Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting

Kerebel, Anthony; Gélinas, Nancy; Déry, Steve; Voigt, Brian; Munson, Alison D.

doi:10.1016/j.landurbplan.2019.02.001

Cited by 51 publications

(22 citation statements)

References 70 publications

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“…A survey was conducted in which respondents were asked to rank landscape features. The criterion weights were calculated according to a landscape aesthetic model (Kerebel et al, 2019). The values of the LA capacity were mapped onto a 200 × 200 m grid.…”

Section: Mapping Landscape Aesthetics Capacitymentioning

confidence: 99%

Examination of Urban Landscape Aesthetics through Mapping Using Geotagged Social Media Data from Flickr

Zhao¹,

Han²

2021

CUS

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Landscapes are part of the earth's surface, and include terrestrial, aquatic, natural, and anthropogenic features with differing dominance. The evaluation of Landscape Aesthetics (LA) and scenery is helpful for the understanding landscapes. The mapping of LA is increasingly being recognized in urban and landscape planning. Examining the flow and capacity of LA is an essential element in understanding the sustainability of urban landscapes. An explicit methodological approach was proposed for mapping LA using social media data (geolocated photographs) from Flickr to study the spatial distribution of LA in Xi'an, China. The LA flow and LA capacity were conceptually distinguished and assessed. The LA flow was analyzed with 10,278 geotagged photographs from Flickr to express people's actual appreciation of LA. The LA capacity was examined by spatial criterion evaluation, combined the weight of landscape features. LA flow and LA capacity differ in both spatial extent and quantity, although there was a spatially explicit balance between them. The results show that LA flow largely occurs in the urban core zone and that LA capacity is concentrated in certain parts of Xi'an due to the imbalanced distribution of historical cultural heritage sites. This research contributes to a better understanding of LA values and the relationships between LA values and landscape characteristics. The results can support the integration of cultural services in landscape planning and land use policy.

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Section: Mapping Landscape Aesthetics Capacitymentioning

confidence: 99%

Examination of Urban Landscape Aesthetics through Mapping Using Geotagged Social Media Data from Flickr

Zhao¹,

Han²

2021

CUS

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“…However, they did not consider the design form of landscape elements (the shape and order of elements together) in their research work. In [11] , researchers designed a Bayesian network to detect landscape elements influencing aesthetic quality perception. They discussed that the Bayesian network is a reliable model for landscape aesthetic quality assessment.…”

Section: Methods Detailsmentioning

confidence: 99%

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

Aboufazeli

Jahani²,

Farahpour

2021

MethodsX

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This work presents a simplified method for the application of the Multi-Layer Perceptron (MLP) model that aims to predict the aesthetic quality of the landscape designed by water and plants in different forms and volume. The MLP was prepared by (Rosenblat) in the field of computer science, followed by the application of a MLP in landscape aesthetic quality prediction proposed by (Jahani). In the method of this research, the structure of MLP was structured for aesthetic quality prediction of plants and water in urban park landscapes. The accuracies of designed MLP structures were tested to achieve the most accurate one in aesthetic quality prediction. This method creates an environmental decision support system tool for landscape designers, and it is a platform to predict the quality of environment. In practice, the designed environmental decision support system tool is applied by landscape managers to predict the aesthetic quality of landscape in designing new urban parks. Applies Multi-Layer Perceptron method in landscape assessment. Accurate MATLAB extension for landscape aesthetic evaluation. Defined criteria for aesthetic value of landscape.

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“…For these reasons, BBNs are increasingly used as decision support tools (DSTs) to address issues in environmental management, including modelling preferences of landscape aesthetics [86], assessing trade-offs and synergies of multiple ESS according to land-use on regional scale [87], predicting crop choice of farmers based on their attitudes toward ESS [88], and quantifying the impacts of planting riparian buffer strips on flood risk and water quality [89]. Pérez-Miñana et al [90] analyzed greenhouse gas emission potentials of farming practices using a BBN approach.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes

Shaaban

Schwartz

Macpherson

et al. 2021

Land

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Appreciation for agricultural sustainability and ecosystem services (ESS) has received considerable attention from the scientific community. However, research has not yet systematically and sufficiently considered the spatial dimension of ESS trade-offs as a source of conflicts. Moreover, approaches for ESS management that address a wide range of beneficiaries and their interactions at landscape scale are lacking. Our main research question is how to motivate different beneficiaries of agricultural landscapes to cooperate in reducing supply–demand mismatches and accompanied conflicts, as well as to assess how different scenarios would impact relevant Sustainable Development Goals (SDGs). We present a novel and conceptual integrated model in which we employ a combination of three methodological tools: participatory geographic information system (PGIS), agent-based modelling (ABM) and a Bayesian belief network (BBN). The objective of our model simulation is to identify and manage site-specific spatial trade-off patterns and to provide decision support for shifting competitive behavior of individual stakeholders in satisfying their demand for ESS to a collective and cooperative scheme, while jointly striving to attain relevant targets outlined in the SDGs. Attached to this work is a short video depicting our conceptual model. We strongly suggest that tackling a complex social-ecological system necessitates a highly integrated modelling approach that fosters the transition from farm- to landscape-scale management, from individualistic to collective action, and from competitive to cooperative behavior.

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Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting

Cited by 51 publications

References 70 publications

Examination of Urban Landscape Aesthetics through Mapping Using Geotagged Social Media Data from Flickr

Examination of Urban Landscape Aesthetics through Mapping Using Geotagged Social Media Data from Flickr

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes

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