Landscape Character and Quality Assessment Through Map-Based Visibility Indicators: A Case Study in Western Crete, Greece

Abstract

Landscape Character Assessment (LCA) is increasingly used to support landscape-sensitive planning; however, existing approaches often lack an operational integration of visual perception and map-based indicators, particularly in complex Mediterranean island contexts. This study demonstrates a methodology for integrated landscape character and quality assessment, combining landform and landcover mapping with map-based visibility indicators derived from the local road network. The approach was applied to the Platanos community in western Crete, a representative Mediterranean landscape of contrasting coastal resort zones, agricultural lowlands, and cultural heritage sites. The methodology followed three stages: desk-based mapping of Land Description Units (LDUs) using landform and landcover data, field surveys to define Landscape Character Types (LCTs) and assess socio-cultural and perceptual attributes, and GIS-based visibility analysis from 18 road observation points. Six visual indicators (connectivity, complexity, naturalness, disturbance, historicity, and visual scale) were calculated to quantify spatial and perceptual characteristics. Results revealed a spatial division between a core northern area of high visual scale, cultural importance, but also disturbance, and a southern area of greater naturalness but lower visual openness and cultural visibility. These results highlight that high landscape quality is not solely associated with naturalness, but emerges from the interaction between physical structure, cultural elements, and visual perception. The findings underscore the complementary value of combining physical, cultural, and perception-based metrics in LCA. The proposed framework offers a reproducible tool for evidence-based landscape planning and heritage-sensitive development in accordance with the principles of the European Landscape Convention (ELC).

1. Introduction

Landscape has been characterized as a broad, relative, and ever-changing concept [1]. According to Déjeant-Pons [2], landscape emerges from the complex relationship between humans and nature and includes a wide range of unique and varied traits. Over the past five decades, the concept of landscape has increasingly been analyzed in a range of academic disciplines, including landscape ecology, human and physical geography, landscape architecture, spatial planning, architecture, public policy, and practices of international organizations, such as UNESCO’s World Heritage cultural landscape program [3] and FAO’s Globally Important Agricultural Heritage Systems initiative [4]. This growing relevance, following the adoption of the European Landscape Convention (ELC) [5], is largely due to the unique capacity of a landscape approach to bridge the gap between natural and human sciences, while connecting policy and practice in the management of both cultural and natural heritage. In recent years, however, many landscapes have come under increasing pressure from anthropogenic and climatic drivers. Climate change, in particular, threatens landscape integrity through biodiversity loss, soil and water degradation, and the transformation of traditional land use systems. Recent initiatives such as the TOPIO project [6] further emphasize this shift, aiming to safeguard both cultural and natural landscape heritage from these impacts by integrating Landscape Character Assessment (LCA), public participation, and digital tools in line with the ELC principles [7]. As interest keeps growing, various academic fields have developed distinct theoretical approaches to landscape, each contributing valuable perspectives while also revealing limitations [8].

Today, landscape remains a broad and complex concept, often characterized by its ambiguity and diversity of meanings [9]. In this research piece, landscape is defined as a concept that encompasses physical, natural, and cultural components. It can be studied and understood not only through its tangible features but also through its aesthetic, sensory, and symbolic values. Because of this multifunctional nature, landscapes integrate ecological, social, economic, historical, and aesthetic dimensions, which are reflected in policy frameworks and management practices [10].

Landscapes are shaped by the interplay of biophysical structures, such as vegetation types, urban forms, and topography [11], and the ways in which people perceive and experience them. However, the way that landscapes are perceived by humans is entirely relational, as for each individual or a community, landscapes are historically and culturally distinct and bearers of plural values, norms, traditions and sentiments [12]. Understanding landscapes therefore requires addressing human perception alongside their structural and physical attributes.

Various frameworks have been developed to describe how people perceive and evaluate landscapes, differing in focus and conceptual depth. Coeterier [13] identified eight descriptive features, degree of unity, style of functioning, chronology, maintenance, spatial ordering, growth as an organism, abiotic basis, and sensory impressions, emphasizing the structural and experiential qualities that shape public perception of landscape. Developing this further, Gulinck et al. [14] proposed a more evaluative approach, relating the value of landscapes to criteria such as integrity, diversity, construction, aesthetics, and ecological quality. Tveit et al. [15] further advanced the discussion by developing a theory-based framework that defines nine key concepts, stewardship, coherence, disturbance, visual scale, historicity, imageability, naturalness, complexity, and ephemera, arguing that visual excellence results from their integration. More recently, Misthos et al. [16] introduced a structured conceptual model that distinguishes between perception and evaluation processes, thus refining the understanding of how people cognitively and emotionally engage with landscapes. Together, these frameworks illustrate a progressive shift from descriptive and structural analyses toward integrated, multidimensional models of landscape perception.

Building on this multidimensional understanding, analytical approaches are evolving to better capture and manage landscape complexity. As the interplay between ecological, cultural, relational and perceptual values becomes increasingly central to landscape governance, tools that can holistically assess and classify landscapes are essential [17]. With its deep insights into the innate characteristics and complex relationships among landscapes, LCA is at the forefront of environmental and spatial planning studies [18]. As a vital tool for decision-making in landscape planning, design, conservation, and sustainable utilization, the LCA process can provide a better understanding of the richness and uniqueness of landscapes by classifying them into distinct types and spatial units [19,20,21]. A greater comprehension of landscape features becomes crucial as there is a mounting pressure to achieve a balance between ecological preservation and human development [22].

LCA has gained renewed momentum as an essential tool for bridging ecological, cultural, and planning dimensions, particularly in Mediterranean island contexts [23,24]. In Greece, and especially in island territories such as Crete, where development pressures and heritage conservation coexist within complex socio-ecological systems, LCA remains underutilized. Studies in Ano Mirabello, a local settlement in Crete, illustrate the method’s capacity to integrate landscape character with rural economic sectors and sustainable tourism strategies, tailoring LCA to local needs and policy instruments [25]. Meanwhile, research by Jridi et al. [26] in Chania employed earth observation data and a suite of quantitative spatial indicators (e.g., patch diversity, visual footprint, heritage index) to evaluate the structural, ecological, visual, and cultural dimensions of the rural landscape. Although not grounded in the LCA framework, their approach aligns with its objectives by linking spatial structure, cultural heritage, and landscape quality assessment, thereby demonstrating the potential for integrating quantitative spatial indicators into future LCA methodologies.

