A new approach to the analysis of adjacencies: Potentials for landscape insights

• Published in: Ecological modelling Vol. 221; no. 16; pp. 1889 - 1896
• Main Authors: Ferrarini, Alessandro, Tomaselli, Marcello
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 10.08.2010; Elsevier
• Subjects: Algorithms; Animal, plant and microbial ecology; Association rules; Biological and medical sciences; Data mining; Ecology; Environmental studies; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Human; Landcover maps; Landscape dynamics; Methods and techniques (sampling, tagging, trapping, modelling...); Naturality–artificiality; Networks; Spatial contiguities; Valleys; Vegetation; Spatial contiguities; Landscape dynamics; Landcover maps; Naturality–artificiality; Association rules; Maps; Landscape; Naturality-artificiality; Dynamics; Plant cover; Models
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2010.04.020

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas. In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale. We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities. Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.