Mapping the most characteristic vistas using observations, current and historical geo-data

Organised by Laboratory of Geo-information Science and Remote Sensing

Fri 25 August 2017 14:00 to 14:30

Venue Gaia, gebouwnummer 101
Room 1

By Lobke Lambertz (The Netherlands)

In the context of increasing pressure on the open areas in the Netherlands and the influence of these pressures on the visibility and characteristics of an area, a research has been carried out. The purpose of this research is to develop a method for mapping the most characteristic vistas. The case study location is the Wageningse Eng.

A vista is defined as a focused view on a fixed scene that can been seen from a single (or multiple) point of view. A vista has a horizontal (22 degrees) and vertical delineation (1.6 meter). A characteristic vista is a vista that focuses on a characteristic target. In this study, the target should be visible in both the years 1900 and in 2012. The characteristic vistas were mapped for different observation points. The most characteristic vistas will be identified by the public. This will be done by asking people who know the case study area well to assess the different characteristic vistas.

There are different elements that need to be taken into account that can cause vistas to change. Firstly, the perceptions of a vista differ based on the observer. Secondly, the surroundings have different obstructing elements that can obstruct a vista. An example of a temporarily obstructing element are crops like cereals and maize. These elements can have a huge influence on vistas. Therefor a distinction is made between the summer and winter seasons in 2012. Finally, there are some data considerations that need to be taken into account that can influence the calculated vistas based on the geo-data handling approach.

For detecting the vistas, a methodology has been designed. The vistas are calculated with the help of different GIS methods. The most important methods are ‘Construct Sight Lines’ and ‘Line of Sight’. Using these methods, the sightlines of the vistas and the visibility along the lines can be calculated.

The calculated vistas were not the intended result, as not enough vistas have been identified. A solution has been found. The vistas that have the longest sightlines or have the most sightlines were selected (the highest 25 % of the data). The methodology for detecting vistas has been extended for this purpose. For the observation points set as ‘roads’, start the highest 25% for at least 3 sightlines and the vistas have a length of at least 1,100 meter. For the observation points set ‘TAWE’ start the highest 25% by at least 5 sightlines and a length of 1,000 meter.

The differences and similarities of the vistas between the years 1900 and 2012, and between the winter of 2012 and summer of 2012 were studied. Depending on the type of observation points set as ‘roads’, 22 vistas in the winter of 2012 and 16 vistas in the summer of 2012 are similar to the vistas in 1900. For observation points set as ‘TAWE’, 17 vistas in the winter of 2012 and the 17 in the summer of 2012 have similarities with the vistas in 1900. From these vistas, the 15 best vistas were selected for the questionnaire in the validation chapter.

An online questionnaire was conducted as validation of the calculated vistas. This questionnaire was filled in by the inhabitants of the case study area. The people were asked to select the most characteristic vistas.

Finally, the 9 characteristic vistas are selected as the most characteristic vistas. These most characteristic vistas are mapped. All together this study successfully developed a method for mapping the most characteristic vistas. This study offers a new way of calculating vistas using lines from observation points to characteristic targets that include land use data of different years as obstructing layers.
However, to facilitate future research where the most characteristic vistas are mapped, further experiments are required to test the reliability of the method. The most important recommendations are to change the definition of ‘vista’ to get better results.

Another recommendation is to make use of a land use map that is as old as possible, in order to get a better picture of the historical obstructing elements and the placement of the target features. Finally, in this research the target visibility of the vistas is used instead of the visibility. The visibility of a vista could give a better result than the used target visibility.

Keywords: Characteristic vistas; geo-data; landscape openness; visibility; observations; historical