Alongside qualitative and descriptive approaches, the use of landscape indicators has increasingly been proposed as a means of supporting the operationalization of landscape assessment by translating landscape structure into measurable spatial properties. Landscape indicators provide quantifiable measures of landscape characteristics, allowing comparisons across space and supporting evidence-based planning and management. Early work by Bartel [27] and Gallego et al. [28] explored the use of landscape pattern and diversity indicators to evaluate land use structure at regional scales, while Griffith et al. [29] demonstrated the sensitivity of landscape metrics to scale and spatial configuration, highlighting their relevance for landscape-level interpretation.

Within landscape architecture and landscape planning research, indicators have also been used to interpret visual and cultural dimensions of landscape. Fry et al. [30] employed a hierarchical set of landscape indicators to relate archaeological site distribution to broader landscape structure, illustrating how spatial indicators can support cultural landscape interpretation. Gkoltsiou et al. [31] further advanced this perspective by developing landscape indicators for the evaluation of tourist landscape structure, linking landcover patterns with perceptual and experiential qualities. In the context of Crete, Legakis et al. [32] emphasized the importance of integrated, indicator-based ecological assessment for coastal landscapes, underscoring the relevance of spatially explicit evaluation approaches in Mediterranean environments.

While several frameworks and methodologies have enriched the knowledge of landscape character and perception, their integration into coherent, operational tools, such as GIS-based spatial analysis, landscape metrics and indicators, earth observation data, and viewshed analysis, is still limited [33]. In Mediterranean island regions, where rapid development, cultural heritage, and ecological vulnerability intersect, there is still a lack of spatially explicit approaches that evaluate both the structural character and visual quality of landscapes. Although landscape indicators have been widely used in landscape ecology, tourism studies, and environmental assessment, they have only partially been integrated into formal LCA workflows, particularly as tools linking measurable spatial structure with human perception of the land- or sea-scape. In Greece, for instance, studies applying LCA have been scarce and often rely on sector-specific assessments, without systematically incorporating geoinformatics or visual indicators [23,25]. This methodological gap hinders the effective application of LCA principles in spatial planning and heritage conservation, especially in regions such as western Crete where diverse landscape types coexist within a compact territory.

Building on this identified gap, this article aims to develop a methodology for evaluating both the character and visual quality of the landscape. The main contribution of this research lies in assessing landscape character through the analysis of landform and landcover characteristics, combined with a series of visual map-based indicators, using geoinformatics. By integrating desk-based mapping, field-based assessment, and visibility-driven analysis, the proposed methodology offers an operational framework for linking landscape structure with visual experience. The proposed methodology is successfully applied to a case study in the western part of the island of Crete, within the Chania prefecture in Greece.

2. Materials and Methods

2.1. Case Study

The study area is the former local community of Platanos, following administrative reform, located in the municipality of Kissamos of Chania prefecture. The community of Platanos, an area of 40 square kilometers and a total population of almost 1150 people, is located in the western part of the island of Crete (Figure 1). It contains 6 settlements, among them the village of Platanos with the vast majority of the population (900 residents), and Falasarna the most significant place of the study area, and the one that provides its character in the whole region. Falasarna is an area of unique ecological value that has been designated as a protected area within the “NATURA 2000” network, with the name “Imeri kai Agria Gramvoussa—Tigani kai Falasarna—Pontikonisi, Ormos Livadi—Viglia” (GR4340001) [34], due to its unique biodiversity, resulting from the coexistence of protected coastal habitats, traditional agricultural landscapes, and semi-natural ecosystems. The area is contradicting in terms of landscape regime and economic activity. On the one hand, due to its extensive sandy beaches and iconic seascape, the area has become a tourist attraction and an economically developed tourist zone, including family-run hotels and restaurants that can be found in the small inland villages in the southern part of the peninsula. On the other hand, extensive agriculture has been developed in the form of large-scale greenhouses, cultivating mainly tomatoes and cucumbers, and olive groves in the valley. At the same time, visible remains of the ancient harbor town and acropolis of the Hellenistic period (323 BC–30 BC) including fortification walls, towers, cisterns, wells, etc., lie to the north.

In the recent past, up until around 2000, the surrounding area relied more on agriculture than tourism. As a result, the beach was undeveloped and no big hotels or resorts were in its vicinity. Small settlements and villages, along with numerous greenhouses dotted the fields. In recent years, this seems to have gradually changed due to the newly established beach bars, hotels, sun beds, etc., that have altered the landscape gradually.

2.2. Methodology

The methodology adapted has three stages, which integrate traditional landscape research with geoinformatics and spatial participatory analysis following the research roadmap established by the TOPIO project [6]. The first stage consists of a desktop-based landscape characterization, followed by the second stage, a field survey, which includes the refinement of spatial boundaries and the assessment of landscape qualities and values. The initial two-stage methodology applied in this study draws on the framework developed by Van Eetvelde et al. [35] for the landscape characterization of the Flemish region, as well as on related research on the implementation of LCA across European territories, with particular emphasis on the eastern Mediterranean region [36]. The third stage involves a visibility analysis, conducted through map-based indicators to evaluate how the regional landscape is perceived from the local road network. All three stages are described in detail in Section 2.2.1, Section 2.2.2 and Section 2.2.3.

2.2.1. Desktop-Based Landscape Characterization According to Landform and Landcover

In essence, the landscape characterization process is a desk-based process that defines Land Description Units (LDUs) by creating and analyzing simplified map overlays. In order to create a number of map overlays which incorporate the essential elements that contribute to landscape character, each step in the LDU mapping process entails a methodical process of data collecting, processing, and interpretation.

In our study, the natural dimension of the landscape is mapped first, not only to offer a framework for analyzing landscape’s historical evolution but also because the fundamental characteristics used to map the natural dimension have actual borders that are easily retrieved from already published maps. The attribute used at the first mapping level of the landscape characterization process is landform. Landform is a manifestation of the structure and the form of the land surface, shaped by the underlying geology as well as the impact of later geomorphological processes. Within the framework of LCA, landform is therefore treated as integrated morphological expression that summarize the cumulative effects of dominant geomorphological processes (e.g., slope, fluvial incision, coastal shaping), rather than as process-specific geomorphological or lithological unit. The attribute of landform is used to define the relative relief and topography of each LDU.

