The Application of Landscape Indicators for Landscape Quality Assessment; Case of Zahleh, Lebanon

Abstract

Landscapes are vital systems where ecological, cultural, perceptual, and socio-economic values meet, making their quality assessment essential for sustainable development. Landscape Quality (LQ), shaped by the interaction of natural processes and human activities, remains methodologically challenging due to its interdisciplinarity and the need to integrate multiple dimensions. This challenge is particularly perceived in peri-urban areas, predominantly understudied in landscape research. This article addresses this gap in LQ assessment at peri-urban landscapes, through the case of Houch Al Oumaraa, Zahleh, a peri-urban area of patrimonial significance and agricultural landscape value. To evaluate the four spatial dimensions of LQ (structural, ecological, cultural and visual), we adopted a mixed methodology, where a pre-developed set of landscape indicators (LIs) applied within GIS and spatial technics, were supplemented by expert analysis through visual studies. Two questions framed this research: (i) is remote sensing sufficient to assess peri-urban LQ, and (ii) what are the limits of applying pre-developed LIs to diverse landscape contexts? Results show moderate fragmentation (CONTAG 61.6%), low diversity (MSDI 0.27), high density of cultural monuments (PROTAP 4.19) and average visual disharmony (FCDHI 0.49). Findings reveal that spatial dimensions alone are insufficient for assessing LQ of peri-urban landscapes, where socio-economic dimensions must also be integrated. Structural indicators (PLAND, MPA, ED, CONTAG) and MSDI proved transferable, while ECOLBAR was less applicable, cultural indicators (PROTAP, HLE) were limited to tangible heritage, and visual indicators (FCDHI, SDHI) highly context dependent. Establishing a differentiated yet standardized framework would not only enhance methodological precision but also ensure that LQ assessment remain relevant across diverse contexts, providing policymakers with actionable insights to align planning with sustainability goals.

1. Introduction

Defining landscape has always been challenging, due to the variety of disciplines concerned with landscape [1]. Referring to the European Landscape Convention (ELC), the landscape is an area as perceived by people, whose character is the outcome of the interdisciplinary interaction between human activities and natural processes [2]. As they continue to shape and characterize landscapes, there is a growing need for effective, integrative, and holistic approaches for assessing the quality of landscapes [3,4].

To date, the term landscape quality (LQ) lacks a universal definition [3]. It is a misconception that mirrors only the natural beauty [5,6], one of the various aspects as mentioned by Spage [7]. On the contrary, it is multi-faceted and could refer to the state in which the different landscape dimensions, i.e., structural, ecological, visual, cultural, social and economic, are found at a particular time [3,5]. The status of the quality of a given landscape plays a vital role in human well-being, both mentally and physically [8]. This recognition has led to a recent growing emphasis on assessing LQ in a holistic manner [7]. Importantly, the LQ is highly affected by the type, variety, and density of existing landscape elements that compose the landscape [3]. This explains the complexity in LQ assessment [9], originating from the various dimensions to be analyzed, hence the lack of studies that consider all LQ dimensions [5].

1.1. Multifunctionality of the Landscape

Landscapes are dynamic systems that deliver essential services with environmental, social, economic, ecological, and health benefits. Green infrastructure, for example, plays a central role in regulating microclimates by mitigating urban heat through shading and evapotranspiration, thereby lowering energy demand, and improving thermal comfort [10,11]. It can also regulate water flows, filter pollutants, and enhance climate resilience [12,13], while providing habitats and biodiversity corridors that strengthen ecological networks [14,15]. Beyond environmental functions, landscapes contribute directly to human well-being, as accessible green spaces promote physical activity, mental health, and community cohesion [16,17]. Well-designed landscapes can have economic benefits as well, such as raising property values, lowering energy bills, and reducing the need for gray infrastructure investments [18]. Because these functions are closely tied to the composition and condition of landscapes, assessing landscape quality becomes critical. Systematic evaluation allows us to identify strengths and vulnerabilities, monitor changes over time, and guide policies to ensure landscapes continue to provide their vital services sustainably [3,4].

1.2. Landscape Indicators: A Reliable Tool for Assessment

Effective assessment suggests the use of landscape indicators (LIs) as a tool that can measure aspects of the landscape [3,4,6] that do not offer direct measurement, through simple and explicit representations [3,4,6]. Also, LIs are expected to surpass conventional indicators [6,19,20] in reflecting all the dimensions of the landscape under study, and addressing landscape challenges [6,19,20]. Several authors stated that another complex issue is the existence of numerous indicators [12], thus they need to be effectively categorized to reduce the large amount of data [3,5]. This is why LIs are currently a prominent policy issue and are increasingly used in the assessment of various landscape values [4,20].

According to a recent review by Spage [7], the most frequently used indicators are related to landscape function, particularly land use and landscape elements, and landscape character including ecological parameters. On the contrary, cultural heritage attributes remain the least represented indicator group, highlighting a methodological gap [7,21]. To address this, our study integrates cultural indicators.

