滨海地区地表温度空间异质性影响因素探究

摘要/Abstract

摘要： 随着城市化进程不断加快, 地表温度升高引起的城市病日益严重。为深入认识滨海城市地表温度的影响因素, 进而为改善人居环境和生态健康提供科学数据支撑, 采用单窗算法反演了大连市内甘井子区、西岗区、沙河口区和中山区四区地表温度, 并综合运用多尺度地理加权回归模型 (Multiscale-GWR) 结合归一化建筑指数 (NDBI)、归一化植被指数 (NDVI)、改进的归一化水体指数 (MNDWI) 和归一化裸土指数 (NDBAI), 探究了地表温度与下垫面指数空间异质性关系。研究结果表明: (1) 大连市内四区的地表温度呈现由东向西递减的分布态势, 中山区、沙河口区和西岗区的北部地表温度较南部高, 甘井子区西南部地表温度较其他区域低。 (2) 大连市内四区地表温度与下垫面指数关系基本上不存在全局效应, 空间异质性很强, Multiscale-GWR 模型可以较好地拟合下垫面指数与地表温度相关关系。 (3) 从相关系数来看, 下垫面指数对地表温度的影响作用力表现为: NDBAI > NDVI > MNDWI > NDBI, NDBAI、 NDVI 和 MNDWI 指数总体上呈现负相关效应, NDBI 总体上呈现正相关效应。

关键词: 多尺度地理加权回归, 单窗算法, 地表温度, 下垫面指数, 大连市内四区

Abstract: With the acceleration of urbanization, urban diseases caused by the rising land surface temperature (LST) are becoming more and more serious. In order to deeply understand the influencing factors of LST in coastal cities and provide scientific data support for improving human health and ecological environment, the single window algorithm is used to inverse the LST of Ganjingzi District, Xigang District, Shahekou District and Zhongshan District in Dalian, China, and multiscale geographical weighted regression (Multiscale-GWR) model combined with normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference bareness index (NDBAI) is used to explore the spatial heterogeneity relationship between LST and the underlying surface index. The results show that: (1) the surface temperature of the four districts in Dalian presents a decreasing distribution trend from east to west, the surface temperature in the north of Zhongshan District, Shahekou District and Xigang District is higher than that in the south, and the surface temperature in the southwest of Ganjingzi District is lower than that in other areas. (2) The relationship between surface temperature and underlying surface index in the four districts of Dalian has no global effect, and the spatial heterogeneity is very strong. The Multiscale-GWR model can better fit the surface temperature correlation in the selected underlying surface exponential domain. (3) In terms of correlation coefficient, the impact of underlying surface index on surface temperature is as follows: NDBAI > NDVI > MNDWI > NDBI, and NDBAI, NDVI and MNDWI indexes show negative correlation effect on the whole, while NDBI shows positive correlation effect on the whole.

Key words: multiscale geographical weighted regression, single window algorithm, land surface temperature; underlying surface index, four districts of Dalian

中图分类号:

P237
X87

宋筱楠, 迟广源, 史月∗, 范强. 滨海地区地表温度空间异质性影响因素探究[J]. 大气与环境光学学报, 2022, 17(3): 317-327.

SONG Xiaonan, CHI Guangyuan, SHI Yue∗, FAN Qiang. Study on influencing factors of spatial heterogeneity of land surface temperature in coastal areas[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(3): 317-327.

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