Introduction

The high aesthetic value of landscape (Landscape Aesthetic Quality, LAQ), as one of the important ecosystem services, exerts a direct influence on human health and well-being and provides the groundwork for numerous outdoor activities. Landscape can be regarded as a valuable resource in the development of human land uses, given that any spatial development undertaken without a rigorous assessment of its aesthetic impacts may diminish the aesthetic value of the environment. Owing to the diversity of natural conditions and land uses, various landscape types—including coastal, forest, urban, agricultural, mountainous, and plain landscapes—emerge across different territorial units, each contributing, through its distinct natural and structural elements, to the formation of visual quality and scenic attractiveness. A review of the relevant literature indicates that studies in this field can be classified into three main categories: objective approaches, which analyze the physical and structural characteristics of the environment using tools such as GIS); perceptual approaches, which focus on observers’ subjective experience and perceptions, typically assessed through questionnaires and methods such as Principal Component Analysis; and integrated approaches, which combining objective and perceptual indicators). Findings from prior studies suggested that integrated approaches provide a more effective tool for the analysis and management of natural landscapes. Accordingly, the present study aims to employ an objective assessment approach and develop a practical framework for modeling and zoning the aesthetic value of different landscape types in Bandar-e Gaz County. Studies adopting the objective approach typically identify areas of high visual value by analyzing structural elements such as slope, elevation, land use, and vegetation cover (Fourier, 2005), and several have combined GIS with the weighted linear combination method to prepare aesthetic zoning maps at the regional scale. At the national scale, Hermes et al. (2018) identified landscape diversity as the most decisive indicator of visual attractiveness. In contrast, perceptual studies emphasize users’ experience and criteria such as naturalness, cleanliness, diversity, and scenic appeal, and have shown that aesthetic perception is shaped by subjective and emotional factors as well as biophysical characteristics (Beza, 2010; Arriaza et al., 2004). Integrated studies, such as Frank et al. (2013) and Guo et al. (2026), validated objective assessments against human perception and showed that coupling the two approaches reduces the bias of each method. Regions with greater diversity of landscape types and natural elements generally show higher aesthetic value (Zheng et al., 2024), which justifies assessing each landscape type separately. Such assessments also support the identification of areas with recreational and tourism potential and the protection of valuable natural landscapes. The present study therefore adopts an objective approach in a GIS environment in order to offer a rapid, simple, and reproducible method that can be used in management decisions.



Methodology

The study area is Bandar-e Gaz County, located in Golestan Province, which was selected as the case study owing to its considerable diversity of landscape types—namely coastal, plain, agricultural-orchard, forested, and mountainous—as well as its approximately 20-kilometer coastal strip and more than 30,000 hectares of forest parks and natural reserves. An MCE process based on Weighted Linear Combination (WLC) was implemented in six steps using ArcGIS 10.2 and IDRISI TerrSet 2020: (1) criteria identification for each landscape type via literature review and field surveys; (2) criteria/constraint mapping (buildings, residential areas, and paved roads as negative factors); (3) fuzzification and standardization to a 0–255 range; (4) AHP weighting based on judgments from 10 Gorgan University experts; (5) layer integration via WLC (Eastman, 1995), valued for factor compensability; and (6) final high-aesthetic-value zoning using Zonal Land Suitability (ZLS) in the macro modeler. Bandar-e Gaz lies in the southwestern part of Golestan Province and is bordered by the Gorgan Bay and the Caspian Sea to the north, Galugah County (Mazandaran Province) to the south and west, and Kordkuy County to the east. Vegetation density and diversity were derived from the red and near-infrared bands of Landsat 8 OLI imagery acquired in June 2024, a period of peak vegetative growth and maximum canopy cover, while slope and elevation were extracted from an ALOS PALSAR digital elevation model with a spatial resolution of 12.5 m. Criteria were standardized with linear fuzzy functions and, for criteria with only presence or absence states, user-defined functions. The number of criteria differed among landscape types, from five in the agricultural-orchard landscape to thirteen in the mountainous landscape. The AHP procedure comprised pairwise comparison matrices, criteria weighting, and estimation of the inconsistency ratio, and ZLS, a non-pixel-based polygon method, converted the continuous WLC output into actionable zones. AHP decomposes a decision problem into several levels that form a hierarchy, allowing criteria to be scored, weighted, and ranked by experts and stakeholders. Constraints, namely buildings, residential areas, and paved roads, were identified from expert opinion, field studies, and the literature as having the strongest negative effect on aesthetic value.