1.3. Landscape Quality: The Spatial Dimensions

Applying remote sensing techniques notably offers substantial saving in effort and time, relative to conventional field-based survey methods [22]. But, this applies only for the structural, ecological, cultural and visual dimensions of LQ [3,23]. These four dimensions define the scope of this study, which is explicitly limited to the spatial dimensions of LQ (Figure 1), and are described as follows:

i.  

Structural quality relies on landscape metrics that reveal the configuration and composition of the landscape under study. These metrics operate at the patch, class and landscape levels [3,24].

ii. 

Ecological quality is commonly assessed, where numerous author-defined indicators have been developed, making ecological metrics the most diverse group [3,20].

iii.

Cultural quality refers to the interactions between man and nature, creating the cultural identity for a community [3] but can also be reflected visually in the landscape (visual quality), not just in intangible identity. This can be expressed through features like agricultural terraces, historical land use patterns, and traditional architecture [6] or others.

iv.

Visual quality remains the most challenging. While visual perception is inherently subjective, research shows that certain landscape features, such as neglected buildings and unsightly billboards, evoke consistent responses across observers, allowing a more objective assessment [3,25].

By contrast, the economic and social dimensions of LQ usually rely solely on non-spatial, precise data collected from interviews, questionnaires, and participatory approaches [3,5] but are not addressed in this study.

1.4. LQ Assessment Methods: From Comparative Analysis to a Mixed Methodology

The Sowińska-Świerkosz and Michalik-Śnieżek methodology [5] is a multi-dimensional, indicator-based approach for assessing landscape quality (LQ) that integrates remote sensing and GIS with social perception data. This holistic method contrasts with traditional and specialized methods that often focus on a single aspect of LQ, such as visual quality or ecological function (

Table 1. Comparative analysis of LQ assessment methods.

Method	Sowińska-Świerkosz and Michalik-Śnieżek	Other Methods (e.g., Traditional, Specialized)	References
Data sources	Combines multiple data types, including remote sensing data, GIS, interviews, questionnaires, social surveys, statistical data, and visual studies	Often relies on a single data source, such as field surveys, public opinion polls, remote sensing data (e.g., LiDAR), specific environmental metrics, and many other techniques	[5,26,27]
LQ dimensions	Assesses LQ through a holistic framework that considers multiple dimensions: structural, ecological, cultural, visual, social and economic	Prioritize one or two dimensions of landscape quality (e.g., visual or ecological) while overlooking the integration of other critical dimensions and lacking a holistic framework	[3,7]
Objectivity vs. subjectivity	Balances objective, quantitative data from spatial analysis with subjective data from social perception studies	Can be heavily subjective (e.g., pure visual assessment based on expert judgment) or purely objective	[5,28]
Scale of application	Has been applied at the broader scale of natural or protected areas so far	Linked to the specific research objectives, which often prioritize detailed, site-specific analysis over a holistic assessment	[3,29]
Key Advantages	Holistic and provides a comprehensive understanding of LQ by integrating multiple dimensions and data types	Some methods are simpler to implement, requiring fewer resources and specialized knowledge	[5,25]
Adaptability	indicator-based framework can be adjusted to specific regional contexts	Well-suited for in-depth analysis of specific aspects as ecological connectivity and visual aesthetics	[3,30]
Gap/Limitation	requires diverse data, advanced GIS skills, social science expertise and highly dependent on availability and quality of detailed spatial and social data	Can face challenges in replicability and validity	[6,21]

).

Also, it provides a robust framework for landscape quality assessment by emphasizing a comprehensive, multi-dimensional approach that integrates objective spatial data with subjective social perceptions. While this methodology offers significant advantages in providing a holistic view of landscape quality and aligning with the fprinciples of the European Landscape Convention, its complexity and dependence on diverse data sources represent potential limitations. Other methods, while sometimes simpler or more focused on specific aspects, can suffer from limitations related to their narrower scope and potential lack of integration across different landscape dimensions.

1.5. Assessment of LQ Within Houch Al Oumaraa: A Peri-Urban Landscape

This study aims at assessing the LQ of Houch Al Oumaraa, Zahleh using spatial techniques. We investigated the potential of applying the set of spatial landscape indicators (LIs), previously developed by Sowińska-Świerkosz and Michalik-Śnieżek [3]. The analyzed area is classified as peri-urban, with more built-up than rural villages [31], yet still retains strong agricultural characteristics [32]. The focus on peri-urban landscapes through this site selection addresses a significant gap in the literature, as they remain underrepresented, constituting only 4% of existing landscape studies globally [21].

Peri-urban areas are critical to study because they embody the dynamic interface between rural and urban systems, where rapid population growth, and urban expansion reshape land use and settlement patterns. These zones are marked by contested land and resource use, as natural and agricultural lands are increasingly converted for residential, industrial, and commercial purposes, placing heavy pressure on water and land resources and threatening livelihoods. Hence, assessing their quality is essential to guide sustainable planning and ensure balanced rural–urban development [33].

