Introduction

The growing intensity of factors such as urbanisation, agricultural practices, and changing climates continues to shift the view of landscapes that individuals around the world interact with on a daily basis (Dadashpoor, Azizi, and Moghadasi, 2019; Plieninger et al., 2016). Due to the ongoing nature of these changes, it is important to understand how both individuals and groups appreciate and form perceptions of their surrounding landscape. The perception and appreciation of a landscape by communities have been shown to play a key role in the acceptance and success of land management activities (Solecka, 2018). As such, having this knowledge provides important insight and context surrounding the economic and environmental activities that either cause or are aimed to mitigate such changes (Cai, Huang, and Lin, 2022). As such, undertaking management activities that maintain or increase the appreciation of a landscape has flow-on benefits. In particular, it has been shown living in, and interacting with, a landscape that is appreciated has a role in the health and happiness of the community (Li, Peng, Jiao, and Ai, 2022; Opdam, 2020; Marselle, Stadler, Korn, Irvine, and Bonn, 2019).

Alongside continually changing landscapes, how individuals view and interact with their surroundings has also changed (Schirck-Matthews, Hochmair, Strelnikova, and Juhász, 2022). Increases in transport networks have allowed societies around the world to view greater proportions of their surrounding landscapes at eye level – either through physical interaction or through access to terrestrial photography. Since the advent of human flight, however, people have increasingly been able to interact with landscape photography captured over larger spatial extents (Cowley & Stichelbaut, 2012). This was first through aerial photography (Cousins, 2001) and then via the medium of satellite imagery (space-borne photography) (Wulder, Coops, Roy, White, and Hermosilla, 2018). This imagery provides the opportunity to visualise both familiar landscapes, through a different perspective, as well as, landscapes previously not visited (Blaschke et al., 2012). This multi-scale visualisation of human-landscape interaction has the ability to influence how individuals perceive both landscapes both familiar and foreign to them. ( Altamirano et al., 2020;Saldias, Reinke, Mclennan, and Wallace, 2021).

With increasing sources of landscape imagery – at terrestrial, airborne and space-borne scales – comes increased exposure. More recently there has been an increase in both the accessibility and number of open satellite imagery sources, such as mapping applications, digital globes, that effectively remove the financial and temporal cost previously associated with the acquisition of macro-scale landscape photography (Law, Paige, and Russell, 2019; McQuire, 2019). This satellite imagery can then be used as an effective form of visual communication surrounding landscapes and their spatial interactions at multiple scales including temporal change, climate events, geo-political conflicts, urban plannings, real estate, tourism and navigation (Wang et al., 2018; Law, Paige, and Russell, 2019). However, for this imagery to be utilised effectively it must be understood how individuals and populations that view the imagery perceive and interpret the landscapes with which they interact (Altamirano et al., 2020;Kraff, Wurm, and Taubenbock, 2020; Saldias, Reinke, Mclennan, and Wallace, 2021 ). As such, this study aims to contribute to our understanding of the role that increasing access to satellite imagery plays in the continued formation of landscape perception.

The purpose of this study is to examine the influence of interpretation abilities and familiarity with the landscape on individuals’ perception and appreciation of land cover from eye-level and satellite perspectives. Specifically, our objectives are to:

  1. 1.

    Determine if land use land cover classes (LULC) can be interpreted from satellite imagery with similar accuracy to eye-level imagery.

  2. 2.

    Determine if landscapes are appreciated in the same way when shown as eye-level imagery compared to satellite imagery.

  3. 3.

    Examine the role of landscape familiarity in interpreting and appreciating landscapes when viewed from eye-level and satellite perspectives.

Background

The study of landscape perception and appreciation

Developing an understanding of how individuals perceive and appreciate landscapes has been approached through a range of lenses (i.e. geography, psychology, and landscape architecture) ( Bell, 2012; Lindemann-Matthies, Briegel, Schüpbach, and Junge, 2010;Zube, Sell, and Taylor, 1982). Nevertheless, how an individual perceives a landscape, is often considered as a combination of direct sensorial assessment, as well as, an individual’s lived experience and cultural heritage (Hedblom et al., 2019). This combination of factors makes the assessment of perception and appreciation a complex and well-researched subject.

