Prediction of the visual impact of motorways using GIS

Large scale transportation projects can adversely affect the visual perception of environmental quality and re- quire adequate visual impact assessment. In this study, we investigated the effects of the characteristics of the road project and the character of the existing landscape on the perceived visual impact ofmotorways, and devel- oped a GIS-based prediction model based on the findings. An online survey using computer-visualised scenes of different motorway and landscape scenarios was carried out to obtain perception-based judgements on the vi- sual impact.Motorway scenarios simulated included the baseline scenario without road, originalmotorway,mo- torways with timber noise barriers, transparent noise barriers and tree screen; different landscape scenarios were created by changing land cover of buildings and trees in three distance zones. The landscape content of each scenewasmeasured in GIS. The result shows that presence of amotorway especially with the timber barrier significantly decreases the visual quality of the view. The resulted visual impact tends to be lowerwhere it is less visually pleasant with more buildings in the view, and can be slightly reduced by the visual absorption effect of the scattered trees between themotorway and the viewpoint. Based on the survey result, eleven predictorswere identified for the visual impact prediction modelwhichwas applied in GIS to generate maps of visual impact of motorways in different scenarios. The proposed predictionmodel can be used to achieve efficient and reliable as- sessment of visual impact of motorways.

Publikationsart
Zeitschriftenbeiträge (peer-reviewed)
Titel
Prediction of the visual impact of motorways using GIS
Medien
Environmental Impact Assessment Review
Band
55
ISBN
ISSN: 0195-9255
Autoren
Like Jian, Jian Kang, Olaf Gerhard Schroth
Seiten
59-73
Veröffentlichungsdatum
01.11.2015
Zitation
Jian, Like; Kang, Jian; Schroth, Olaf Gerhard (2015): Prediction of the visual impact of motorways using GIS. Environmental Impact Assessment Review 55, 59-73. DOI: 10.1016/j.eiar.2015.07.001