We focused on the spatial dimensions to examine whether remote sensing alone could provide a reliable basis for assessing the landscape quality of Houch Al Oumaraa. In other landscape types, such as the protected area studied in Poland [3], spatial indicators proved sufficient, making it important to test their adequacy in a peri-urban context.

In addition to the above-mentioned methodology, considered as one of the few approaches that integrates the various dimensions of the landscape [5], our application in a new setting, generates region-specific insights relevant for both local management and broader comparative research. Knowing that the selected set of LIs, expected to provide an objective and quantitative framework for assessment, were originally developed in the context of Polish national parks [3], so their applicability may be limited to similar landscape types.

And instead of incorporating the human perception (public perception) through questionnaires, we have supplemented expert analysis with visual studies, to provide a more robust, multi-indicator framework that integrates objective spatial data with subjective human perception. As such, we may be able to capture the perceptual preferences of diverse communities.

This study, therefore, addresses the following questions: (a) To what extent can remote sensing data alone be relied upon to accurately assess the LQ of Houch Al Oumaraa? (b) What are the limitations of applying a set of landscape quality indicators, originally developed and validated within protected natural areas, to a peri-urban landscape context? Beyond this methodological contribution, the findings of this study will offer valuable insights for policymakers, planners, and practitioners in making agile decisions and designing effective measures tailored to peri-urban landscapes.

2. Materials and Methods

This study addresses the assessment of landscape quality of Houch Al Oumaraa, Zahleh, Lebanon, through a multidimensional and mixed methodology by using a set of spatial landscape indicators, expressing structural, ecological, cultural, and visual values [3]. A key distinction in this approach is the incorporation of social and visual assessments through expert analysis instead of questionnaires.

The research process was organized into five main stages, each building on the previous one to form a coherent methodological framework. Beginning with a review of existing studies to ground the selection of indicators, the process then moves through site selection and analysis, indicator calculation, and benchmarking against a case study. Finally, a critical reflection stage highlights both the strengths and limitations of this approach, ensuring transparency and offering directions for future applications. A visual overview of these interconnected stages is provided in Figure 2.

The following section details the study area selection, the analysis of land use and landcover, the specific indicators, and the case study (

Table 2. Four-phases sequential land focused approach.

Site Selection	Land Use/Landcover Analysis	Indicators	Case Study
Location	Classification of residential and built-up infrastructure	LIs Selection and calculation	Roztocze National Park, Poland
Site particularities	Occupation of industrial or commercial areas	New context of application	First application of developed LIs for LQ assessment
Identity features	Identification of green urban areas	Incorporation of social and visual assessments through expert analysis	Basis for comparative studies in other landscapes
Pace of undergoing urbanization examination	Recognition of main water sources, road network and other spatial contrast	Comparative analysis and elaboration of our mixed approach	Analysis based on Corine Land Cover classification map

).

2.1. Site Selection

Situated in the Bekaa Valley, Zahleh—once adopted within UNESCO Network of creative cities as a City of Gastronomy (since 21 October 2013) [34]—has long served as a hub for trade and cultural exchange, contributing to a distinctive and layered development of the anthropogenic landscape at the expense of the natural one. Historically, Zahleh has expanded around the Berdawni River, that played a major role in the development of the town [32].

This study focuses on Houch Al Oumaraa, a distinctive cadaster in Zahleh (Figure 3), situated between 900 and 1100 m above sea level [34]. The area encompasses a diverse mix of land uses, including urban neighborhoods, cultivated agricultural lands, and a developing industrial zone [35]. These different functions overlap and interact, creating a landscape that is both complex and dynamic within a compact area. This coexistence provides a valuable opportunity to explore how human activity can shape the character and quality of the landscape in distinct yet interconnected ways.

As such, the landscape of Houch Al Oumaraa exemplifies the multifunctional nature of peri-urban environments through its spatial and functional complexity [31,36], where the tension between development pressures and the preservation of ecological and cultural values becomes especially evident. Studying this setting is essential to establish an evidence-based policy framework that can provide important insights for balanced strategies that safeguard both environmental integrity and human well-being [36].

2.2. Site Analysis

The analysis was primarily based on the Corine Land Cover (CLC) classification, updated after 2022, obtained from the National Council for Scientific Research CNRS in Lebanon (Figure 3). This involved a detailed assessment of the urban fabric, focusing on the distribution and density of built-up areas. The presence of both high-density urban fabric and pockets of low-density or discontinuous fabric was evaluated to identify patterns of formal and informal urban development. Furthermore, the extent of industrial and commercial areas was analyzed to determine the degree of functional diversity. Also, integrated green spaces were identified, such as green urban areas and urban vacant lands, alongside persistence of agricultural land within the urban fabric. Finally, structural elements including the main water sources and the primary road network were recognized for their fundamental role in shaping the overall peri-urban form and connectivity.

In addition, the temporal dimension of land-use change was examined to identify pace of undergoing urbanization. Satellite images obtained from Google Earth Pro between the years 2004 and 2022 (Figure 4) reveal accelerated urban expansion that has largely reformed the land use structure over the past two decades.