Contrarily, cultural attributes typically lack such well-defined boundaries. However, due to the constraints that have historically been imposed on land utilization by drainage, slope, and soil fertility, it is frequently feasible to map cultural patterns at the landscape scale using the LDU framework. The attribute being used for the second mapping level of the landscape characterization process is landcover. Landcover is the physical substance that covers the earth’s surface [37]. Since it encompasses both areas with primarily natural vegetation and areas that have largely or completely modified by human activities, it is an attribute that connects the landscape’s natural and cultural dimensions. In the context of the current study area, where all land has diachronically been shaped and transformed by human interventions on the natural environment, landcover is more relevant to the cultural dimension. The attribute of landcover at this mapping level is used to describe the general type of vegetation (natural or man-made) covering the surface of each LDU.

The mapping procedure is carried out in these two distinct steps/levels. The second step builds on the outcomes of the first one, in order to define and refine the LDUs. The LDUs based on the landform are further divided and refined based on landcover (Figure 2).

Landform and landcover were selected as baseline attributes for the desk-based mapping, while other natural (e.g., soil, vegetation characteristics) and cultural attributes (e.g., field patterns, settlement form, archaeological elements) were intentionally addressed during the field survey phase, where site-specific observation allows for more reliable interpretation.

The spatial delineation of LDUs is conducted manually by digitizing polygons on screen. This approach requires a subjective interpretation of the underlying data for the delineation of the LDUs. While this process can be time consuming, especially when working with large datasets, it allows the user full control over the mapping and characterization process. Automated methods and clustering techniques were avoided since they frequently produce generic outputs that do not correspond to the real-world landscape patterns [38].

Prior to the mapping process, all relevant available information for the study area, including Digital Elevation Model (DEM), topographic contour and slope maps, CORINE Landcover data and high-resolution satellite imagery, is either collected or generated, and compiled as series of spatial layers stored within a geodatabase. For the natural dimension, and specifically the landform attribute, a 5 m resolution DEM was used to generate both topographic contours at 50 m intervals and a slope map, supporting landscape-scale interpretation consistent with the objectives of LCA. For the cultural dimension, the CORINE Landcover map, with minimum mapping unit (MMU) 25 ha, was used to delineate landscape patches characterized by human land use patterns. Many CORINE Landcover classes (e.g., agricultural land, urban fabric, managed coastland) directly reflect anthropogenic influences, making the dataset a suitable basis for mapping the cultural landscape structure. Throughout the process, Google Earth satellite imagery served as a supplementary visual reference. Potential inconsistencies arising from data generalization or scale differences were addressed through visual cross-checking with high-resolution imagery and subsequent field survey verification, rather than statistical removal of values.

In terms of mapping parameters, an MMU of 10 km² was adopted, and digitization was conducted at a scale of 1: 20,000. This choice reflects the landscape-scale focus of the study and is consistent with the objectives of LCA, which aims to identify coherent landscape units rather than fine-grained land parcels. Although a smaller MMU could theoretically be achieved at this mapping scale, the selected threshold was intentionally used to avoid over-fragmentation of LDUs in a highly heterogeneous Mediterranean landscape and to ensure interpretability and consistency across the study area.

The mapping process started by defining the landform classes used. Firstly, by using the DEM, the mapped territory was classified into eight (8) distinct categories according to elevation (

Table 1. Elevation classes.

Elevation Classes
≤200
200–400
400–600
600–900
900–1000
1000–1200
1200–1750

), then, creating the slope map of the area, the territory was classified into six (6) classes based on the average slope percentages (

Table 2. Slope classes.

Slope Classes
≤2 flat
2–5 gently undulating
5–10 undulating
10–20 rolling
20–50 steeply rolling
>50 steeply sloping

). The step values for elevation and slope were defined so as to reflect the natural breaks in terrain across the study area, while at the same time ensuring that the resulting categories remain interpretable and practical for LCA.

The definition of landform types is based on the above categorization, and it is a mix of basic geographical and descriptive terms [38], following the exact classes below (

Table 3. Landform type descriptions.

Landform Types	Landform Type Description
Plains (PL)	Areas with flat (0–2%) to gently undulating (2–5%) topography, lying at the base of rising slopes. They may appear at any elevation.
Lowlands (L)	Areas of land generally lying < 200 m above sea level, with undulating (5–10%) or rolling (10–50%) topography.
Hills (H)	Somewhat elevated tracts of land (generally between 200 and 600 m above sea level), characterized by rolling topography (10–50%).
Uplands (U)	Elevated and often extensive tracts of land generally lying between 600 and 1200 m above sea level, with variable but mostly moderate relief (typically rolling topography, between 10 and 50%).
Deep Valleys (DV)	Valleys at least as deep as they are wide, with vertical or near-vertical slopes on large parts of the valley sides, often including cliffs and rock outcrops.

), resulting on the creating map in Figure 3.

The mapping process proceeded by identifying the principal landcover patterns within each LDU, based on a broad classification scheme. The CORINE Landcover dataset of 2018 was used as the primary source for this stage, as it offers a consistent and reliable reference for landscape classification across Europe, while its vector format and thematic classes allow for efficient incorporation into the GIS framework [39].

However, CORINE’s resolution and generalization can constrain the detection of finer scale patterns, especially in regions of heterogeneous land use. To address this, and to support a more spatially comprehensive classification, high-resolution satellite imagery from the Pléiades constellation was also utilized. This imagery, with a spatial resolution of 50 cm, allowed for a more detailed examination of landcover features. A supervised object-based classification was carried out, using the Random Forest algorithm, to produce a high-detail classification map of the study area (Figure 4).

While the CORINE classification served as the baseline for defining broad landcover categories, the classification from Pléiades constellation [40] contributed a complementary dataset that reinforced the mapping accuracy at a local level. In particular, it supported the refinement of LDU boundaries in complex or transitional zones, validated uncertain classifications, and assisted in recognizing mixed landcover types that would otherwise be generalized. This dual source approach facilitated a more robust representation of the cultural dimension of the landscape.

At this point, mapping landcover is the primary step for describing the physical coverage of the Earth’s surface. Spatially refined classification is developed during the second phase, the field survey, where the focus shifts to land use categorization.

Table 4. CORINE combinatory landcover classes of the study area.

