Analysis of Building Height Impact on Land Surface Temperature by Digital Building Height Model Obtained from AW3D30 and SRTM
Dibyanti Danniswari (),
Tsuyoshi Honjo and
Katsunori Furuya
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Dibyanti Danniswari: Graduate School of Horticulture, Chiba University, 648, Matsudo, Matsudo-shi 271-8510, Chiba, Japan
Tsuyoshi Honjo: Graduate School of Horticulture, Chiba University, 648, Matsudo, Matsudo-shi 271-8510, Chiba, Japan
Katsunori Furuya: Graduate School of Horticulture, Chiba University, 648, Matsudo, Matsudo-shi 271-8510, Chiba, Japan
Geographies, 2022, vol. 2, issue 4, 1-14
Abstract:
Land surface temperature (LST) is heavily influenced by urban morphology. Building height is an important parameter of urban morphology that affects LST. Existing studies show contradicting results where building height can have a positive or negative relationship with LST. More studies are necessary to examine the impact of building height. However, high accuracy building height data are difficult to obtain on a global scale and are not available in many places in the world. Using the Digital Building Height Model (DBHM) calculated by subtracting the SRTM from AW3D30, this study analyzes the relationship between building height and Landsat LST in two cities: Tokyo and Jakarta. The relationship is observed during both cities’ warm seasons (April to October) and Tokyo’s cool seasons (November to February). The results show that building height and LST are negatively correlated. In the morning, areas with high-rise buildings tend to have lower LST than areas with low-rise buildings. This phenomenon is revealed to be stronger during the warm season. The LST difference between low-rise and mixed-height building areas is more significant than between mixed-height and high-rise building areas.
Keywords: building height; digital building height model (DBHM); land surface temperature (LST); urban morphology; urban heat island (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jgeogr:v:2:y:2022:i:4:p:34-576:d:921789
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