In order to assess how landscapes are perceived researchers have applied survey-based approaches where participants have been asked to evaluate an aspect of a landscape (Nahuelhual et al., 2018; Petrova et al., 2015;Sevenant and Antrop, 2010 ). Aspects considered in these evaluations have included both the opinion and ratings of the entire landscape (Sevenant and Antrop, 2010), and the attribution and rating of key features within the landscape (i.e.LULC class, type of vegetation) (Nahuelhual et al., 2018). Initially, these studies involved on-site evaluations where participants visited locations and assessed them directly (Cai, Huang, and Lin, 2022). Following this work, and in order to increase the number of participants and sites, current research often focuses on the use of photography or simulated scenes representative of eye-level landscape interaction (van den Berg, Koole, and van der Wulp, 2003). As a result of this, the use of such imagery is considered to be an appropriate surrogate for in-situ observation in these evaluations (van den Berg, Koole, and van der Wulp, 2003).

From these studies, several aspects of a landscape have been found to be important in how they are perceived (Tveit, Ode, and Fry, 2007). For example, landscape composition and patterns have been shown to be particularly important in the process of visual landscape perception (Uuemaa, Mander, and Marja, 2012). Furthermore, preferences are often found towards open and heterogeneous landscapes where natural features such as water or trees are present (van den Berg, Koole, and van der Wulp, 2003; Nahuelhual et al., 2018).

The contribution of culture and experience has also been studied by evaluating the responses of people from different countries or regions to landscape imagery. The authors of Petrova et al. (2015) for example sought to investigate the visual and emotional perception of landscapes between Russian and Japanese cultural groups. Their finding suggested that the cultural tradition of respondents influenced their interpretation of landscape beauty. Furthermore, the authors proposed that overall there was a strong correlation between respondents’ estimates of landscape attractiveness, suggesting there may be a universal appreciation for landscape appeal.

The emerging role of satellite imagery

Modern satellite imagery is used to provide a range of scientific and operational insights across a range of fields ( Czekajlo et al., 2021; Wulder, Coops, Roy, White, and Hermosilla, 2018) and using a range of satellites of varying temporal and spatial resolutions Burke et al. (2021); Gazzea et al. (2022); Skakun et al. (2021). The proven value of satellite imagery across both research and commercial activities has also led to the imagery of increasing quality imagery being available within frequently used media platforms. For example, high spatial resolution imagery (with a ground sampling distance of 0.6 m) is now served on digital globes and applications such as Google and Apple maps. Whilst the primary use of this imagery within these applications is to serve as a background map to vector information (such as roads and building locations), it is often used by the public to gain individual insights informing them on route planning, the landscapes of different areas of the globe and for pastimes such as fishing, hiking, and cycling (Schirck-Matthews, Hochmair, Strelnikova, and Juhász, 2022). Furthermore, these platforms are becoming embedded in teaching and learning approaches across a range of subjects (McDaniel, 2022). As well as being used in research activities that require participatory mapping to be undertaken (Buendía, Albert, & Giné, 2021; Gentzel, Wimmer, & Schlagowski, 2021). These platforms and activities serve to expose and educate people to the landscape view offered by satellite imagery.

Satellite imagery presents a different top-down perspective of the landscape to the one we regularly view. This presents a challenge to existing knowledge on landscape perception which has historically investigated in-situ observation or terrestrial photography captured at eye-level (Zhao, Zhang, Xu, Sun, and Deng, 2021). A landscape that appears closed from eye-level photography, may appear more open from the bird’s eye view offered by satellite imagery. Altamirano et al. (2020) for example showed that eye-level imagery of the same landscape was often preferred to aerial (satellite imagery). They also showed that they eye-level imagery had a variation in appreciation in comparison to satellite imagery. Whilst, San Martin Saldias et al. (2021) showed that clear differences in how people perceive their surrounding landscape occur following the viewing of satellite imagery. In particular, this study showed that estimates of the abundance of LULC classes (such as Agriculture or Urban areas) in the surrounding landscape were particularly influenced by the aerial view offered by satellite imagery.