Results

An AHP inconsistency ratio of 0.028% confirmed weighting validity and required no revision of expert judgments. Following the integration of the fuzzy standardized layers using the WLC method, aesthetic quality maps were generated for each of the five landscape types within a range of 0 to 255, whereby higher values indicate greater visual desirability. In the zoning stage, a suitability threshold of 150 and a minimum patch size of one hectare were applied using the ZLS method, and areas meeting these criteria were identified as zones of high aesthetic value. The results of criteria weighting revealed that the relative importance of the factors influencing aesthetic value differed across landscape types: in the coastal landscape, visibility and quality of sea view emerged as the most influential factor; in the plain and agricultural-orchard landscapes, vegetation density and diversity played the predominant role; and in the forested and mountainous landscapes, a combination of topographic variables, structural diversity of vegetation cover, and the presence of distinctive natural elements—such as rivers, waterfalls, and peaks—exerted the greatest influence. Furthermore, areas of high aesthetic value were predominantly concentrated along visual corridors offering views of the sea and rivers and in regions with dense vegetation cover, whereas anthropogenic interventions were identified as one of the most significant factors contributing to the degradation of visual quality across all landscape types. In the coastal landscape, sea-view visibility received the highest weight (0.24), followed by river visibility and bird biodiversity hotspots (0.16 each). In the plain landscape, vegetation density (0.24) ranked first, and in the agricultural-orchard landscape it dominated clearly (0.33), followed by river visibility (0.20). In the forested landscape, vegetation density (0.16) and sea-view visibility (0.13) ranked highest, whereas in the mountainous landscape the weights were more evenly distributed (0.04–0.11). The ZLS analysis showed that 176.64 ha (15.77%) of the coastal landscape, 3,611.33 ha (27.58%) of the agricultural-orchard landscape, and 5,567.20 ha (38.89%) of the mountainous landscape qualified as high-aesthetic-value zones. In a sensitivity analysis, raising the threshold from 150 to 200 reduced the selected areas to 1.22, 188.27, and 206.23 ha, respectively, indicating that the zoning outcome is highly sensitive to the threshold. A threshold of 100 was not considered because it would include moderate rather than high aesthetic values. The mountainous landscape thus devoted the largest relative share of its area to high-value zones, which may reflect the greater extent and diversity of effective natural elements, such as topography, dense vegetation, and distinctive natural features, compared with the coastal and agricultural-orchard landscapes.



Discussion

The findings demonstrate that the concurrent application of GIS and multi-criteria decision-making models aligns with recent trends in visual landscape quality assessment, which emphasize spatial modeling and data-driven analysis. The prominence of sea view as the dominant criterion in the coastal landscape is consistent with the intrinsic nature of such landscapes and corroborates prior research on the pivotal role of water elements and open horizons in shaping perceived visual desirability. Moreover, the synergistic importance of river visibility and avian biodiversity hotspots indicates that the aesthetic value of the coastal zone depends on a network of complementary elements, rather than solely on the land–sea interface. In the plain and agricultural-orchard landscapes, the predominance of vegetation density as a criterion is consistent with studies emphasizing the role of vegetation texture and biodiversity in enhancing visual value. In the forested and mountainous landscapes, the more multidimensional weighting pattern—encompassing topography, vegetation cover, bedrock, and distinctive landmark elements—is in alignment with the literature on forest aesthetics and recreational landscape management. The compensatory nature of the WLC method further implies that, in management applications, the interpretation of high-value zones should be accompanied by field verification and conservation considerations. The AHP–WLC framework with fuzzy standardization has been described as an efficient way to integrate heterogeneous ecological, socio-economic, and visual criteria. The importance of sea view agrees with Ergin et al. (2004), Morgan (1999), Anfuso et al. (2014), and Mooser et al. (2021), who identified sea-view quality, dominant natural elements, and low construction disturbance as key coastal criteria, while the importance of vegetation density agrees with Arriaza et al. (2004) and Dudek (2018). Treating bedrock as an independent criterion in the mountainous landscape is methodologically important, because rock outcrops create prominent forms and visual contrast (Schirpke et al., 2013). Finally, defining thresholds and a minimum patch size in ZLS turns pixel-based output into zones usable for locating recreation areas, tourism routes, and conservation priorities. . In relatively flat landscapes with low geological diversity, vegetation density, the continuity of vegetation patches, and the vitality of the landscape matter most, which is also consistent with views that treat place identity and cultural meaning as basic components of landscape experience alongside visual quality (Zhang and Xu, 2020; Despotovic and Hauser, 2025).



Conclusion

The findings indicate that the assessment of landscape aesthetic value in areas characterized by high landscape diversity results from the interaction of a range of ecological factors, visual elements, and the intensity of anthropogenic interventions, rather than from any single or limited set of physical indicators. In Bandar-e Gaz County, the aesthetic quality of the coastal landscape is primarily shaped by visual axes toward the sea; in the plain and agricultural-orchard landscapes, by vegetation vitality and density; and in the forested and mountainous landscapes, by a combination of geology, vegetation cover, and distinctive visible elements. Accordingly, landscape management in areas of high landscape diversity should simultaneously concentrate on the protection of visual corridors, the enhancement of green-patch connectivity, and the regulation of anthropogenic development within high-value zones. The framework can serve as a practical basis for land-use planning and management authorities in identifying sensitive zones, prioritizing conservation efforts, and guiding the development of nature-based tourism. Practical recommendations include using the zoning maps as base layers in comprehensive and detailed land-use plans, treating sea and river view corridors as aesthetically sensitive zones in construction regulations, preventing fragmentation of farmland and orchards, and protecting rivers, waterfalls, and peaks while managing access routes. Future studies should integrate this framework with perceptual evaluations such as SBE–GIS models, use drone imagery and viewshed tools to improve the spatial accuracy of visibility criteria, add visual pollution indicators as an independent criterion, and extend the method to other counties. In short, the expansion of human-made elements and the loss of continuity and quality of natural cover reduce the aesthetic value of all landscape types.