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Landscape aesthetic quality assessment of forest lands: an application of machine learning approach

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Abstract

Forests, with natural factors, provide visual aesthetic features, as a social ecosystem service for human beings. Nowadays, forest managers are looking for decision support system tools which evaluate the aesthetic quality of forest landscapes, particularly in the development of human ecosystem services. In our methodology, the aesthetic quality of environment is assessed with a human-perception-based approach to apply three machine learning techniques (support vector machine (SVM), radial basis function neural network (RBFNN) and multilayer perceptron (MLP)) for the aesthetic quality simulation of forest areas. To perform this method, the landscape attributes (13 features) were defined in 72 Hyrcanian broad leaves forest landscapes. The landscapes aesthetic quality model was designed to determine the visual qualities by machine learning techniques. Considering the results, MLP model was detected as the most practical, reliable and accurate model for evaluation of landscape quality in broad leaves forest areas. Comparing to RBFNN (R2 = 0.809), and SVM (R2 = 0.829), MLP (R2 = 0.878) model represents the most reliable results of R2 in the test data set. The number of species, tree density, Alnus subcordata, canopy density, altitude and Carpinus betulus in forest areas were detected as the main influential factors of the MLP model. On the other hand, the designed graphical user interface tool finds the most scenic landscapes for peoples who are looking for relaxation and recreation services in the nature. The forested lands planted according to the ecological techniques could be designed with resulted aesthetics criteria.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

MLP:

Multilayer perceptron

RBFNN:

Radial basis function neural network

SVM:

Support vector machine

ANN:

Artificial neural network

EDSS:

Environmental decision support system

GUI:

Graphical user interface

MSE:

Mean squared error

RMSE:

Root mean squared error

MAE:

Mean absolute error

LM:

Levenberg–marquardt

GIS:

Geographic information system

DBH:

Diameter at the breast height

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Acknowledgements

We thank the manager of Khyrud Forest who support us in field survey.

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. Faculty of Assessment and Environment Risks Department, Research Center of Environment and Sustainable Development (RCESD), Pardisan Park, Tehran, Iran

    Ali Jahani

  2. College of Natural Resources, University of Tehran, Tehran, Iran

    Maryam Saffariha

  3. Environment Department, Azad University Central Tehran Branch, Tehran, Iran

    Pegah Barzegar

Authors
  1. Ali Jahani
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  2. Maryam Saffariha
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  3. Pegah Barzegar
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Correspondence to Ali Jahani.

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Jahani, A., Saffariha, M. & Barzegar, P. Landscape aesthetic quality assessment of forest lands: an application of machine learning approach. Soft Comput 27, 6671–6686 (2023). https://doi.org/10.1007/s00500-022-07642-3

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