Methods

Study area

The study area was located in Yungay, Chile (see Fig. 1). Yungay is located in Ñuble, the 16\(^{th}\) region of Chile (37.1220\(^{\circ }\)S, 72.0132\(^{\circ }\)W) and is a semi-rural municipality covering 823 km\(^2\) with a local population of 17,787 (Chilean census, 2017). The inhabitants of Yungay are distributed between two urban settlements, Yungay (pop. 11,097) and Campanario (pop. 2206), with the remaining population (38\(\%\)) living in rural areas across the region (Chilean census, 2017).

Fig. 1
figure 1

Examples of eye level photography and satellite imagery showing a and b Native Vegetation, c and d Plantation Forestry, e and f Agriculture and g and h Urban land covers. i A map showing the land cover distribution within Yungay, Chile. Land cover was derived from Sentinel-2 imagery using a random forest classification learning approach. Satellite image sources: Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

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Yungay exists upon a large plateau that extends from the Andes mountain range, located in the east of the municipality, that dominates the majority of the visual landscape. The landscape within this plateau largely consists of (1) the two main Urban areas (Yungay and Campanario); (2) agricultural crops (consisting of grains, canola, and stone-fruit) that increase in density towards the West of the region; (3) Plantation Forestry consisting of both smallholder plantations spread throughout the region and industry plantation concentrated to the east (dominant species include Pinus Radiata, Eucalyptus globulus and Eucalyptus nitens); and (4) Native Vegetation along the rivers and in the eastern alpine region composed mainly of Nothofagus species and shrubland (Fig. 1i). In this study we considered these four LULC classes to be dominant across the landscape, henceforth referred to as Urban, Agriculture, Plantation Forestry and native forest. The definition of these land covers are outlined within Table 1.

Table 1 The definition of the four land use and land cover (LULC) classes used in this study and provided to survey participants
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Imagery

This study utilised imagery from both satellite and eye-level perspectives. Eye-level photography was captured in October 2018 using the 8-megapixel rear camera of an iPad 6

tablet (Apple Inc., Cupertino, CA, USA). Photographs were acquired at 124 locations throughout the region with four images captured at each location, one in each cardinal direction. Locations were selected from an initial set of random points based on accessibility and being unambiguously representative of a single LULC class. At each location, position (taken from the Global Navigation Satellite System (GNSS) receiver within the iPad) and LULC class (based on local expert interpretation) were recorded. From this set of images, three photographs that were representative of a single class were selected for each LULC class. During the selection process, it was ensured that the images had sufficient depth of field and quality. Examples of these different eye-level landscape perspectives are provided in Fig. 1a, c, e, and g.

DigitalGlobe (GE01) imagery from ESRI’s Living Atlas (ESRI, 2022) was used in this study to provide examples of LULC classes from a satellite perspective. This imagery has a native spatial resolution of between 0.5m (at-nadir) to 1 m (off-nadir) and shown as true color images in ESRI’s Living Atlas. When selecting satellite images care was taken to ensure they were taken within the same season, at the same location, and within 2 years of the terrestrial photographs (i.e. from 2016 to 2020). Where satellite images of locations were not available within the same year (2018), the images were manually inspected to ensure land use conversion had not occurred in the area between the capture of the photograph at that point and the satellite image. Examples of these different satellite landscape perspectives are provided in Fig. 1b, d, f, and h.

Research design and methodology

Sample and data collection

Two main population groups were targeted in this survey, those familiar with the Yungay municipality and those unfamiliar (i.e. who do not live in, or had not previously visited the area). As the researchers had no direct access to the target population in Yungay due to the pandemic, snowball convenience sampling was used following the approach used in Leighton et al. (2021). Initial participants were contacted directly through the researcher’s networks such as email and messaging applications (i.e. WhatsApp groups), and through social media platforms (such as LinkedIn, Facebook and Twitter). The researchers then asked these participants to further distribute the questionnaire link via social media and flyers. Survey data was collected from 17th February 2022 to the 17th April 2022.

Questionnaire

The questionnaire was designed to investigate an individual’s ability to interpret land cover classes as well as how appreciative they were of these classes. The survey initially asked participants several demographic questions (including age, gender, education, and familiarity with satellite imagery). The participants were then asked to indicate whether they live or have visited the Yungay municipality.

