Relationship between land surface temperature and land cover types based on GWR model: A case of Beijing-Tianjin-Tangshan urban agglomeration, China.

• Published in: Ying yong sheng tai xue bao Vol. 27; no. 7; p. 2128
• Main Authors: Wang, Jia, Qian, Yu Guo, Han, Li Jian, Zhou, Wei Qi
• Format: Journal Article
• Language: Chinese
• Published: China 01.07.2016
• Subjects: Agriculture; Beijing; China; Environmental Monitoring; Forests; Satellite Imagery; Spatial Regression; Temperature; Urbanization; Wetlands; geographically weighted regression (GWR); Beijing-Tianjin-Tangshan urban agglomeration; land surface temperature (LST); land cover; spatial non-stationarity
• ISSN: 1001-9332
• DOI: 10.13287/j.1001-9332.201607.008

We used land cover data derived from Landsat thematic mapper (TM) and land surface temperature (LST) data from moderate-resolution imaging spectro-radiometer (MODIS) satellite images to study the variations in LST in July of different land cover types in Beijing-Tianjin-Tangshan urban agglomeration. Ordinary linear regressions (OLS) models and geographically weighted regressions (GWR) models were used to investigate the relationships between the proportions of land cover types and LST. The results showed that great variations in LST occurred among different land cover types. The average LST ranged from high to low in the order of developed land (40.92±3.49 ℃), cultivated land (39.74±3.74 ℃), wetland (35.42±4.33 ℃), and forested land (34.43±4.16 ℃). The proportions of land cover types were significantly related to LST, but with spatial non-stationarity. This might be due to inherent difference in land cover across locations, and the surrounding environments. GWR models had higher R values, compared to OLS, ind