• Journal of the Korean earth science society
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• v.31 no.7
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• pp.738-748
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• 2010

In order to clarify the impact of variation of albedo on the atmospheric boundary layer caused by the density of building in urban areas, both satellite data analysis and numerical experiments were carried out. Utilized satellite data were multi-spectral visible data detected by the Korea Multi- Purpose Satellite -2 (KOMSAT-2), and the numerical models for the estimation of surface heat budget are Albedo Calculation Model (ACM) and Oregon State University Planetary Boundary Layer model (OSUPBL). In satellite data analysis, the estimated albedo in densely populated building area is lower than other regions by 17% at the maximum due to the shadow effect of skyscraper buildings. The surface temperature reached $43.5^{\circ}C$ in the highly dense and tall building area and $37.4^{\circ}C$ in the coarse density area of low buildings, respectively. However, the low albedo in densely integrated building area is not directly related to the increase of surface air temperature since the mechanical turbulence induced by the roughness of buildings is more critical in its impact than the decrease of albedo.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.