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Estimation of Longwave Radiation Intensity Emitted from Urban Obstacles in Each Direction Using Drone-Based Photogrammetry.

Authors :
Ishida, Yasuyuki
Fujiyama, Mamiko
Kobayashi, Hikaru
Source :
Remote Sensing. Nov2024, Vol. 16 Issue 21, p4017. 16p.
Publication Year :
2024

Abstract

Longwave radiation is a crucial factor affecting human thermal comfort and thermal stress, especially in outdoor spaces in summer, owing to the vast effect of longwave radiation emitted from high-heated asphalt roads, building walls, and automobiles. Although controlling the longwave radiation environment to improve thermal comfort in summer is crucial, the prediction of the longwave radiation environment is frequently conducted only at the assessment stage of the final proposal because of the high computational cost of radiation calculations and unsteady heat balance analysis considering multiple reflections. This is a significant constraint for the design of urban and architectural environments. A previous study proposed a method to rapidly estimate the longwave radiation environment based on a point-by-point method with longwave radiation intensity distributions of the heat sources. To use this method, 3D models of the geographical objects in urban areas, such as buildings and trees, must be accurately generated, and these models should have information on the longwave radiation emitted in each direction from each object. However, no specific examples of a 3D model and longwave radiation intensity distribution have been presented. In this study, a 3D modeling method for geographical objects in urban areas with longwave radiation information based on drones and photogrammetric techniques was utilized. Moreover, a 3D model of a small-scale building was generated. A longwave radiation intensity distribution was produced for the building. Based on the distribution data, the directional characteristics of longwave radiation were discussed, and the availability of the proposed method was assessed. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
21
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
180782527
Full Text :
https://doi.org/10.3390/rs16214017