Constructing a Forest Color Palette and the Effects of the Color Patch Index on Human Eye Recognition Accuracy

As the first visual element, color is most attractive in forest landscape. There are various kinds of colors; however, human eye’s ability to recognize them limited. In order combine composition and eye recognition quantify colors more appropriately improve ornamental effect landscapes precisely, we have constructed a palette using k-means clustering based on information 986 images from 40 national parks China. The differences accuracy sensitivity among populations were analyzed. patch indices identification for interior distant was also explored. results as follows: (1) could be divided into eight families—orange, yellow, yellow-green, green, blue-green, blue, purple, red. (2) For humans, highest green lowest blue-green. (3) landscapes, mean area proportion fractal dimension patches showed significant positive effects accuracy, whereas number density negative effects. Shannon’s diversity index number, edge density, division index, cohesion We thus suggest that it necessary increase complexity shape when creating focus balance matching landscapes. future studies, collection pathways should expanded, extraction algorithms incorporate perception selected. This will data available studies enable construction accurate palette.