ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Oct 2022)

EVALUATING THE PERFORMANCE OF HIGH LEVEL-OF-DETAIL TREE MODELS IN MICROCLIMATE SIMULATION

  • H. Xu,
  • C. C. Wang,
  • X. Shen,
  • S. Zlatanova

DOI
https://doi.org/10.5194/isprs-annals-X-4-W3-2022-277-2022
Journal volume & issue
Vol. X-4-W3-2022
pp. 277 – 284

Abstract

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Present urbanization influences urban morphology by the increasing number of dense buildings and infrastructure, which effects climate change. Microclimate simulations including urban vegetation help in mitigating climate change. Most of existing microclimate simulations simplify trees and thereby may introduce some erroneous estimates. Tree models of high levels of detail (LOD) can provide a more accurate measure. Technology advances make it possible to reconstruct detailed tree models, which can be further used in microclimate simulations. However, the few studies presenting detailed tree models focus predominantly on the reconstruction process and omit the microclimate simulations. The objective of this study is to investigate high LOD tree models in microclimate simulation to estimate the potential gain in accuracy. This study focuses on voxel-based tree models and microclimate simulations using computer fluid dynamics software. A series of microclimate simulations are completed in two scenarios, which are single tree and a set of trees with buildings. Based on the simulation results, the advantages of detailed voxel tree models are demonstrated. Final discussion elaborates on the needed and preferred levels of detail for tree models.