The main content of the survey was two questions in relation to a set of eye-level and satellite images that were repeated for each image pair. The first of these questions asked the participant to identify the dominant LULC class within the image. For clarity within the satellite images, the LULC class of question was highlighted with a red polygon overlaid over the homogeneous LULC area of interest. The second question asked the participants to indicate, on a seven-point Likert scale, how attractive the landscape in the imagery was to them. To avoid corresponding locations being shown in consecutive order, the sequence in which participants were presented images was randomised.

Following the completion of these questions, the participants were asked to indicate, on a 5-point Likert scale, how familiar they were with each LULC class as presented within the eye level and satellite imagery. Last, they were presented an open-ended question asking what features of the image they found useful when determining the land cover class, how these features differed between the image perspectives, and how useful they feel satellite imagery is for examining landscapes.

Quantitative analysis

Survey responses regarding the interpretation of LULC type of the imagery were compared using Fisher’s exact test. Statistical tests were performed to determine if there were significant differences in interpretation between the eye-level and satellite image perspectives, and within image type to determine the influence of familiarity with the target landscape. The response to the three examples of each land cover were combined for use in a single test for each image perspective (i.e. the responses to all three eye-level image perspectives of Agriculture were compared to all three satellite image perspectives of Agriculture). Further, the accuracy of interpretation was summarised by comparing each survey response to the known land cover at each point using a confusion matrix approach.

A two-sample t-test was used to compare responses to questions regarding landscape attractiveness. Attractiveness was calculated as the mean response given by an individual across the three eye-level and satellite images respectively per land cover type. Where the differences were paired (i.e. in comparing responses between image types), a paired t-test was used. This test was performed with groups assigned by the actual landscape type shown in the image (as interpreted in situ by an expert) and groups assigned considering the participant’s interpreted LULC class response.

Qualitative analysis

Answers to open-ended questions were analysed using Qualitative Content Analysis (Mayring, 2014). Using this approach, participant responses were coded into categories regarding the composition of the landscape such as: land cover types, and individual features (trees or buildings), spatial patterns and colours (i.e. linear features, geometric features, random distributions), and perceptions (i.e. attractiveness, abundance). Within each category, a comparison of the main word groups used to describe the landscapes seen in response to eye-level and satellite imagery was made. Further grouping was undertaken to see if these words varied between people familiar and unfamiliar with the landscape.

Results

Demographics and survey respondents

The key characteristics of the sample population are described in Table 2. A total of 115 participants completed the questionnaire of which 68 (59 %) indicated familiarity with the Yungay landscape. A total of 47 participants were unfamiliar with Yungay (41 %) and were from a wide range of countries including Chile, Australia, Brazil, the United States, Spain, and Japan. Participants were also from a diverse range of genders, ages, and educational backgrounds (high school through to Ph.D.).

Table 2 Responses to demographic questions undertaken during the survey
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Of the respondents how had previous been exposed to satellite imagery the majority (58%) had done so for personal reasons (with purposes including navigation and hiking). Those who used satellite imagery for work purposes (35%) were employed in sectors such as agriculture, geospatial, forestry and public service.

Interpretation

Comparison of interpretation accuracy between eye-level and satellite imagery

The overall interpretation accuracy of LULC classes from the satellite and eye-level viewpoints was found to be equivalent with 80% of the interpretations being correct from each viewpoint. However, key differences (\(p<0.01\)) between the interpretation of the images from the two viewpoints were identified for all LULC classes other than the Urban class. Native Vegetation and Plantation Forestry were identified with higher accuracy from the eye-level point of view (correct interpretation rates of 86 and 89% respectively) in comparison to the satellite point of view (correct interpretation rates of 72 and 70% respectively). Confusion between these two classes was the most common misinterpretation from the satellite viewpoint (Table 3). Plantation forests were also commonly identified as being native forests and Native Vegetation was often interpreted as being Agriculture or Plantation Forestry from eye-level imagery.