CORINE Combinatory Landcover Classes
agriculture with natural vegetation
broad-leaved forest
olive groves
sclerophyllous
bare rock, sclerophyllous
bare rock, sparsely vegetated areas
complex cultivation patterns
discontinuous urban fabric
grasslands
grasslands, sclerophyllous
sclerophyllous, pastures, grasslands
complex cultivation patterns, beach
woodland-shrub
Beach

below presents a landcover classification suitable for this initial mapping stage, aligned with relevant CORINE landcover classes. It should be noted that the classification categories listed in Table 1 may be combined (i.e., mixed landcover characterizations) when a single dominant landcover type cannot sufficiently describe an LDU.

The cartographic combination of the landcover categories with the identified landform classes produced the final mapping output of this stage, resulting in the delineation of the LDUs shown in Figure 5, with each LDU named according to its dominant landform–landcover combination.

2.2.2. Field Survey Refinement and Assessment

The field survey constitutes a fundamental phase in LCA, providing an essential supplement to the desk-based mapping and analysis. The fieldwork in the current study proved to be an indispensable component in capturing the landscape’s complexity. Engaging directly with the terrain allowed us to experience the landscape at eye level, revealing features that are frequently hidden by satellite images or GIS-based interpretation alone, such as fine-grained elements including understory vegetation patterns and small patches of shrub with minor field boundaries that cannot be reliably detected through remote sensing [41,42]. This on-site perspective enabled the identification of components, characteristics, and perceptual attributes that were not apparent during the desk study and gave insight into how people actually see, use, and feel the landscape, i.e., the landscape perception.

The survey was structured within the spatial framework of LDUs, which had been delineated during the prior mapping phase. Field observations were conducted through a total of 14 field stops distributed within the study area, played a critical role in validating and refining these units. Boundaries were cross-checked and, where necessary, adjusted to reflect physical or visual discontinuities not evident in the spatial data acquired during the first phase of the analysis (2.2.1). In several cases, this led to the merging of LDUs to better reflect the observed landscape character. As a result, descriptive categories that group LDUs sharing similar character, the LCTs, were created and presented in the map of Figure 6.

In parallel with its role in refining spatial boundaries and character units, the field survey also provided a robust framework for assessment of landscape qualities and values. A total of 28 field survey sheets were completed, covering the different categories of LDUs present within the study area. The structured format of the field survey sheets (Supplementary Tables S1–S3) enabled the recording of both quantitative and qualitative observations, facilitating a multi-dimensional understanding of each LDU. These forms captured physical attributes such as geology, land use, settlement presence, topography, vegetation patterns, field structure, and visual exposure, offering insight into the condition and management intensity of the landscape.

Beyond the physical inventory, the assessment included socio-cultural value-based dimensions. Four surveyors, all of whom were experienced practitioners in landscape studies and assessment, rated attributes such as aesthetic quality, cultural significance, tourism and recreational value, ecological richness, and historical associations on a scale from 0 to 100%, allowing for differentiation between areas. The 0–100% scale represents a continuous rating scale, where 0% corresponds to very low presence or importance of an attribute and 100% to very high presence or importance. Intermediate values reflect proportional judgments by the surveyors based on their field observations, allowing flexible but consistent differentiation between landscape units. The selection of these attributes in the field survey was guided by the need to reflect key material (ecological and visual) and non-material (cultural, historical, and experiential) dimensions of landscape character, in line with established approaches to LCA and international best practices in landscape evaluation and planning [43,44,45]. The third form further incorporated landscape perceptual descriptors such as serene, wild, ancient, or fierce, which reflect experiential and emotional responses to the landscape. These descriptors helped capture the intangible and symbolic dimensions of landscape character, reinforcing the human-centered perspective of the assessment. This viewpoint posits that landscapes are not only interpreted as biophysical phenomena, but also as social constructions that are shaped by human agency, cultural values, and individual experiences. An awareness of these interactions is essential for successful landscape characterization and management [46].

This approach enabled a consistent, comparable assessment across all LDUs and enriched the overall characterization by integrating subjective perceptions and cultural values with objective field observations. In this way, the field survey also complements the desk-based mapping by incorporating finer-scale natural and cultural attributes that cannot be consistently represented through desk-based spatial data alone. Specifically, Sheet 1 (objective field observations) supported the refinement of LDU boundaries, leading to the delineation of LCTs, while Sheets 2 and 3 (subjective perceptions and cultural values) contributed to the description of each LCT. To quantify the results of Sheets 2 and 3, an average score was calculated for each LCT based on the surveyors’ ratings of the sheet elements, by summing the values recorded for the individual LDU categories included within each LCT and computing their mean, producing socio-cultural value and perception value indices. The outcomes were subsequently mapped (Figure 7 and Figure 8), and the interpretation of these mapped indices further informed and enriched the descriptive profiles of the LCTs. The outcomes of this assessment phase thus contributed first to the delineation of the LCTs and subsequently to the development of draft landscape character descriptions (

Table 5. Landscape character descriptions.