Moreover, panoramic photographs were incorporated as part of visual landscape studies aimed at complementing spatial techniques. To capture contextual cues related to the visual dimension, elements that are inherently perceptual and not fully conveyed through satellite imagery or GIS data, panoramic photographs were taken in June 2025. The scene selection prioritized landscape diversity, taking into consideration the various land cover forms and the degree of human impact. Each image was captured with an 80° field of view, aligning with the optimal angle for human visual perception [25].The initial phase involved capturing of a set of thirty-five high-resolution panoramic photographs across the study area. Next, a rigorous refinement process was undertaken through a preliminary screening. The core criterion for selection was maximizing visual diversity, thereby strengthening the validity of the expert interpretations for each distinct typology. This process revealed a considerable degree of visual repetition in typologies among the initial photographs. To avoid redundant data and to optimize the expert evaluation process, a careful sampling strategy was employed. This led to the selection of a final, representative subset of twelve unique panoramas.

2.3. Selection and Calculation of Selected Landscape Indicators

The selection of indicators for this study was guided by a comprehensive literature review, which revealed that the first systematic attempt to address the multiple dimensions of LQ was undertaken by Sowińska-Świerkosz and Michalik-Śnieżek [3]. Their framework proposed a set of eleven spatial indicators explicitly linked to structural, ecological, visual, and cultural dimensions of LQ, and has since provided a methodological reference point in the field.

Building on this foundation, our research adopted these indicators, while also acknowledging the need to test their transferability beyond the protected natural areas for which they were originally designed and validated. This approach reflects the critical opportunity to identify limitations when applied to a peri-urban context such as Houch Al Oumaraa. The analysis of the selected indicators was carried out using Geographic Information System (GIS) software, specifically ArcGIS Pro 3.0.1. All indicators were analyzed based on Corine Land Cover classification updated after 2022.

For easier reference to each indicator, a simple alphanumeric coding system is used to reflect the dimension and the number of the indicator within that dimension (

Table 3. Summary of adopted landscape indicators.

Dimension		Indicator	Indicator Name	Data Type	Tool	Source
Structural Quality	S1	PLAND	Percentage of landscape occupied by the class of the highest share	Spatial	ArcGIS Pro Fragstats	[3,24]
	S2	MPA	Mean Patch Area
	S3	ED	Edge Density
	S4	CONTAG	Contagion
Ecological Quality	E1	MSDI	Modified Shannon Diversity Index	Spatial	ArcGIS Pro	[3,37]
	E2	ECOLBAR	Ecological Barriers
Cultural Quality	C1	PROTAP	Historical Monuments	Spatial	ArcGIS Pro	[3]
	C2	HLE	Historical Landscape Elements
Visual Quality	V1	PLE	Positive Landscape Elements	Spatial Photographs	ArcGIS Pro Landscape visual studies	[3,25]
	V2	FCDHI	Form and Color Disharmony Index
	V3	SDHI	Shape Disharmony Index

).

2.4. Benchmarking Landscape Quality Through a Case Study: Roztocze National Park

Roztocze National Park, a protected natural area in Poland, was selected by Sowińska-Świerkosz to be the first area to apply the developed set of LI for assessing its LQ [3], laying the basis for future comparative applications in other landscape types. The studied area was analyzed based on Corine Land Cover (2018) classification map.

3. Results

3.1. Historical Landcover Change

Satellite images obtained from Google Earth Pro between the years 2004 and 2022 reveal an accelerated urban expansion that has profoundly reshaped the land use structure over the past two decades (

Table 4. Comparison of urban and agricultural areas coverage between 2004 and 2002.

Year	Built-Up	Agricultural Areas
2004	46%	53.50%
2022	54.40%	19%

). In 2004, urbanized zones accounted for 46.6% of the total area, while agricultural lands still covered 53.5%, reflecting their coexistence in a relative balance. By 2022, this balance has been shifted dramatically. The urban development has expanded to 54.5%, while agricultural lands declined to just 19%, meaning a concerning decline in productive lands.

The transformation was primarily driven by rapid population growth, increased housing demand, and the expansion of industrial and commercial activities, which collectively exerted pressure on available agricultural lands. Also, economic incentives and weak land use regulations and limited urban planning accelerated urban expansion.

Beyond the ecological and economic impacts, such as the erosion of agricultural livelihoods, loss of local food security, and landscape fragmentation, this transformation also affects the region’s social fabric and cultural identity. To illustrate, the decrease in agricultural lands means not only a weakening of traditional rural identity but also a gradual detachment of communities from their agricultural heritage, once been central to local life. Thus, accelerated spread and industrial growth are threatening the peri-urban character that defines Houch Al Oumaraa’s identity.