Table 3 Confusion matrix showing the accuracy of interpretation of land use and land cover (LULC) types from eye-level and satellite images
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The Agriculture class was more accurately interpreted from the satellite point of view (91%) in comparison to the eye-level point of view (68%). The agricultural class was often confused with Native Vegetation or attributed as being another LULC class (a response of Other) from eye-level imagery. Whilst, Agriculture was not identified as Native Vegetation by any participant in the interpretation of satellite imagery. The Urban class was often interpreted as Other or as Agriculture from both viewpoints.

Effect of familiarity on interpretation

For several LULC classes, respondents familiar with Yungay provided a different interpretation of the imagery compared to those unfamiliar with the region (Fig. 2). Satellite image interpretation was found to be significantly different between the two respondent groups for three of the four LULC classes (\(p<0.1\)), with only the Urban LULC class being interpreted similarly (\(p=0.241\)). For satellite images of Agriculture or Plantation Forests, a higher interpretation accuracy was achieved by those familiar with Yungay compared to those unfamiliar with the area. In contrast, the interpretation of Native Vegetation from satellite images was achieved with a similar accuracy between the two groups. In this case, respondents familiar with Yungay primarily confused Native Vegetation images as being of plantations (28\(\%\) of responses), while those unfamiliar confused Native Vegetation with both Plantation (23\(\%\)of responses) and Agriculture (7\(\%\) of responses). Differences in interpretations made from the eye-level point of view between those familiar and unfamiliar with Yungay occurred for images of the Native Vegetation and plantation classes (\(p<0.1\)). In both cases, respondents familiar with the area provided more accurate responses.

Fig. 2
figure 2

Interpretation results from the a eye-level and b satellite viewpoints made by those familiar (f) and unfamiliar (u) with Yungay for images of each land use and land cover (LULC) class

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Respondents appreciation of landscape views

When considering the actual LULC class shown in the image, differences in the level of appreciation of the agricultural and Native Vegetation landscapes were found from both viewpoints Table 4. For both classes, the eye-level view of the landscape was preferred. However, when considering the interpreted LULC class no significant difference was found in the appreciation of Native Vegetation between viewpoints. This was the result of an increase in appreciation of this class when users interpreted the image they were viewing as Native Vegetation from satellite imagery. No significant differences were found between Plantation Forest and Urban landscapes when viewed from the two viewpoints for both actual and interpreted groupings.

Table 4 Mean appreciation response of for images of each land use and land cover (LULC) classes from the two viewpoints. P-value is derived from two tail T-test comparing the set of responses from each viewpoint
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Overall, people familiar with the region demonstrated a higher appreciation of the landscape views for both perspectives for all classes except Plantations (Table 5). Significant differences in appreciation was found between the two groups for all LULC classes and both perspectives when using the class type as interpreted by the respondent. In all cases similar differences were seen between eye-level and satellite viewpoints for both groups. This was also the case when considering actual class for the Agriculture and Urban LULC classes and the plantation and Native Vegetation classes when viewed from eye level. However, no significant differences were found between the two vegetation classes from eye-level viewpoint when considering the actual class. In the case of Native Vegetation this was due to a significantly lower appreciation of actual images of this class (in comparison to when the respondent believed the image was from this class) by those familiar with the Yungay landscape.

Table 5 Mean appreciation response for images of each land use and land cover (LULC) classes from the two viewpoints for groups familiar and unfamiliar with the landscape. P-value is derived from two tail T-test comparing the set of responses from each viewpoint within each group
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Qualitative comments

When queried about the process used to determine a LULC class from both viewpoints, participants both familiar and unfamiliar with the region indicated that they used similar landscape features. These landscape features included built Urban and rural structures, soil, and vegetation types, and scene elements of socio-cultural importance (such as native or rare Native Vegetation, buildings, and key agriculture outputs such as wheat). Respondents from both groups also indicated that they utilized predetermined knowledge surrounding the perceived appearance of the classes from different viewpoints when identifying the LULC of Yungay. When participants were asked to determine the landscape composition from the satellite perspectives respondents focused on the holistic features present within the landscape. Words such as distribution, texture, line, color, order, homogeneity, geometry, and pattern were commonly used when describing their identification process. In contrast, at eye level, rather than assessing holistic features present across the image, participants were more likely to describe discrete individual objects and features. Descriptions of trees and vegetation species, as well as, objects within the built and rural environments (such as houses, prairie, roads, and farming structures) were indicated as being used to identify different land covers.

