Forest landscape visual quality evaluation using artificial intelligence techniques as a decision support system

Abstract

Forest management should be directed towards multifunctional management and utilization of forest services (other than wood production) in order to achieve maximum utilization and minimum degradation. Artificial intelligence enables forest managers to plan for utilization of forest landscape aesthetic values. Visual quality evaluation is a stochastic problem in natural forest landscapes and it is influenced by forest characteristics. We aimed to landscape visual quality evaluation by expert/human-perception-based approach and application of artificial intelligence modeling techniques for the visual quality prediction of forest landscapes. Therefore, we recorded five landscape attributes in 100 forest landscapes. We developed the stochastic model to evaluate visual quality potential by artificial intelligence techniques. Comparing to multi-layer regression (R2 = 0.588) and multi-layer perceptron (R2 = 0.847), the radial basis function (RBF) (R2 = 0.887) model represents the highest value of R2 in the test data set. The water, shrubs, roads, rocky hills, and trees, in forest landscapes were introduced respectively as the most important attributes which influence the RBF model. The designed graphical user interface tool, as an environmental decision support system, evaluates landscape visual quality of forests, and it helps to solve stochastic problems such as visual quality value.