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MAO Bin, PENG Li-qun, LI Le, XU Cheng-yang. Non-linear scenic beauty model of scenic Platycladus orientalis plantations based on in-forest color patches[J]. Journal of Beijing Forestry University, 2015, 37(7): 68-75. DOI: 10.13332/j.1000-1522.20140481
Citation: MAO Bin, PENG Li-qun, LI Le, XU Cheng-yang. Non-linear scenic beauty model of scenic Platycladus orientalis plantations based on in-forest color patches[J]. Journal of Beijing Forestry University, 2015, 37(7): 68-75. DOI: 10.13332/j.1000-1522.20140481
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Non-linear scenic beauty model of scenic Platycladus orientalis plantations based on in-forest color patches

  • Key Laboratory for Forest Silviculture and Conservation of Ministry of Education,Key Laboratory for Silviculture and Forest Ecosystem in Aird and Semi-arid Areas of State Forestry Administration, Beijing Forestry University, Beijing, 100083, P. R. China

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  • Received Date: December 28, 2014
  • Revised Date: December 28, 2014
  • Published Date: July 30, 2015
    Graphical Abstract
    • Abstract
  • We applied the theory of landscape spatial pattern analysis in landscape ecology to evaluate the quality of landscape by analyzing the structure of color patches in photos. The relationships between SBE grades and 14 patch indexes were studied.Six indexes among them, i.e.,SPLIT, SHDI, DIV, SIEI, ACOV and LPI,which influence scenic degree significantly, were selected by using correlation analysis. Results showed that the relationships between DIV, SIEI, ACOV, LPI and SBE grades were in multiple ways rather than merely in a linear way; SPLIT and SHDI changed in a positively linear trend with the decrease of SBE grade.Based on the six indexes, the scenic Platycladus orientalis plantations could be classified into four types by using cluster analysis. The indexes were used as the dependent variables to establish linear and non-linear models. Comparison showed that fitting accuracy of the non-linear model was higher than that of the linear model. The relationships between SBE value and landscape index could be well reflected by quadratic polynomial regression models.
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    • References (35)
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