3.2. Current Land Use/Landcover

This study area shows how multiple land uses can coexist within a relatively compact space, shaping both its character and overall quality. The urban neighborhoods introduce dense built-up structures, road networks, and infrastructure that impact both the visual character and ecological balance of the area. Alongside, the cultivated agricultural lands contribute not only to local livelihoods and productivity but also preserve patches of green open spaces that buffer against the urban expansion. At the same time, the presence of a developing industrial zone adds another layer of complexity. While it brings economic opportunities and jobs, it also introduces visual disharmony and potential environmental pressures, such as air or soil pollution. Together, these multiple functions—urban, agricultural, and industrial—shape a landscape that is both dynamic and vulnerable. This mix is typical of peri-urban landscapes, where competing demands must be carefully managed to maintain overall landscape quality.

According to Corine Land Cover classification map (Figure 3), dense (48.3%) and medium-density urban fabric (6.2%) dominate the central and northern parts of the area, indicating a consolidated urban core with residential and built-up infrastructure. Pockets of low-density (14.5%) and informal urban fabric (0.4%) at the periphery of Houch Al Oumaraa, particularly along the eastern and southern edges adjacent to agricultural lands, reflects patterns of unregulated expansion. These areas are closely tied to the residence of Syrian refugees, many of whom depend on seasonal agricultural work for their livelihoods. This settlement pattern highlights a dynamic interaction between social vulnerability and land use, where the demand for affordable housing has translated into informal development on the rural fringe. Although these areas address immediate needs for housing and agricultural labor for refugees, they also raise questions about planning oversight, service provision, and the long-term integration of refugee communities.

A portion of land is occupied by industrial or commercial areas (3.7%), primarily in the southeastern sector, reinforcing the area’s functional diversity. Green urban areas (2.5%) and urban vacant lands (1.7%) are scattered within the built-up zones, offering limited but essential open spaces and potential for urban greening. The agricultural lands are evident along the southern and northern borders, where field crops in medium to large terraces (10%) and fruit tree orchards (9%) exist. These cultivated zones coexist alongside the urban environment, highlighting the transitional nature of land use typically for a peri-urban landscape.

The Berdawni River (0.5%), which flows centrally, is the main water source used for domestic and agricultural supply as well as the most important natural touristic symbol in the area. The Chtoura-Zahleh Highway (3.2%) divides the area, creating a clear spatial contrast: the eastern side is predominantly agricultural, while the western side is largely occupied by built-up urban fabric.

3.3. Calculation of Selected Landscape Indicators

3.3.1. Structural Quality

After, rasterization of obtained Corine Land Cover vector data using ArcGIS Pro at a 5 m resolution, the structural quality for Houch Al Oumaraa is calculated by means of four landscape metrics: PLAND, MPA, ED, and CONTAG, computed using Fragstats software (version 4.2.681).

The calculation of the selected landscape metrics revealed that the highest value of PLAND is 48.30%, indicating that the landscape is dominated by a single class, the dense urban fabric, which has the highest share of the total landscape according to Corine Landcover Classification.

The value of MPA is 4.62 ha and that of ED is 104.92 m/ha are considered low to moderate in this case. Considering that the dense urban fabric occupies approximately half of the landscape, these values explain that most of the remaining patches are slightly smaller areas. In addition, the CONTAG has a value of 61.60%, indicating moderate contagion. This value aligns with the results obtained for both MPA and ED, confirming moderate fragmentation.

3.3.2. Ecological Quality

Of the adopted ecological indicators, only MSDI is calculated while ECOLBAR was excluded for being unapplicable. The latter considers only existing paved roads and railways crossing sensitive habitats like meadows, peat bogs, and forests as ecological barriers, which is not the case of Houch Al Oumaraa.

The calculation of MSDI involves assigning for each land cover class a quality score (I₁) ranging from 0 to 1, that considers the ecological value of each land cover category, assuming that less human-modified environments provide more suitable habitats for species and therefore have higher ecological value.

The obtained value for MSDI is 0.27, indicating low diversity. This is expected due to the dominance of anthropogenic, modified land covers.

3.3.3. Cultural Quality

PROTAP is used to assess the cultural richness and heritage value of the landscape, expressed by the official count of both cultural and religious monuments. In addition, HLE requires historical landscape elements—such as historical parks, gardens, cemeteries, and avenue of trees—to be identified, mapped, and their areas are calculated.

The value of PROTAP is 4.19, indicating high density of monuments. Mapping the 6 identified monuments allows us to highlight the fact that they exist only within the extent of the northern urban areas, which is expected since built heritage is usually the result of anthropogenic activities.

On the contrary, the value of HLE remains low, having a value of 0.03, although there are 8 historical landscape elements. This is explained by the fact that cultural assets are concentrated but have limited areas.

3.3.4. Visual Quality

The calculation of PLE involves the classification of landcover classes according to the impact on visual quality, where land cover forms perceived as positive by the majority of people are water, natural and semi-natural forms of vegetation. Panoramic photographs were incorporated as part of visual landscape studies for the calculation of FCDHI, aimed at complementing the spatial techniques to capture contextual cues related to perception. Thus, 12 panoramic photographs were taken in June 2025, with an 80° field of view aligning with the optimal angle for human visual perception.