When the participants were asked to contrast the difference in features from the two viewpoints, the most used word was ground and this was often associated with adjectives describing scale (big, small, wide, and open). For instance, some participants mentioned that satellite imagery opened up the view of the ground, providing a panoramic view, allowing more information to be ascertained. However, some participants also mentioned that the interpretation of this information was difficult and the understanding would be improved through training in its interpretation. This was often associated with a discussion on how to determine the difference in colour between Native Vegetation and plantation classes. Nevertheless, when queried about the usefulness of satellite imagery most participants indicated that the imagery was useful; highlighting a range of activities including leisure (such as cycling, running, and hiking) through to land management (such as fire hazard reduction, and determining the sustainability of current and future projects).

Discussion

This study aimed to demonstrate whether the alternative perspective provided by satellite imagery can result in both a different interpretation of LULC and a change in appreciation of the same landscape. Similarly to Altamirano et al. (2020), we found that the eye-level perspective of the same landscape was more appreciated in comparison to that captured from satellite platforms. In this case, however, this greater appreciation was only seen for Agriculture and Native Vegetation LULC classes. Whilst several studies have suggested that people from different cultures appreciate landscapes and their features differently (Petrova et al., 2015), this relationship was shown for both those familiar with the landscape they were viewing and those who were not. This suggests that perspective plays a role in determining how much people appreciate a landscape, however, a number of other factors also are involved.

As we view landscapes from eye level on a daily basis, this perspective can be considered to be more familiar than that provided by satellite imagery. However, this increased familiarity of perspective was observed to only lead to improved interpretation in two of the four classes. These two classes, Native Vegetation and Plantations, are distinct as they likely require participants to interpret the properties of individual features (such as tree species) that may not be retrievable from satellite imagery. This is supported by the fact that people familiar with the landscape, those more likely to know which species form native forests and those commonly used in plantations in the area, more accurately interpreted images of Native Vegetation from eye level in comparison to those who were unfamiliar. There was no difference seen in the interpretation of satellite imagery, in which case it is likely that features and properties of individual trees could not be distinguished.

Individuals appreciate landscapes when viewed from satellite imagery will be dependent on a range of factors. Including what is seen in the images, but similar factors that have been shown to affect the appreciation of landscapes from eye-level perspectives such as experiences and characteristics of the viewer (Hedblom et al., 2019). However, whilst an individual may derive their livelihood from agriculture, an Agricultural landscape from another part of the world, viewed from satellite imagery, may appear significantly different such that the appreciation of agriculture does not translate into an appreciation of that landscape view. This is seen in the results of this study, where incorrect interpretation lead to a significantly different appreciation score. For example, when the user believed they were looking at a satellite image of native forests, it was appreciated more than an actual image of a native forest in the area which they perceived to be something else.

When interacting with satellite imagery users had the ability to pan and zoom within the surrounding area. This access was provided as it is the most common means of interacting with this type of imagery. This extra information, over a single static eye-level image, likely played a role in how the land cover was both interpreted and appreciated. This extra information is available when interacting with satellite imagery and has the potential to both improve interpretation through spatial context and may also increase confusion when classes look similar from this perspective. As indicated from the qualitative responses it is likely that participants were able to identify agricultural areas more accurately from satellite imagery due to the surrounding context (including the landscape patterns present). Whilst other users accessed features such as a perceived lack of road access to indicate Native Vegetation. Whilst those familiar with the landscape indicated that they also used the geographic location of the landscape and existing perceptions and knowledge of the area, such as a low likelihood of agriculture occurring in elevated regions. The added context, available when satellite imagery is served on a digital platform, likely also affects appreciation. Users can quickly compare the current view of a target region, to other landscapes. Furthermore, access to growing archives of historic satellite imagery also allows for temporal analysis and communication surrounding landscape change. How humans have managed and shaped the landscape across the world is highly varied. For example, the fragmented, irregular land parcels within Yungay present a different view of Agriculture to that seen when viewing large-scale farming in California, USA (Fig. 3).

