Exploring 3D visualisation of vegetation

authors Hempenius, J. source Faculty of Geosciences Theses (2010) full text [Full text] document type Master thesis disciplines Geowetenschappen abstract 3D visualisation of vegetation change can communicate for instance the loss of rare plant species, vegetation stress or vegetation risks in order to raise awareness. 3D visualisation might also be helpful in the nature management process, by visualising the effects of certain decisions, such as removing biomass from a vegetation area. In order to make a 3D visualisation of a vegetation model, it is necessary that this model has a map as output together with more information which describes the composition of this vegetation. These will serve as the input for distribution modelling of the individual plant species. 3D models of the plants can be placed at the point distribution which is created by this distribution tool. To get these 3D models of the plants, the plants which contribute to the grain, colour and structure of the vegetation type will have to be selected and 3D modelled. This point distribution of the plants and the 3D models can be brought together in a 3D simulation, together with a DEM and an aerial photo to model height and to give the substrate a natural colour There are several models for vegetation modelling, plant distribution modelling and 3D plant modelling. The vegetation modelling is done using vegetation mapping models or vegetation succession models. The mapping can be done using geostatistical interpolation, generalized linear networks, artificial neural networks and classification trees. These models result in a map, and in order to be used as input for the plant distribution tool, a description of the abundance of each plant species within the vegetation types is necessary. However, when the species abundance is described using the Braun-Blanquet scales as a cover percentage, it is not possible to use it as input for a computer generated distribution, because this requires the plants per square meter as an input. A conversion is possible, but requires accurate modelled 3D plant models. The vegetation succession modelling models the transition from the one vegetation type to another vegetation type. This change is driven by changing circumstances for the plants and the output as biomass is calculated for five different layers in the vegetation. In order to model this layered output correctly, it is necessary that the 3D plant models give a correct representation of the biomass of the plants and it therefore requires accurate 3D growth models of the plant species. There methodologies for creating a plant distribution vary a lot in complexity: the complex Agent Based Models and Cellular Automata can model competition for resources and dispersion, but also require a lot of research, calibration and validation. A simple computer generated distribution on the other side does only require the number of plants per square meter per vegetation type as input. The 3D plant models can be divided into two types: the accurate models and the sketch based models. Accurate modelling techniques are AMAP and L-systems and it requires scientific measurements of the plants growth, size and shape to model a plant in 3D and the modelling needs to be calibrated and validated. These accurate 3D models are necessary to model vegetation succession or to make a conversion from a cover percentage to the number of plants per square metre. The accurate modelling techniques can also be used sketch based, but for instance Xfrog works faster if a plant has to be 3D modelled. Photographs from different angles of the plant can serve as the input for the sketch based modelling process. This research has shown that 3D visualising a vegetation map with a computer generated distribution and sketch based 3D plant models is possible. However, vegetation maps with the plant species abundance described as cover percentage are unsuitable for 3D visualisation, unless an easy method is developed to convert this percentage to the number of plants per square meter. keywords 3D, GIS, vegetation, visualisation, distribution modelling