Landscape aesthetics and environmental quality have both become central investigatory areas in transportation planning and design. Environmental designers are interested in applying research-based models to study the effects of specific transportation design treatments on the built and natural landscape. The development of a perception-based visual quality predictive equation is investigated for application in both naturalistic and designed landscapes for transportation planning and design projects. The prediction model contained total area of noospheric features and total area of motorized vehicles; presence of humans, wildlife, utility structures, and foreground flowers; total area of distant nonvegetation landscape features such as mountains and buttes; perimeter of intermediate nonvegetation; total area of foreground vegetation; and openness, mystery, and environmental quality indexes (p < 0.0001 for the overall regression, p ≤ 0.05 for each regressor using Type II sums of squares, and multiple. R-square of 0.67). Other variables such as motorized boats, nonflowering foreground herbaceous plants, a greenness index, fire, total area of exposed foreground substrate, smoke, water, smoothness, and the Shafer index were not significant regressors. By using a graph plotting the 95 percent confidence limits for the equation, an investigator can predict the statistical differences between a pair of images at a 90 percent confidence level.
A dataset of LC-MS QTOF analysis of potato and mustard crop residue smoke water