Fig. 3
figure 3

A comparison between two satellite images captured from different parts of the world. a the study area of this research and b an agricultural landscape in California, USA. Sources: Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

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In this study, respondents indicated several potential uses of satellite imagery, both for everyday activities and in understanding the impact of larger landscape projects. However, they also highlighted that their understanding of what they are observing is not as clear as when viewed from eye level. The result of this differing ability to interpret landscape cover was seen with decreased appreciation of landscapes interpreted as being plantation forests. Preconceptions about the naturalness of the landscape respondents thought they were seeing (i.e. native forests) led them to indicate a higher appreciation than they otherwise would for a plantation forest. This indicates the importance of research, such as Escobedo et al. (2022) and Svatoňová (2016), which aims to assess and create a society literate in imagery from satellite perspectives. Furthermore, whilst access to this data is beneficial, having a larger number of people capable of interpreting imagery from this viewpoint will most likely lead to a better understanding of how individuals value their surrounding landscape. This will also allow satellite imagery to become a more powerful communication tool in decision-making processes.

The properties of the imagery are an important consideration when determining how individuals may interpret and perceive the landscape within the image. Imagery captured at eye-level of open landscapes, with no obstructing features, allows for easier interpretation. This is similar to satellite imagery, where the motivations for the capture of satellite imagery result in images with varying information, quality, and detail being captured. Imagery is captured at a wide range of spatial resolutions (0.5 m to greater than 1 km) and spectral resolutions allowing synoptic and detailed views of the landscape to be obtained. Spectral information is also often used to better detect changes in the landscape and this information beyond the visible spectrum is also seeing used as a tool for communication. While this study focused on high-resolution imagery across the visible spectrum, the influence of images with other properties (such as time series or false color infrared images) are also likely to play a role in how individuals value and assess landscapes moving forward.

Overall, this study provides an initial indication that how individuals interpret and appreciate a landscape from satellite imagery is different to how they do so from eye-level. Nevertheless, the results need to be considered within the limitations of study. The study considers imagery taken from a single landscape type at a single geographic region. Drawing conclusions from this study cannot necessarily be extrapolated beyond the study area. Other landscapes and land covers (such as found in a coastal landscape for example) may illicit a different set of responses. As such, further research explore how satellite imagery interacts with individual perceptions of landscapes, that are known to be complex and evolve from a range of factors including personal experiences, cultural context, and interactions with the landscape (Hedblom et al., 2019). This study provides an initial insight into possible influences and acknowledges there may be other factors contributing to our individual and collective perception of landscape. Better understanding of how these factors also influence perception of landscapes seen from satellite imagery beyond that shown in this study will have impact in both how we manage landscapes and how we interact with satellite imagery. One of the key factors that should be considered, for example, is how personal experiences, memories, and connections to specific places shape the interpretation of satellite imagery. Analysis of this factor, through in person interviews, could be considered in conjunction with the ever-expanding archive of satellite imagery that allows us to access landscapes of the past.

Conclusion

Insights into the interpretation and appreciation of landscapes, including the type of cover and patterns of cover, are essential in making informed management decisions that are accepted by the wider community. Satellite imagery provides a new perspective from which to view landscapes, presenting the viewer with further information on how the landscape is constructed and how individual land covers can look. This study analyzed people’s interpretation and appreciation of a landscape through this now commonly accessed viewpoint and compared this to eye level view of the same landscape. Whilst similar overall accuracy was found for landscape interpretation from the satellite perspective in comparison to eye-level imagery, it was shown that differences in interpretation from the two viewpoints were present in some of LULC classes shown. Similar differences between LULC classes were seen in the appreciation of landcover imagery. In particular appreciation of the imagery was found to be linked to how the image was interpreted with higher appreciation found if the image was believed to be of a native landscape. Through this interaction, familiarity was found to play a role in how landscapes were appreciated, with differences in interpretation of LULC by those familiar and unfamiliar with the groups affecting the appreciation of that landscape view. This study contributes to our understanding of how both perspective and familiarity of the landscape interact with other factors such as attachment to the various landscape.