Landscape Character Type	Landscape Character Description
Beachfront and agricultural coastal lowlands (mainly greenhouses)	A sandy beachfront area with high tourist activity, featuring organized umbrellas, restaurants, cafés, bars, and various recreational facilities. The coastal lowlands are sparsely settled, lacking organized residential areas, with development mainly consisting of rental accommodations and food service establishments. The landscape is almost flat, dominated by intensive agricultural activity, including some private crops but primarily large-scale greenhouse farming. On the north side and in the middle of the main Falasarna beach, there are areas of archaeological interest, adding historical significance to the coastal landscape. Socio-cultural values within this setting show notable variation, with some sub-areas closely tied to everyday tourism and farming activity, while others hold stronger importance due to their role in supporting local livelihoods and heritage. Perceptual qualities also differ across the landscape: certain zones, such as the central organized Falasarna beach, are experienced as more ordinary, whereas its southern part conveys a stronger sense of place and visual appeal, with another distinct sub-area, the beachfront of Sfinary village in the south of the case study, offering a balanced, moderate level of perceptual interest.
Rocky shrubland lowlands	An unsettled landscape of low elevation with a mixed topography, ranging from undulating to rolling terrain. The area is predominantly covered by shrubs and general sclerophyllous vegetation, scattered with patches of bare ground. Along the hilly coastline, small beaches can also be found, adding diversity to the landscape. Socio-cultural values vary across the sub-areas, reflecting the role of neighboring patches and their connection with local practices and significance. Perceptual qualities are generally high, with most sub-areas experienced as particularly distinctive and visually appealing.
Rocky shrubland hills	A landscape with very low settlement density, ranging from 200 to 600 m in elevation, characterized by exposed views and gently undulating to undulating topography. The area is mainly covered by sparse sclerophyllous vegetation, with extensive patches of bare rocky ground. At slightly higher elevations, grasslands and pastures also appear, adding to the landscape’s ecological diversity. Socio-cultural values are generally low, with only a few localized areas showing greater significance. Perceptual qualities are more varied, with the northern, more remote part of the case study area and an area south of Platanos village convey a stronger sense of distinctiveness and visual appeal, enriching the overall character of the landscape, while the remaining rocky hills are generally experienced as more subdued and ordinary.
Lowland olive groves	An extensive olive grove landscape with mainly rolling topography, featuring scattered rental accommodations and food service establishments at low density, primarily near the coast or the main village up the hill to the east. Among the olive trees, shrubs can also be found, adding to the vegetation diversity. Socio-cultural values are moderate across the entire area, reflecting the ongoing role of olive cultivation and its associated practices in shaping both livelihoods and local identity. Perceptual qualities are also consistently moderate, offering a landscape that is familiar and functional, yet still maintains a steady level of visual interest and experiential value.
Terraced agriculture and natural vegetation on hills	A hilly landscape with undulating topography, sparsely settled due to its location around the main settlement of the area. Terraced agricultural activity, including crops and olive trees, coexists with natural low vegetation, creating a diverse and structured landscape. Socio-cultural values are mixed, with some areas reflecting low significance while others hold moderate importance, largely linked to ongoing cultivation and local practices. Perceptual qualities, by contrast, are consistently high across all subareas, as the terraces combined with natural vegetation create a striking and distinctive visual character that strongly defines the sense of place.
Lowland settlement	The landscape includes the village of Sfinari, the second-largest settlement in the area, located 200–300 m from the beachfront. This small settlement of approximately 100 residents consists of family houses, a few rental accommodations, and restaurants, all surrounded by olive groves, cultivated fields, and forested areas. Socio-cultural values are particularly high, reflecting the village’s role as a local hub of community life, services, and economic activity. Perceptual qualities, however, are comparatively limited, as the built fabric and everyday functions of the settlement convey a more ordinary character within the wider landscape.
Hilltop settlement	The landscape includes the settlement of Platanos, the largest settlement in the area and one of the largest villages in the Chania prefecture. Home to approximately 1000 residents, it is situated on a hilltop at an elevation of 250 m, offering expansive open views to the west. The settlement consists primarily of family houses, along with stores, cafés, taverns, and various small-town amenities. As the last stop before heading west to Falasarna beach, it serves as a key hub for the surrounding region. Socio-cultural values are particularly high, reflecting the village’s central role in local life and its importance in sustaining regional connections. Perceptual qualities, however, are more modest, with the settlement experienced as functional and familiar rather than visually distinctive within the wider landscape.
Forested lowlands	An unsettled forested landscape with undulating topography at low elevation. The forest follows a streambed and is composed of broad-leaved deciduous trees with a shrubby understory, creating a diverse natural environment. Socio-cultural values are moderate, reflecting a landscape with some relevance for local use and identity but without strong direct economic activity. Perceptual qualities, however, are particularly high, as the dense vegetation, stream corridor, and natural diversity create a distinctive and visually appealing environment that enriches the overall character of the area.
Forested hills	An unsettled forested landscape in a high hill area with steep topography, ranging from 300 to 800 m in elevation. The narrow-line forest formation consists of broad-leaved deciduous trees with a shrubby understory, surrounded by rocky terrain with sclerophyllous vegetation, blending into a varied and untamed landscape. Socio-cultural values are moderate, reflecting some local relevance but limited direct economic activity. Perceptual qualities are consistently high, as the combination of steep relief, forest cover, and rocky outcrops creates a striking and memorable landscape that strongly defines the area’s natural character.

) which are supported by representative landscape photographs presented in

Table 6. Representative landscape photographs.

Landscape Character Type	Landscape Photograph
Beachfront and agricultural coastal lowlands (mainly greenhouses)
Rocky shrubland lowlands
Rocky shrubland hills
Lowland olive groves
Terraced agriculture and natural vegetation on hills
Lowland settlement
Hilltop settlement
Forested lowlands
Forested hills

.

2.2.3. Local Road Network Dependent Visibility Analysis Through Map-Based Indicators

The indicators in landscape analysis denote the extent to which physical landscape attributes are quantified, assessed, or scaled to facilitate comparisons between diverse landscapes or to detect changes within the same environment over time [26,47].

Building on the conceptual understanding of landscape indicators and their role in linking landscape structure with visual qualities, as outlined in the Introduction, the literature was examined to investigate the prevailing terms employed to describe landscape visual quality. The levels of abstraction range from the conceptual level, which is abstract, to the tangible measures of physical landscape descriptors. Concepts, dimensions, attributes, and indicators (high to low abstraction) are the four levels of a hierarchical structure that Tveit et al. [15] established in relation to the levels of abstraction. These fall into two main groups: the abstract conceptual level, which includes concepts and dimensions, and the physical landscape level, which includes attributes and indicators. Concurrently, concepts may be seen as an overarching term, with dimensions delineating various facets, attributes defining the dimensions, and visual indicators serving as descriptors. In order to compare different landscapes or to detect changes in the same landscape over time, the indicators show the degree to which the physical landscape qualities are tallied, measured, or scaled. Their visual concepts can be broken down into a triplet of structure, function, and value, according to Parris [48] and his explanation of the evolution of landscape-related indicators.

The current study places primary emphasis on landscape structure, as this provides a reproducible foundation for GIS-based analysis. Although perceptual and socio-cultural dimensions (linked to function and value) were recorded in the field survey, they were used mainly as descriptive supplements rather than the core analytical focus. This ensures the framework remains consistent and spatially explicit, while recognizing that function and value require participatory evaluative approaches. By focusing on structural attributes, the research establishes a clear baseline for articulating landscape visual character, which can later be expanded to include functional and value-based perspectives.

Martin et al. [49], following the previously discussed methodology, investigated landscape character based on visual perception from motorways. Their approach categorized the variables used to identify various related concepts into two main groups: those calculated for the entire territory and those calculated specifically at observation points from which the landscape is viewed. In the present study, we adopt a similar framework, selecting points on the main road network (Figure 9), to assess landscape character, employing the following variables: connectivity, complexity, and naturalness, which are calculated for the entire study area, with the mean value within the viewshed assigned to each observation point, and disturbance, historicity, and visual scale, which are derived directly from the viewshed at the observation points (Figure 10). A total of eighteen observation points were distributed along the road network, with each point located at least one kilometer from the next, to ensure spatial coverage.