The scene selection prioritized landscape typology, taking into consideration the various land cover forms and the degree of anthropogenic impact. The objective analysis of photographs was made possible through the detection and marking of disharmonious objects according to the criteria developed by Sowińska-Świerkosz. For SDHI, two variables are combined: (1) the proportion of land cover types, and (2) their shape complexity, measured by the Fractal Dimension Index (FRAC) which is calculated using Fragstats software, version 4.2.681 (2023).

PLE has a value of 0.03, meaning that only 3% of the landscape comprises visually positive elements. The obtained value of FCDHI is 0.49, revealing moderate disharmony in form/color and average visual coherence. Moreover, the value of SDHI is 0.32, which indicates low to moderate shape disharmony favoring the mosaic structure of the landscape (

Table 5. Indicator results for Houch Al Oumaraa.

Dimension		Indicator	Result	Visualization
Structural Quality	S1	PLAND	48.30%
	S2	MPA	4.62 ha
	S3	ED	104.92 m/ha
	S4	CONTAG	61.60%
Ecological Quality	E1	MSDI	0.27
	E2	ECOLBAR	N/A
Cultural Quality	C1	PROTAP	4.19 monument/km2
	C2	HLE	0.03
Visual Quality	V1	PLE	0.03
	V2	FCDHI	0.49
	V3	SDHI	0.32

).

3.4. Case Study Results

Roztocze National Park was also analyzed based on Corine Land Cover (2018) classification map. The results (

Table 6. Indicator results for Roztocze National Park.

Dimension		Indicator	Result
Structural Quality	S1	PLAND	35.96%
	S2	MPA	158.4 km2
	S3	ED	24.19 m/m2
	S4	CONTAG	67.75%
Ecological Quality	E1	MSDI	0.77
	E2	ECOLBAR	2.93 km/km2
Cultural Quality	C1	PROTAP	0.09 monument/km2
	C2	HLE	0.002
Visual Quality	V1	PLE	0.70
	V2	FCDHI	0.60
	V3	SDHI	0.15

) showed high structural, ecological, and visual values, due to the minimal human disturbance and stable ecological state of this protected area. The low value of cultural heritage reflects the area’s predominantly natural and semi-natural land cover forms.

4. Discussion

4.1. Landscape Quality of Houch Al Oumaraa

The value of PLAND (48.30%) indicates that nearly half of landscape is dominated by the dense urban fabric class. This indicated a relatively high degree of urbanization [35] and the need for constant monitoring, while retaining a balanced mix of land uses in the other half of the landscape, which requires monitoring to prevent the overdominance of rapid urbanization.

The MPA (4.62 ha) suggests a moderate patch size, implying that the landscape is not excessively fragmented. Moreover, the medium to high ED (104.92 m/ha) indicates that numerous boundaries exist between land cover types. This is a characteristic of peri-urban areas where urbanized areas are bounded with agricultural or natural lands, creating many edges. The moderate to high CONTAG (61.60%) further supports this interpretation, reflecting a balance between clustered and dispersed patterns, which is typical of peri-urban areas.

These results clearly show that Houch Al Oumaraa retains average structural quality, indicating moderate fragmentation resulting from three main reasons: (i) the variety of the remaining patch types, which is typical for peri-urban areas, (ii) the impact of the road infrastructure, primarily the highway that separates the urban setting from the rural and (iii) the expansion of urban areas which, if not controlled properly, would impact the whole landscape by turning it entirely urban [31].

The MSDI value (0.27) suggests a relatively low ecological diversity when weighted by the ecological significance of different land cover forms. This value indicates that the landscape lacks a high proportion of ecologically significant natural or semi-natural vegetation. This aligns with observations of peri-urban expansion often leading to the reduction or degradation of natural habitats [31]. Therefore, the overall ecological quality is low, and it is compromised by human modification. This is crucial to identify peri-urban areas to help policy planning for natural land reclamation.

The PROTAP value (4.19 monuments/km²) indicates a notable density of cultural monuments, as the area possesses significant embedded heritage [34], which contributes to its landscape identity. However, the relatively low HLE value (0.03) indicates that while monuments exist, large, organized historical landscape features are limited. This might suggest that cultural heritage is not broadly integrated into green spaces. As a result, the cultural quality of Houch Al Oumaraa could be described as mid-to-high, having a strong cultural heritage, but remaining restricted within the landscape.

The very low PLE value (0.03) suggests that a minimal portion of the total area contributes positively to landscape quality. This is explained by the lack of well-maintained natural features, reflecting aesthetic degradation from urbanization. The FCDHI value (0.49) indicates an average level of disharmony in the form or color of anthropogenic objects. This generally results from unplanned urban structures. The low SDHI (0.32) further suggests some level of shape disharmony, implying moderate, irregular fragmentation and disturbance of natural shapes due to human activities. Collectively, these visual indicators suggest medium visual quality, influenced by urban development patterns in Houch Al Oumaraa, that can negatively impact its overall visual appeal and coherence.

In few words, visual quality is affected by unnatural elements, with a low percentage (3%) of positive landscape elements, highlighting the need for intervention to improve the area’s aesthetics. It is suggested that a set of landscape indicators tailored to the specific characteristics of peri-urban areas be standardized to facilitate effective assessment and planning.

