visibility models [68
] or calculating vision attenuation based on foliage density and seasonal variation, may be preferred [27
]. These techniques, however, may pose challenges, due to prohibitive computing time and limited integration with GIS analysis [8
]. Another point worth mentioning is that we assumed similar trunk diameters for all the decimated trees given that the majority of the deciduous trees in our study area are similar sizes. However, in areas with more varied tree typology, this can potentially cause errors in the estimation of under-canopy visibility, especially when the viewpoint is near the trunk. More precise estimation of the trunk can be achieved using tree diameter at breast height (DBH) metric calculated from height (derived from lidar point) and species growth coefficients [28
An additional contribution of this work includes a novel method for model assessment through IVE technology. Employing IVE images allowed us to capture and display the entire FOV, thereby addressing the concerns regarding the inconsistency of perspective photographs with viewshed coverage [8
], and correspondence with “in situ” experience [69
]. However, photograph-based IVEs are static and limit participant’s navigation (moving in the environments) and may include contents that are not captured in the spatial data (e.g., people and cars). Alternatively, 3D simulations and game environments that generate landscape views from geospatial data can be used to achieve higher control over the scene content and implement enhanced interactions (e.g., allowing user-controlled walk-throughs). However, it can be argued that photorealistic panoramas as a cost-effective and easy, yet highly realistic method to capture viewscapes, runs up against the problem of low ecological validity, and higher production effort of 3D simulations.
We should emphasize the need for consideration of more detailed and case-relevant landcover classification. Existing classifications are overly broad and distinguish only between a few forest types (deciduous, evergreen and mixed forest), ground cover, and building typologies (residential and public administrative buildings). Indeed, landscapes are not reduced to their material characteristics alone. People interpret landscape components semantically assigning meanings to them based on their use and cultural, spiritual and historical significance [18
]. Examples include the presence of attractive, historic or landmark buildings, blooming trees, ornamental and exotic vegetation, and attributes, such as maintained and unmaintained vegetation. These indicators are linked to aesthetic preferences or important visual characteristics, such as imageability and stewardship [40
]. Thus, a possible avenue to improve the explanatory power of viewscape models can be using a more granular classification aligned with indicators established in environmental psychology and visual landscape character literature.
We should note that unrestricted exploration of 360° viewscapes afforded by HMDs may come at the cost of reduced control over the amount of visual information that participants receive from a scene. The extent that participants explore the immersive scene, and thus, the information they receive, may vary based on their level of engagement, comfortability and familiarity with the VR equipment, and preference to certain elements and characteristics. Also, as opposed to the unique perspective of still images, the unconstrained horizontal and vertical viewing generates a myriad of perspectives and occlusions, which poses additional standardization challenges. Although we tried to control for these biases by instructing participants to thoroughly explore each IVE scene and base their response on the experience of the place as a “whole,” we cannot make strong inferences of the relative contribution of scene element to perceptions and whether participants received the same information from each scene. In this respect, it would be interesting to examine whether the viewing patterns play a part in respondents’ perception of immersive scenes and explore the specific contribution of certain perspectives or certain landscape elements on perceptions. This can be achieved by leveraging the ability of modern HMDs that record the user’s head orientation and eye-movement in real-time, allowing for establishing the links between viewing behavior, viewscape characteristics, and perceptions.
Finally, the explanatory power of our models may have been affected by personal and socio-cultural differences between participants, such as familiarity with the landscape and place they grew up [71
], level of expertise [18
], and values that they ascribe to the landscape [73
]. Nevertheless, since landscape variations are reported to have much greater influence than the variations between observer’s differences [17
], we do not expect them to have a major influence on our results. In cases where individual and cultural differences are of interest, pre-tests, such as nature connectedness ratings [74
], familiarity [72
], and demographic information can be incorporated in our model to control for baseline differences or as a way to model perceptions of different cohorts (e.g., experts vs. non-experts, local vs. non-local), as shown by Sahraoui et al. [18
This study demonstrated that viewscape modeling based on high-resolution spatial data and improved vegetation modeling can effectively quantify the composition and configuration of visible landscape and predict perceived characteristics and qualities of urban park environments. We also demonstrated that photorealistic IVEs could be used as a viable method to represent and gather human perceptions of viewscapes, and thus, bridge the gaps between objective and subjective analysis of urban landscapes. Several avenues to further improve prediction power of viewscape models are suggested, including refining spatial metrics, using a more granular landcover, quantifying participants’ viewing pattern of immersive scenes, and factoring individual differences into the model. While our results are particular to a context of the urban park area, the workflow could be replicated in other urban and landscape contexts with a step of calibration through conducting IVE survey. Our suggested method can benefit several applications. First, landscape designers and planners can use viewscape model as a way to develop spatially explicit maps of aesthetic and restorative qualities of a site, design a scenic route with specific characteristics in mind (e.g., open, views to the lake), compare landscape characteristic before and after a design intervention or landscape change. Second, research in cultural ecosystem services can use our automation workflow to model viewscapes for millions of appreciated, revered, or frequently visited locations harvested from social media datasets, such as images scraped from Flickr and Panoramio, or comments scraped from Tripadvisor. Third, studies focused on visual impact assessments of infrastructure (e.g., wind turbines and highways) will similarly benefit from improved modeling of vegetation and built features. Finally, landscape perception research can benefit from our approach to investigate subtle relationships between landscape elements and their configuration, and specific psychological outcomes, such as attention restoration or stress-reduction. As our understanding of relationships between urban environments and human psychological and physiological well-being improves, high-resolution models of urban viewscapes will provide a valuable tool to facilitate community engagement and decision-making in urban planning and design.