The selection of these indicators was guided by their ability to capture complementary aspects of landscape structure and visual experience within an LCA framework. Connectivity, complexity, and naturalness were selected to describe the spatial configuration, coherence, and degree of human influence at the territorial scale, reflecting how landform and landcover patterns structure the landscape as a whole. Disturbance, historicity, and visual scale were included to capture perception-related properties that emerge from specific viewpoints and viewshed conditions, allowing the interpretation of how landscape structure is experienced visually. This combination of territory-based and viewshed-based indicators enables an integrated assessment of landscape character and visual quality while maintaining a primary emphasis on reproducible, map-based measures.

Before proceeding with the calculation of the map-based indicators and their visual representation on a raster surface using Kernel Density Estimation, the appropriate mapping background was created by calculating and illustrating the viewshed areas for each observation point (

Table 7. Viewshed areas (shown in red) for each of the 18 observation points along the main road network of the study area. In each map, the corresponding observation point is highlighted in yellow.

), which constitute the viewshed analysis underpinning the visibility assessment, and serve as a methodological illustration of the visibility conditions used for indicator calculation, while their comparative interpretation is addressed through the aggregated indicator results presented in Section 3.1. The observers’ height was set at 1.70 m, and the viewshed radius was calculated as 5 km, representing a realistic upper bound of human visual range in open Mediterranean landscapes, appropriate for the scale of the study area. This was estimated using the following formula [50,51]:

$$d\approx 3.57\surd h$$

where h is the observer’s height in meters, and 3.57 is the coefficient in the standard geometric approximation used to estimate the theoretical horizontal distance (d) to the horizon in kilometers.

Individual viewshed maps are not interpreted separately, as their analytical role is to provide a standardized spatial input for the derivation of visibility-related indicators, ensuring consistent and comparable assessment within the LCA framework.

Connectivity

The aim of this variable is to measure the degree of consistency among the land uses found in the landscape, specifically evaluating whether the land use in a given area aligns with that of the surrounding territory. To this end, the Connectivity Indicator (CI) utilized in Mancebo et al. [52] is employed. Conceptually based on Hanski’s metapopulation model [53], which has been applied in landscape ecology to evaluate habitat connectivity, the CI is also applicable for characterizing landscape coherence. When applied to land use patterns, it provides a valuable measure of the spatial unity or coherence of the scene.

It is given by the following equation:

$$CIi={\displaystyle \frac{\sum _{j=1}^n\frac{Aj}{Cij}}{2\pi C_{max}}}$$

where CI is the value of the connectivity indicator for origin i, Ci,j is the effective distance between origin I and destination j, Aj is the area of each one of the n destinations j belonging to the same origin class i (in this case, by class it means the land use), C_max is the maximum value of the numerator.

The indicator calculates the total number of cells with land uses that are the same as the origin, minus the effective distance between each destination and the origin (which weights the distance between two points according to the characteristics of the area between them). The indicator’s value is consistently in the range of 0 and 1. As a result, values near 0 signify low coherence, whereas values near 1 suggest stronger unity.

Complexity

Landscape complexity is typically assessed using indicators that capture the spatial configuration of its constituent patches [15]. Among these, the perimeter-area ratio is widely proposed method for quantifying patch complexity [54], and it is employed in the present study. Although the perimeter–area ratio is formally a shape metric, it is widely used as a proxy for structural complexity because irregular patch boundaries produce disproportionately large perimeters relative to area. Riitters et al. [55] showed that perimeter–area metrics load on a distinct factor associated with patch shape irregularity and spatial pattern complexity, supporting their use as indicators of structural intricacy. In this way, the perimeter–area ratio provides a quantitative approximation of patch-level complexity, contributing to the interpretation of landscape heterogeneity.

The function is defined as follows:

$${PARA}_i={\displaystyle \frac{P_i}{A_i}}$$

where P_i is the perimeter of the patch corresponding to land use i, A_i is the area of patch i.

Naturalness

The magnitude of the patches covered by natural land use types (low-intensity, minimally modified covers such as forests and shrublands [39]) is the basis for the naturalness metric. This strategy considers the region’s ecological significance in promoting the resilience and survival of natural habitats. Ecologically, the number and presence of various species are strongly linked to the size of these patches [54].

As a result, the indicator is defined as follows:

$$S_i=area of the patch associated to a natural land use$$

Disturbance

Disturbance is often linked in academic literature to visible human interventions, such as construction and alterations to the landscape. More specific, visual disturbance arises from artificial elements that conflict with their surroundings due to disproportionate scale, stylistic inconsistency, or inadequate contextual integration [56,57]. The related indicator calculates the disturbance as it is visually perceived from the observation points.

And it is defined as follows:

$$P_i={\displaystyle \frac{artificial area into the viewshed}{area of the viewshed}}\times 100$$

Historicity

Historicity and the presence of historical elements have been recognized as significant factors influencing landscape perception and preference [58,59]. Rather than existing in isolation, such elements usually constitute broader systems or interconnected networks, commonly referred to as historical settings or historic landscapes [60]. The indicator of historicity calculates the density of cultural heritage elements present in the viewshed of specific observation points.

And it is defined as follows:

$$H={\displaystyle \frac{area of cultural elements into the viewshed}{area of the viewshed}}\times 100$$

The significance of these cultural heritage elements transcends aesthetics, as they provide tangible links to the past and significantly influence modern landscape interpretations [61,62]. In the present case study, the relevant data were provided by the Archaeological Cadaster of Greece [63].

Visual scale

A key component of theoretical frameworks pertaining to landscape preference and visual quality is the concept of visual scale. It concerns how people interpret the dimensions, shapes, and variations in spatial units of a landscape, often referred to landscape rooms or perceptual landscape units [15]. Visual scale is directly related to the texture or degree of openness of the landscape and is affected by line-of-sight and the size of the viewshed area [64,65,66]. Studies have also demonstrated a strong relationship between landscape openness and landscape preference, highlighting the perceptual relevance of visual scale in landscape quality assessment [63,64].

In the current research visual scale is defined as follows:

$$V={\displaystyle \frac{area of the actual viewshed }{area of the circle}}\times 100$$

3. Results

3.1. Landscape Visibility Indicators and Integration into LCA

Table 8. Calculated percentage (%) results for the map-based visibility indicators.