4.2. Understanding Landscape Quality by Comparison

To better understand the landscape quality status of our peri-urban landscape, a comparison is conducted with a protected area, namely Roztocze National Park [3]. Protected areas can be perceived as benchmarks due to their limited human interventions, ecological stability, and regulated development [38]. By placing Houch Al Oumaraa, a human-influenced and functionally mixed landscape, beside such a benchmark, we can reveal not only impacts of urban pressures but also unexpected strengths of peri-urban landscapes. This contrast helps contextualize the results of the indicators, allowing us to assess where human presence has positively or negatively impacted the landscape quality. Such a comparison also directs planners and policymakers towards most needed intervention measures and offers an evidence-based approach to guiding sustainable development in peri-urban landscapes.

Structurally, Houch Al Oumaraa demonstrated lower performance than the protected area. Although Roztocze National Park had a larger MPA, which was expected due to its undisturbed natural areas, still the peri-urban area showed a more dynamic and mosaic-like composition due to the mixed, converging functions which characterize peri-urban areas [31,36]. This divergence is not merely a numerical difference but a fundamental reflection of each landscape’s purpose. The high MPA and low ED in Roztocze are intrinsic to its conservation efforts, favoring ecological integrity. Conversely, the moderate MPA and higher ED in Houch Al Oumaraa are inherent to its peri-urban function, representing a necessary interface of land uses. This implies that structural indicators like MPA and ED are universally applicable for description but are not universally prescriptive, meaning that a high ED is a negative indicator in a national park but a neutral or even expected one in a peri-urban context. Thus, the system must adapt its interpretation of these values based on landscape type. Hence, it is recommended to integrate strict land-use zoning to preserve heterogeneity while controlling urban sprawl. This can help sustain both agricultural productivity and urban livability.

Ecologically, the protected area has a higher quality, as anticipated. Its ecological indicators reflect less human intervention and a more stable habitat. However, it is important to note that although the peri-urban landscape is more fragmented, yet it maintained a modest ecological status and could benefit from targeted green infrastructure improvements [39]. The stark difference in MSDI values highlights a fundamental contrast in ecological character between the two landscapes. The MSDI, weighted towards natural vegetation, correctly scored the peri-urban environment of Houch Al Oumaraa lower than the protected area. This accurately reflects the landscape’s reality, i.e., a reduced proportion of ecologically significant natural cover due to urbanization. Therefore, the MSDI proves to be an applicable indicator for assessing the ecological dimension of landscape quality in peri-urban contexts, serving as a reliable measure of the penetration and health of natural systems within the human-dominated area. This suggests prioritizing the integration of green corridors and greenbelts while ensuring that urban expansion does not compromise biodiversity potential.

Culturally, the results of the peri-urban area were promising. Houch Al Oumaraa has a much higher density of monuments (including culinary monuments and wineries) and historical landscape elements, highlighting its deep cultural roots and the layered human presence embedded within it. The area has a high density of monuments (4.19 monuments/km²) but presents limitations in organized historical elements, suggesting that cultural identity may not be well integrated into green spaces. On the contrary, the protected area had much lower values, due to the exclusion from human activities for many years. The low PROTAP and HLE values in Roztocze support the goal of minimizing human impact. The high values in Houch Al Oumaraa correctly highlight its cultural assets. However, this comparison also exposes a systems-level weakness, as these two indicators only capture formal, tangible heritage. They fail to capture the intangible cultural value of biodiversity in Roztocze or the everyday cultural practices in Houch Al Oumaraa. Policymakers can build on this by adopting heritage-sensitive planning approaches and promoting cultural tourism through community-based initiatives such as cultural heritage trails.

Visual quality presented a challenging performance. The protected area scored higher in PLE, due to the dominance of natural features. However, the peri-urban area showed lower scores in form and color disharmony, indicating the existence of efforts to control visual disharmony. The difference in visual scores reveals how perception is shaped by expectation. The high PLE in Roztocze is based on the universal positive perception of naturalness. The peri-urban area’s lower PLE score does not mean it is visually poor, but that its beauty is more complex and relies on smart design and maintenance (e.g., well-kept historical buildings, clean streets). The fact that its FCDHI was not higher suggests that visual harmony can be achieved even in built environments since their interpretation is deeply context-dependent. This implies that visual indicators must be calibrated through local public perception studies to avoid a bias towards natural landscapes and to accurately capture the qualities valued by the people who live in peri-urban areas. Hence, enforcing local design guidelines and integrating public green spaces could be suggested to enhance scenic quality in peri-urban contexts.

This comparison challenges the conventional expectations, showing that peri-urban areas like Houch Al Oumaraa are not just transitional zones—they are living areas that are complex, layered, multifunctional, and can sometimes surpass traditional protected areas in terms of cultural richness. Most importantly, it provides a critical test of the indicator system’s universality. The system performs well in quantifying differences but requires significant adaptation to interpret them fairly across contexts. Its applicability depends on moving from a rigid, one-size-fits-all framework to a flexible tool where indicators are selected, interpreted, and weighted based on the specific functions and values of the landscape being assessed. By adopting context-specific policies for each dimension of landscape quality, peri-urban landscapes like Houch Al Oumaraa can serve as models for sustainable and resilient development [36].

