rarygit
commented
2 years ago For example Zhou etal. 2017 outlined some straightforward landscape design criteria for their siting of residential shade trees:
"Because infinite potential tree locations exist, the simplification of potential tree siting location set is necessary. Potential tree placement on the residential parcel is summarized based on landscape design guidelines. In the northern hemisphere, landscape design guidelines suggest that trees should be planted on the south, west, or east of structures. Because of the space limitation on the west and east side of the house, we limited tree placement to the south of the building. To avoid tree crown overlap, the potential tree siting locations within the existing tree crown is excluded. Further, to avoid unnecessary tree shade coverage on the rooftops, a minimum distance of 3 m between the tree and the building is predefined. We locate two trees because this is the most common number of trees to be planted in the desert city considering the water usage and landscape regulation, but in general, the spatial optimization method can be used to locate any number of trees in the 3D environment."
A suggestion made by Nils during the recent online seminar was to run several tree planter scenarios, each run having a specific tree and canopy dimension. This occurs because Tree Planter cannot automatically change these in the simulation.
What would be an optimal approach to optimisation in circumstances where
My question relates to the reasoning one should use to efficiently & effectively combine the scenarios described by Nils into a small number of "most probable solution sets" to run in SOLWEIG. Then to compare the magnitude of heat mitigation for each solution set. I am interested in the reasoning towards "efficient & effective", or "optimal approach" to conducting the tree planter optimisation; i.e something to support the choices made.
Has anyone come across literature that describes an optimal approach to designing such scenarios? I would like to avoid a plug and chug approach, manually spinning scenarios and then guessing the best match (aka. trial and error).
Intuitively I could begin by rezoning the planting area into a gradient of Tmrt max values, and then subdivide the scenarios hierarchically, e.g. highest Tmrt max values --> plant larger trees required. Exclusion criteria would be inherent in the planting polygon dimensions, e.g. building distance < largest canopy width of trees.