ID	Connectivity	Complexity	Disturbance	Historicity	Naturalness	Visual Scale
0	29.6	2.0	71.8	84.2	11.2	16.8
1	14.0	0.8	0.0	0.0	100.0	2.2
2	26.8	1.0	19.6	3.5	29.5	10.4
3	29.4	1.1	0.0	0.0	100.0	2.3
4	19.1	0.8	38.6	15.7	61.4	13.3
5	25.7	1.0	88.3	79.0	11.6	8.8
6	27.9	0.9	24.0	0.0	76.0	2.5
7	30.1	1.0	50.7	41.3	14.5	17.3
8	19.7	0.8	42.9	13.1	57.1	8.6
9	28.8	1.7	40.1	0.0	59.9	14.1
10	24.8	0.9	56.7	57.5	24.9	20.1
11	25.3	1.1	73.2	90.6	3.2	13.1
12	25.0	0.9	48.6	52.5	41.1	17.4
13	28.9	1.7	48.2	0.0	51.8	7.9
14	21.6	0.8	42.3	0.0	57.7	15.4
15	24.3	0.8	0.0	0.0	100.0	2.0
16	25.0	0.9	36.7	57.7	43.1	11.0
17	15.3	0.9	47.0	0.0	53.0	1.6

shows the percentage results of the visibility indicators analysis. Each column corresponds to a specific indicator, and each row corresponds to an observation point selected on the main road network of the study area (see Figure 9).

Connectivity

The highest value of connectivity (around 30%) was observed in the viewsheds of points 7, 0, and 3 (Figure 11). These viewshed areas include lowland olive groves and beachfront agricultural land located in the middle northern part of the case study area. The viewsheds with the lowest connectivity values are those of points 1 and 17 (around 15%), which mainly include rocky shrubland hills in the southern part of the study area (Figure 11). The low connectivity values in the southern part indicate a generally more fragmented landscape. Moreover, regarding the character of the landscape as approached through road observation points, it can be concluded that the methodology effectively captured the overall pattern, as the majority of points (12 out of 18) showed connectivity values above the average.

Complexity

The perimeter-area ratio was calculated for each LDU patch, and to attribute it to the viewshed of each observation point, the average value was used. As a result, the most complex patches are found mainly in lowland olive groves and beachfront agricultural land, and secondarily in hilly rocky shrublands and terraced agricultural areas with natural vegetation, all located in the middle northern part of the study area (Figure 12). On the other hand, the least complex patches consist of lowland olive groves and rocky shrublands, along with beachfront agricultural land and rocky shrublands on hills (Figure 12). It is significant to note that all areas covered by patches of lower complexity are located in the southern part of the study area.

Naturalness

The highest values of the naturalness indicator were calculated for the viewsheds of observation points 1, 3, and 15 (Figure 13). These viewshed areas are characterized by a 100% natural environment and consist of rocky shrublands in lowlands and hills located in the middle southern part of the study area. In contrast, the lowest naturalness values were found in the viewshed areas in the northern part, which include lowland olive groves and beachfront agricultural land (Figure 13).

Disturbance

The disturbance indicator provides a complementary perspective to the naturalness indicator. The highest disturbance values were observed in the viewsheds of the northern part, which include olive groves and beachfront areas, while the lowest values, with disturbance equal to 0%, were found in the rocky shrublands of the middle southern part of the study area (Figure 14).

Historicity

Historical elements are present in specific locations within the study area, resulting in some observation points having very high values and others with values equal to zero. The viewsheds of ten out of eighteen observation points include historical, archaeological, or cultural elements, which are distributed throughout the area but are mainly concentrated in the middle northern part, where the visible remains of the ancient harbor town and the Hellenistic period acropolis are found (Figure 15). In addition to this archaeologically significant site, there are two more extensive areas in the central part of the study area and a small remote area in the south that further contribute to the overall historical perspective (Figure 15). Conclusively, it can be assumed that culturally significant places are visible from many locations along the local road network.

Visual scale

Regarding the visual scale indicator, the higher the value, the greater the viewshed area in relation to the viewshed circle (the maximum area that could be viewed). The highest values were found at observation points 7, 10, and 12 (17–20%), all located along the beachfront in the northern part of the study area (Figure 16). In contrast, the lowest values were found at observation points 1, 3, 6, 15, and 17 (1.6–2.5%), which are situated in the eastern part of the study area, characterized by more rolling topography (Figure 16).

The visibility analysis through road-based observation points makes a direct contribution to the LCA by providing a measurable, perception-oriented interpretation of how landscape components are experienced in situ. Whereas the LDU mapping and their grouping into LCTs was based primarily on landform, landcover, and field-based observations and assessment, establishing the physical and cultural pattern of the landscape, the visibility indicators provide insight into how those patterns are perceived, in terms of coherence, visual scale, disturbance, naturalness, and cultural visibility. By attaching specific visual properties to mapped areas, the visibility analysis refines the spatial variation interpretation across the study area and addresses the interpretation of both landscape character and landscape quality in a systematic, evidence-based manner. This allows the indicator results to be interpreted with direct reference to specific LCTs, strengthening the linkage between the desktop-based classification and the visibility-driven analysis.

3.2. Landscape Character

Regarding landscape character, as approached through road network–dependent visibility indicators, the study area can be divided into two main parts. The first is the core area, located in the middle northern part, which includes olive groves, the Falasarna beachfront with its agricultural land (mainly greenhouses), the hilltop settlement of Platanos village with its surrounding terraced agricultural areas, and small landscape patches of rocky shrublands in both lowlands and hills (Figure 17 and Figure 18). North of this core area lies a more extensive zone of hilly rocky shrublands. This area is difficult to access, as it is not connected to the road network and can only be reached by footpaths (Figure 17 and Figure 18).

The secondary area is situated in the middle southern and southern parts of the study area. It includes large areas of rocky shrublands in lowlands and hills, the small lowland settlement of Sfinari village with its beachfront, some limited crops and greenhouses, a small olive grove, and forested areas (Figure 19 and Figure 20).

3.3. Landscape Quality

Overall, the core area is characterized by higher values of visual scale, especially in the lowland and beachfront zones, resulting in a landscape of higher preference and perceived quality. At the same time, it is a spatially integrated but also complex area in terms of land use patches, with cultural significance, relatively low naturalness, and a high degree of human intervention.