4.3. Strengths, Limitations, and Prospects

The landscape indicator system applied in this study was originally developed and validated within the context of protected areas, specifically Polish national parks [3]. Its development represented an important advancement in landscape quality (LQ) research, as it was among the first attempts to integrate multiple dimensions—structural, ecological, cultural, and visual—into a structured, spatially explicit framework. Earlier assessments often focused on a single dimension, such as aesthetics or ecology, whereas this system provided a replicable, GIS-based methodology that could quantify and map several aspects of landscape quality in a coherent manner [22].

Our contribution, however, lies in critically testing the system’s transferability to a more complex and multifunctional peri-urban setting. The results show that while the framework provides a strong foundation, it is not yet sufficiently developed for universal application. Certain measures describing landscape structure and configuration (e.g., PLAND, MPA, ED, CONTAG) demonstrated high transferability, confirming their robustness across diverse geographic contexts. However, critical limitations were exposed in other indicators, highlighting where the system’s development must evolve:

i. 

The ECOLBAR indicator, while well-suited to natural and protected landscapes, proved inapplicable within the peri-urban context of our study, where ecological values compete with social and economic functions.

ii.

The cultural indicators (PROTAP, HLE) are limited to formal, tangible heritage such as monuments and historical elements, overlooking the intangible cultural values and everyday landscapes that are central to peri-urban identity [5].

These findings highlight that the system, while advanced in its methodology and multidimensional scope, remains under development when applied beyond natural landscapes. Importantly, the analysis reveals a limitation in applying a GIS-based indicator framework to peri-urban landscapes. Spatial dimensions alone proved insufficient to fully conclude on overall landscape quality as they fail to account for their social and economic multifunctionality of peri-urban areas, where social and economic dynamics play a crucial role in peri-urban landscapes. This aligns with previous studies showing that reliance on satellite imagery alone may lead to misinterpretation of indicators [40]. Therefore, to advance this methodology, future development of the indicator system must include:

i. 

Adapting or replacing indicators like ECOLBAR to reflect peri-urban context.

ii.

Integrating indicators such as land and property value dynamics, employment opportunities in the various sectors, and household income diversification to reflect on the economic dimension.

Similarly, including social indicators like access to public services, community cohesion, population density, and residents’ perceptions could provide valuable insights into how people experience and value their surroundings. Therefore, this study positions the indicator system not as a fully mature or universal tool but as a developing framework. Its application in Houch Al Oumaraa has helped clarify both its strengths and limitations, offering a roadmap for necessary refinement. This research contributes directly to the continued development of the system and underscores the need for more integrative, context-sensitive approaches to landscape quality assessment.

5. Conclusions

This research investigated the landscape quality in Houch Al Oumaraa, Zahleh, Lebanon, using a set of landscape indicators through geospatial analysis techniques, showing the importance of assessing LQ in peri-urban areas, predominately understudied. It highlighted that LQ is not restricted to natural elements but also to the cultural significance and socioeconomic context of the area. Although some indicators are universal and applicable across diverse geographical contexts, the need for a standard and differentiated framework is crucial to capture the complexity of LQ.

The results also showed a sustained strong cultural quality, despite the significant anthropogenic transformation that threatens such a peri-urban landscape. Moreover, the structural indicators reveal a moderate level of fragmentation, and low ecological diversity, raising concerns about the sustainability of habitats in the peri-urban area. The visual quality, however, is affected by disharmonious anthropogenic elements and the low share of positive landscape features.

Considering these findings in future planning is a must to help enhance the ecological diversity and ensure visually harmonious growth. This would involve strategies for the integration of green infrastructure, smart urban design, and the preservation of remaining natural and cultural landscape assets.

Although the four spatial dimensions studied in this research were partially sufficient to conclude on the overall LQ of Houch Al Oumaraa, yet this peri-urban area is characterized by its mix of various land uses such as the urbanized parts, industrial zones, agricultural lands, and natural areas —within a limited extent. Hence, future research should take into consideration the assessment of the additional economic and social dimensions, that are non-spatial, to fully reflect the LQ of this multifunctional area.

A good way forward is to develop standardized sets of landscape indicators tailored to each landscape type. For instance, natural protected areas could rely predominantly on the adopted set of spatial LI, while peri-urban landscapes require the integration of non-spatial dimensions in addition to the spatial ones to capture their dynamic and multifunctional character. Establishing such differentiated, yet standardized, frameworks would not only enhance methodological precision but also ensure that landscape quality assessments remain relevant across diverse contexts, providing decision-makers with more actionable insights.

Finally, it is recommended to apply this set of LIs on other peri-urban areas and compare their results with Houch Al Oumaraa to better evaluate these vulnerable landscapes and incorporate holistic approaches for better territorial planning.