Paper
20 August 2009 Analyzing the relationship between urban heat island and land use/cover changes in Beijing using remote sensing images
Xiaoyan Zhao, Shenbin Yang, Shuanghe Shen, Yulong Hai, Yongxia Fang
Author Affiliations +
Abstract
In this study, three scenes of Landsat TM/ETM+ images covering Beijing area were used to examine the relationship between the UHI and land use and land cover (LULC) changes, as well as between the UHI and vegetation greenness. The brightness temperatures, LULC, and NDVI were retrieved from the calibrated images. The results showed that the urban or built-up area in Beijing has increased by 4.07% from 1988 to 2005, with nearly 5.7% of vegetated land lost during the same period. The barren area was also increased in this period as large number of land was taken over for urban construction. Seasonal pattern of UHI was obvious with highest UHI intensity observed in summer and lowest in winter. Moreover, with the rapid urbanization, the extent of UHI expanded with newly hot spots emerged surrounding the central urban area. In addition, higher NDVI or vegetation coverage leads to higher land surface temperature (LST) in winter and lower LST in summer. This was due to the different thermal characteristic between vegetated area and non-vegetated area. Therefore, increasing vegetation coverage can be beneficial to the mitigation of UHI effect in urban area in hot season while to keep the land warmer in cold season.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoyan Zhao, Shenbin Yang, Shuanghe Shen, Yulong Hai, and Yongxia Fang "Analyzing the relationship between urban heat island and land use/cover changes in Beijing using remote sensing images", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 74541J (20 August 2009); https://doi.org/10.1117/12.825492
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Cited by 4 scholarly publications.
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KEYWORDS
Vegetation

Agriculture

Earth observing sensors

Landsat

Climatology

Remote sensing

Satellites

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