Meanwhile, the secondary area, including Sfinari village and its surroundings, is characterized by extensive patches of natural landcover, fewer artificial elements, and less general visual disturbance. It is a less spatially complex and less coherent area, with significantly lower cultural attributes and lower values of visual scale, except at its northern edge where higher naturalness values contribute to increased landscape quality.

4. Discussion

The results of this research demonstrate the benefits of combining landform and landcover mapping, field-based character assessment, and road network-dependent visibility indicators to comprehend landscape character and quality in an integrated manner. The composite methodology highlights distinct spatial contrasts between the core and secondary areas of the study region, identifying an intricate interaction among naturalness, cultural significance, and perceptual attributes. Interestingly, landscapes with high visual scale and cultural visibility, such as the beachfront of Falasarna and the hilltop settlement of Platanos, showed stronger indicators of landscape preference. This supports earlier theoretical research that openness, historicity, and complexity tend to be allied with higher perceived landscape value [10]. In contrast, the southern zone, being less disturbed and more natural, scored lower on cultural and visual indicators, reaffirming the idea that naturalness is not an adequate predictor of visual landscape quality [47,67].

The inclusion of field survey assessment forms (Tables S1–S3) enabled the recording of intangible landscape aspects, including perceptual qualities (e.g., “serene,” “wild,” “dramatic”) and experiential socio-cultural values (e.g., sense of place, aesthetic coherence). Such factors, neglected in purely quantitative GIS models, greatly enhanced the interpretation of spatial patterns. The capacity to integrate measurable map-based indicators with subjective field-derived values supports a more human-centered LCA model. In doing so, this research aligns with recent conceptual advances promoting the inclusion of both visual-experiential and ecological-functional landscape dimensions in planning frameworks [16,17,18]. In addition, the use of a systematic survey approach for all LDUs provided for comparability and consistency of evaluation, strengthening landscape typologies (LCTs) derived from the data.

Despite the strengths of the proposed methodology, several limitations should be acknowledged. First, the visibility analysis is based on observation points located along the road network, reflecting common patterns of everyday landscape experience but excluding viewpoints outside transport infrastructure. Second, socio-cultural and perceptual attributes were assessed through expert-based field surveys, which ensured analytical consistency but did not capture the full range of public perceptions. In addition, the analysis relies on static representations of landform, landcover, and visibility, and therefore does not account for temporal dynamics such as seasonal vegetation change, short-term tourism fluctuations, or longer-term landscape transformations. Finally, while the selected set of indicators provides a robust framework for interpreting landscape structure and visual quality, it does not encompass all possible ecological or social dimensions, which could be addressed through complementary indicators or participatory approaches in future research.

Beyond its direct application to landscape character and visual quality assessment, the proposed framework provides a spatial and conceptual basis that could support future analyses linking landscape characteristics to ecological processes and ecosystem service supply. By integrating landform, landcover, and visibility-based indicators, the methodology can help identify landscape units that are both visually prominent and ecologically sensitive, such as riparian corridors, wetlands, or coastal lowlands, which are often subject to heightened development pressure. Similar integrative perspectives have been adopted in recent ecosystem services research examining the combined effects of landscape change and environmental drivers on water-related ecosystem services, emphasizing the importance of spatially explicit landscape characterization for understanding service provision and vulnerability. In this context, the delineation of LDUs provides a spatial structure that could support the identification of areas contributing to water-related ecosystem services, including water regulation, purification, and retention, as discussed in landscape–ecosystem service frameworks applied in different geographic settings [68,69]. While the present study does not explicitly model ecosystem services or climate change impacts, the spatially characterization of landscape structure and visibility could form a useful foundation for future integrated frameworks that examine interactions between landscape characteristics, ecological processes, and service provision under changing environmental conditions.

Finally, it should be acknowledged that in coastal settings such as western Crete, landscape character and visual experience are often shaped by a continuum between terrestrial and marine environments. While the present study focuses on the land-based component of this continuum, future research could extend the proposed framework toward a coastal seascape perspective, integrating marine visibility, coastal waters, and land–sea interactions into landscape character and quality assessment.

5. Conclusions

In conclusion, beyond methodological implications, the results are of specific utility to planning and policy in Mediterranean island environments, such as western Crete. Given that the region is coming under intensified pressure from tourism-related development, the tension found between high scenic value and visual disturbance highlights the necessity for spatially precise protection measures. This need is reinforced by the current planning context, where landscape character is only partially addressed in local spatial plans. Coastal zones experience strong development pressure, yet municipal frameworks tend to regulate land use without incorporating visual or perceptual analyses. As a result, planners lack spatially explicit tools to anticipate and manage landscape impacts. The findings imply that planning interventions should balance the conservation of sites of high naturalness and low disturbance (southern hills and shrublands) with the careful management of visually and culturally prominent locations exposed to degradation pressures (northern beachfront areas).

The methodology developed in this study offers a reproducible model for incorporating landscape character and quality assessment into municipal land use planning, impact studies, and heritage-sensitive zoning initiatives, in correspondence with the ELC and emergent landscape governance agendas. The approach is reproducible because it relies on standard datasets (DEM, Corine Landcover) for the delineation of LDUs and LCTs, commonly used GIS indicators, and a uniform field-survey method. It can also be extended by incorporating additional indicators or participatory components whenever planning objectives require more social or ecological detail.

Moreover, the study area’s designation as a protected biodiversity zone reinforces the importance of integrating ecological priorities into planning. The results suggest that safeguarding habitats of high naturalness in the southern hills must be balanced with managing development pressures in visually and culturally prominent coastal areas. Linking LCA with biodiversity protection ensures that planning aligns with both ecological resilience and perceptual quality.

Looking ahead, future research will entail the integration of participatory approaches in further enhancing the assessment of landscape character and quality. In particular, a more extensive questionnaire will be distributed to both locals and visitors of the study area. This forthcoming phase is intended to collect a wider and more varied set of perceptual, emotional, and cultural landscape associations that go beyond the scope of expert-led field survey alone. Embedding these participatory processes within landscape assessment acknowledges that landscapes are dynamic socio-ecological systems, where human agency, collective memory, and place-based values play a decisive role in shaping both character and management priorities [46]. By engaging stakeholders themselves, the research will be able to discern how individuals encounter, value, and connect with particular landscape elements, along with their beliefs regarding development, conservation, and change. Such information will not only add interpretive richness to the LCA but also inform more inclusive and socially embedded planning approaches, strengthening the interface between scientific evaluation and local knowledge systems.