3D city models are now common planning and analysis tools. Urban vegetation as a feature in these models, however, is neglected overshadowed by the focus on buildings, so its inclusion in 3D city models is often symbolic. On the other hand, urban vegetation improves the comfort and social well being of a city’s inhabitants and is a resource for sustainable urban growth and an environmentally friendly resource for mitigating the negative effects of climate change, e.g., frequent heat waves, floods from storm downpours and extended dry periods. Trees also mitigate Urban Heat Island (UHI) effects. Urban vegetation’s ecosystem services (ecoservices) as mitigation functions of pollution and negative effects of climate change have propelled research, studies, applications, and simulations that need data and 3D models of existing vegetation e.g., for spatial simulations, to assess the canopy cooling impact to surroundings or to identify areas prone to UHI. In view of these needs, urban vegetation in 3D city models is underrepresented. Guidelines for modelling vegetation are already provided in CityGML, the 3D city modelling standard, but they are insufficient for today’s needs because it has vagueness and focuses mostly on built infrastructures.
In this research, 14 Single Vegetation Object (SVO) Levels of Details (LOD) and four root LODs are proposed. They target to meet different adherence requirements and scales. Their formulation is based on LOD specification approaches, and on a needs analysis that identified the vegetation models and data most commonly required in applications in the urban environment. Vegetation LOD description approaches include semantic 3D modelling standards, industry, and common practices of municipality users which are also GIS data providers.
Current vegetation LOD descriptions approaches fall into two different groups based on the geometry they adopted for their specifications: implicit or explicit, and no one approach fulfills identified needs. Acquisition techniques and demand in IT resources have influenced the definitions of vegetation LODs, the adoption of one geometry type or the other, and the wide use (or not) of certain LODs.
Refined SVO LODs specifications of this research combine the strengths of each group with descriptions that cover beyond geometric specifications. Refined LODs incorporate implicit components, underground representations and reconstruction LODs not defined by any approach. With them, most datasets can be represented by at least one LOD, and modelers can tell what LOD is possible to implement based on the data they already have. For acquisition, it is possible to tell what data is required for a particular LOD, and which LOD can be used to obtain data needed for an application. The broad spectrum of refined LOD allows them to meet different requirements.
A shadow analysis case study was done with implementations of refined SVO LOD specifications. Acquisition from aerial and mobile LiDAR data was done in a workflow that brought the 0D tree inventory of the municipality of Rotterdam to 3D models using mainstream and open source tools. The case study confirmed a quantitative impact in shadow duration and extent by each LOD indicating that each is independently differentiated. Volumetric and non-volumetric models had different shadow over and underestimation impacts. The study further highlighted the properties of the crown of the real-world object that help in choosing a LOD and gave insights offered by lower LOD models.
The implementation of the assorted LODs revealed that while much research has been done in acquiring vegetation parameters from LiDAR data, the many options, methods and algorithms are scattered necessitating a unifying process or tool.