A Soft Clustering Approach to Detect Socio-Ecological Landscape Boundaries Using Bayesian Networks

Detecting socio-ecological boundaries in traditional rural landscapes is very important for the planning and sustainability of these landscapes. Most of the traditional methods to detect ecological boundaries have two major shortcomings: they are unable to include uncertainty, and they often exclude...

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Bibliographic Details
Published in:Agronomy (Basel) 2021-04, Vol.11 (4), p.740
Main Authors: Ropero, Rosa F., Maldonado, Ana D., Uusitalo, Laura, Salmerón, Antonio, Rumí, Rafael, Aguilera, Pedro A.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian networks
Boundaries
boundary detection
Cartography
Clustering
Ecological effects
Environmental science
Heterogeneity
Landscape
Mediterranean cultural landscape
Methods
Probability distribution
Random variables
Social-ecological systems
socio-ecosystems
Statistical analysis
Uncertainty
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Summary:Detecting socio-ecological boundaries in traditional rural landscapes is very important for the planning and sustainability of these landscapes. Most of the traditional methods to detect ecological boundaries have two major shortcomings: they are unable to include uncertainty, and they often exclude socio-economic information. This paper presents a new approach, based on unsupervised Bayesian network classifiers, to find spatial clusters and their boundaries in socio-ecological systems. As a case study, a Mediterranean cultural landscape was used. As a result, six socio-ecological sectors, following both longitudinal and altitudinal gradients, were identified. In addition, different socio-ecological boundaries were detected using a probability threshold. Thanks to its probabilistic nature, the proposed method allows experts and stakeholders to distinguish between different levels of uncertainty in landscape management. The inherent complexity and heterogeneity of the natural landscape is easily handled by Bayesian networks. Moreover, variables from different sources and characteristics can be simultaneously included. These features confer an advantage over other traditional techniques.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy11040740